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.gitattributes

@@ -121,3 +121,20 @@ tests/test_images/Kate[[:space:]]Winslet.jpg filter=lfs diff=lfs merge=lfs -text
 tests/test_images/Tom[[:space:]]Cruise.jpg filter=lfs diff=lfs merge=lfs -text
 tests/test_images/Tom[[:space:]]Hanks.jpg filter=lfs diff=lfs merge=lfs -text
 tests/test_images/Viola[[:space:]]Davis.jpg filter=lfs diff=lfs merge=lfs -text
+apps/assets/examples/example_face_2.jpg filter=lfs diff=lfs merge=lfs -text
+assets/gradio_app_demo.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/assets/benchmark.png filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/assets/edgeface.png filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det2.caffemodel filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det3.caffemodel filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det4.caffemodel filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/images/example.png filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/images/office2.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/face_alignment/mtcnn_pytorch/images/office4.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Cate[[:space:]]Blanchett.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Daniel[[:space:]]Day-Lewis.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Elon_Musk.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Gal[[:space:]]Gado.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Kate[[:space:]]Winslet.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Tom[[:space:]]Hanks.jpg filter=lfs diff=lfs merge=lfs -text
+src/third_party/edgeface/tests/test_images/Viola[[:space:]]Davis.jpg filter=lfs diff=lfs merge=lfs -text

apps/gradio_app.py

@@ -2,90 +2,118 @@ import gradio as gr
 from PIL import Image
 from gradio_app.inference import run_inference
 from gradio_app.components import (
-    CONTENT_DESCRIPTION, CONTENT_IN, CONTENT_OUT,
-     list_reference_files, list_mapping_files,
-      list_classifier_files, list_edgeface_files
+    CONTENT_DESCRIPTION, CONTENT_OUTTRO,
+    CONTENT_IN_1, CONTENT_IN_2,
+    CONTENT_OUT_1, CONTENT_OUT_2,
+    list_reference_files, list_mapping_files,
+    list_classifier_files, list_edgeface_files
 )
+from glob import glob
+import os
 
-def create_image_input_column():
-    """Create the column for image input and output display."""
-    with gr.Column():
+def create_image_io_row():
+    """Create the row for image input and output display."""
+    with gr.Row(elem_classes=["image-io-row"]):
         image_input = gr.Image(type="pil", label="Upload Image")
         output = gr.HTML(label="Inference Results", elem_classes=["results-container"])
     return image_input, output
 
-def create_model_files_column():
-    """Create the column for model file selection."""
-    with gr.Column():
-        with gr.Group(elem_classes=["section-group"]):
-            gr.Markdown("### Model Files", elem_classes=["section-title"])
-            ref_dict = gr.Dropdown(
-                choices=["Select a file"] + list_reference_files(),
-                label="Reference Dict JSON",
-                value="data/reference_data/reference_image_data.json"
-            )
-            index_map = gr.Dropdown(
-                choices=["Select a file"] + list_mapping_files(),
-                label="Index to Class Mapping JSON",
-                value="ckpts/index_to_class_mapping.json"
-            )
-            classifier_model = gr.Dropdown(
-                choices=["Select a file"] + list_classifier_files(),
-                label="Classifier Model (.pth)",
-                value="ckpts/SlimFace_efficientnet_b3_full_model.pth"
-            )
-            edgeface_model = gr.Dropdown(
-                choices=["Select a file"] + list_edgeface_files(),
-                label="EdgeFace Model (.pt)",
-                value="ckpts/idiap/edgeface_s_gamma_05.pt"
-            )
-    return ref_dict, index_map, classifier_model, edgeface_model
+def create_model_settings_row():
+    """Create the row for model files and settings."""
+    with gr.Row():
+        with gr.Column():
+            with gr.Group(elem_classes=["section-group"]):
+                gr.Markdown("### Model Files", elem_classes=["section-title"])
+                ref_dict = gr.Dropdown(
+                    choices=["Select a file"] + list_reference_files(),
+                    label="Reference Dict JSON",
+                    value="data/reference_data/reference_image_data.json"
+                )
+                index_map = gr.Dropdown(
+                    choices=["Select a file"] + list_mapping_files(),
+                    label="Index to Class Mapping JSON",
+                    value="ckpts/index_to_class_mapping.json"
+                )
+                classifier_model = gr.Dropdown(
+                    choices=["Select a file"] + list_classifier_files(),
+                    label="Classifier Model (.pth)",
+                    value="ckpts/SlimFace_efficientnet_b3_full_model.pth"
+                )
+                edgeface_model = gr.Dropdown(
+                    choices=["Select a file"] + list_edgeface_files(),
+                    label="EdgeFace Model (.pt)",
+                    value="ckpts/idiap/edgeface_s_gamma_05.pt"
+                )
+        with gr.Column():
+            with gr.Group(elem_classes=["section-group"]):
+                gr.Markdown("### Advanced Settings", elem_classes=["section-title"])
+                algorithm = gr.Dropdown(
+                    choices=["yolo", "mtcnn", "retinaface"],
+                    label="Detection Algorithm",
+                    value="yolo"
+                )
+                accelerator = gr.Dropdown(
+                    choices=["auto", "cpu", "cuda", "mps"],
+                    label="Accelerator",
+                    value="auto"
+                )
+                resolution = gr.Slider(
+                    minimum=128,
+                    maximum=512,
+                    step=32,
+                    label="Image Resolution",
+                    value=300
+                )
+                similarity_threshold = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.05,
+                    label="Similarity Threshold",
+                    value=0.3
+                )
+    return ref_dict, index_map, classifier_model, edgeface_model, algorithm, accelerator, resolution, similarity_threshold
 
-def create_settings_column():
-    """Create the column for advanced settings."""
-    with gr.Column():
-        with gr.Group(elem_classes=["section-group"]):
-            gr.Markdown("### Advanced Settings", elem_classes=["section-title"])
-            algorithm = gr.Dropdown(
-                choices=["yolo", "mtcnn", "retinaface"],
-                label="Detection Algorithm",
-                value="yolo"
-            )
-            accelerator = gr.Dropdown(
-                choices=["auto", "cpu", "cuda", "mps"],
-                label="Accelerator",
-                value="auto"
-            )
-            resolution = gr.Slider(
-                minimum=128,
-                maximum=512,
-                step=32,
-                label="Image Resolution",
-                value=300
-            )
-            similarity_threshold = gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                step=0.05,
-                label="Similarity Threshold",
-                value=0.3
-            )
-    return algorithm, accelerator, resolution, similarity_threshold
+# Load local CSS file
+CSS = open("apps/gradio_app/static/styles.css").read()
 
 def create_interface():
     """Create the Gradio interface for SlimFace."""
-    with gr.Blocks(css="gradio_app/static/styles.css", theme=gr.themes.Soft()) as demo:
+    with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
         gr.Markdown("# SlimFace Demonstration")
         gr.Markdown(CONTENT_DESCRIPTION)
-        gr.HTML(CONTENT_IN)
-        
-        with gr.Row():
-            image_input, output = create_image_input_column()
-            ref_dict, index_map, classifier_model, edgeface_model = create_model_files_column()
-        
-        with gr.Row():
-            algorithm, accelerator, resolution, similarity_threshold = create_settings_column()
+        gr.Markdown(CONTENT_IN_1)
+        gr.HTML(CONTENT_IN_2)
+
+        image_input, output = create_image_io_row()
+        ref_dict, index_map, classifier_model, edgeface_model, algorithm, accelerator, resolution, similarity_threshold = create_model_settings_row()
         
+        # Add example image gallery as a table
+        with gr.Group():
+            gr.Markdown("### Example Images")
+            example_images = glob("apps/assets/examples/*.[jp][pn][gf]")
+            if example_images:
+                # Create a list of dictionaries for the table
+                table_data = []
+                for img_path in example_images:
+                    table_data.append({
+                        "Image": img_path,  # Will be rendered as an image
+                        "Action": f"Use {os.path.basename(img_path)}"  # Button text
+                    })
+                
+                # Create a table with images and buttons
+                gr.Dataframe(
+                    value=table_data,
+                    headers=["Image", "Action"],
+                    datatype=["image", "str"],
+                    interactive=False,
+                    elem_classes=["example-table"],
+                    # Add click event for buttons
+                    row_click=lambda row: Image.open(row["Image"]),
+                    outputs=image_input
+                )
+            else:
+                gr.Markdown("No example images found in apps/assets/examples/")
+
         with gr.Row():
             submit_btn = gr.Button("Run Inference", variant="primary", elem_classes=["centered-button"])
         
@@ -104,13 +132,15 @@ def create_interface():
             ],
             outputs=output
         )
-        gr.Markdown(CONTENT_OUT)
+        gr.Markdown(CONTENT_OUTTRO)
+        gr.HTML(CONTENT_OUT_1)
+        gr.Markdown(CONTENT_OUT_2)
     return demo
 
 def main():
     """Launch the Gradio interface."""
     demo = create_interface()
-    demo.launch()
+    demo.launch(share=True)
 
 if __name__ == "__main__":
     main()

apps/gradio_app/components.py

@@ -44,90 +44,51 @@ def list_edgeface_files():
 CONTENT_DESCRIPTION = """
 **SlimFace: Advanced Face Classification with TorchVision Backbones**
 """
-CONTENT_IN = """
-<style>
-    body {
-        font-family: Arial, sans-serif;
-        line-height: 1.6;
-        margin: 0; /* Remove default margin for full-width */
-        padding: 20px; /* Adjust padding for content spacing */
-        color: #333;
-        width: 100%; /* Ensure body takes full width */
-        box-sizing: border-box; /* Include padding in width calculation */
-    }
-    .attribution {
-        background-color: #f9f9f9;
-        padding: 20px;
-        border-radius: 8px;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-    }
-    .quote-container {
-        border-left: 5px solid #007bff;
-        padding-left: 15px;
-        margin-bottom: 15px;
-        font-style: italic;
-    }
-    .attribution p {
-        margin: 10px 0;
-    }
-    .badge {
-        display: inline-block;
-        border-radius: 4px;
-        text-decoration: none;
-        font-size: 14px;
-        transition: background-color 0.3s;
-    }
-    .badge:hover {
-        background-color: #0056b3;
-    }
-    .badge img {
-        vertical-align: middle;
-        margin-right: 5px;
-    }
-    .source {
-        color: #555;
-    }
-</style>
-    <div class="quote-container">
-        <p>
-            This project leverages code from 
-            <a class="badge" href="https://github.com/otroshi/edgeface">
-                <img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
-            </a>
-            by 
-            <a class="badge" href="https://github.com/otroshi">
-                <img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
-            </a>, 
-            with our own bug fixes and enhancements available at 
-            <a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
-                <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
-            </a>.
-        </p>
-    </div>
-    <p class="source">
-        For more information, you can follow below:<br>
-        Source code: 
-        <a class="badge" href="https://github.com/danhtran2mind/SlimFace">
-            <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
-            ,
-        </a>
-        Author: 
-        <a class="badge" href="https://github.com/danhtran2mind">
-            <img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
-            ,
-        </a>
-        PyTorch Docs: 
-        <a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
-            <img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
-        </a>
-    </p>
-"""
-
-CONTENT_OUT = """
-## More Information about SlimFace
 
+CONTENT_IN_1 = """
 SlimFace empowers developers to build high-accuracy face classification models using transfer learning, leveraging TorchVision's powerful pre-trained architectures. 🌟 It provides a flexible, efficient, and scalable solution for facial recognition, delivering top-tier performance for custom applications.
+"""
+CONTENT_IN_2 = """
+<p class="source">
+    For more information, you can follow below:<br>
+    Source code: 
+    <a class="badge" href="https://github.com/danhtran2mind/SlimFace">
+        <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
+    </a>,
+    Author: 
+    <a class="badge" href="https://github.com/danhtran2mind">
+        <img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
+    </a>,
+    PyTorch Docs: 
+    <a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
+        <img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
+    </a>
+</p>
+"""
+
 
+CONTENT_OUTTRO = """
+## More Information about SlimFace
+"""
+CONTENT_OUT_1 = """
+<div class="quote-container">
+    <p>
+        This project leverages code from 
+        <a class="badge" href="https://github.com/otroshi/edgeface">
+            <img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
+        </a>
+        by 
+        <a class="badge" href="https://github.com/otroshi">
+            <img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
+        </a>, 
+        with our own bug fixes and enhancements available at 
+        <a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
+            <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
+        </a>.
+    </p>
+</div>
+"""
+CONTENT_OUT_2 = """
 **Supported Architectures:**  
 - **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸  
 - **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻  

apps/gradio_app/inference.py

@@ -71,7 +71,7 @@ def run_inference(image, reference_dict_path, index_to_class_mapping_path, model
                 # Similarity with Reference Image
                 similarity = result.get('similarity', 'N/A')
                 similarity_str = f'{similarity:.4f}' if isinstance(similarity, (int, float)) else 'N/A'
-                output += f'<div class="result-item"><span class="label">Similarity with<br>Reference Image</span><span class="value">{similarity_str}</span></div>'
+                output += f'<div class="result-item"><span class="label">Similarity with<br> Reference Image</span><span class="value">{similarity_str}</span></div>'
                 
                 # Confirmed Person
                 confirmed = result.get('confirmed', 'N/A')

apps/gradio_app/static/scripts.js

@@ -0,0 +1,3 @@
+// Placeholder for future JavaScript functionality
+// Currently, no JavaScript is required for the Gradio app as interactions are handled by Gradio
+console.log("SlimFace Gradio App JavaScript loaded");

apps/gradio_app/static/styles.css

@@ -0,0 +1,270 @@
+body {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+    background: linear-gradient(145deg, #e2e8f0 0%, #b8c6db 100%);
+    margin: 0;
+    padding: 0;
+    min-height: 100vh;
+    color: #1a202c;
+}
+
+.gradio-container {
+    max-width: 1280px;
+    margin: 0 auto;
+    padding: 2.5rem 1.5rem;
+    box-sizing: border-box;
+}
+
+h1 {
+    color: #1a202c;
+    font-size: 2.75rem;
+    font-weight: 800;
+    text-align: center;
+    margin-bottom: 2.5rem;
+    letter-spacing: -0.025em;
+    background: linear-gradient(to right, #2b6cb0, #4a90e2);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+.section-title {
+    color: #1a202c;
+    font-size: 1.5rem;
+    font-weight: 700;
+    margin-bottom: 1rem;
+    border-bottom: 2px solid #4a90e2;
+    padding-bottom: 0.5rem;
+    letter-spacing: -0.015em;
+}
+
+.section-group {
+    background: rgba(255, 255, 255, 0.95);
+    border-radius: 0.5rem;
+    padding: 1.5rem;
+    border: 1px solid rgba(226, 232, 240, 0.5);
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+}
+
+.results-container {
+    display: flex;
+    flex-direction: column;
+    gap: 1.75rem;
+    padding: 2rem;
+    background: rgba(255, 255, 255, 0.95);
+    border-radius: 1.25rem;
+    box-shadow: 0 10px 20px rgba(0, 0, 0, 0.15), 0 4px 6px rgba(0, 0, 0, 0.1);
+    border: 1px solid rgba(226, 232, 240, 0.5);
+    backdrop-filter: blur(8px);
+}
+
+.result-card {
+    background: linear-gradient(145deg, #f7fafc, #edf2f7);
+    border-radius: 1rem;
+    padding: 2.25rem;
+    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.1);
+    transition: transform 0.3s ease, box-shadow 0.3s ease, background 0.3s ease;
+    position: relative;
+    overflow: hidden;
+}
+
+.result-card:hover {
+    transform: translateY(-5px);
+    box-shadow: 0 10px 24px rgba(0, 0, 0, 0.15);
+    background: linear-gradient(145deg, #ffffff, #e6eefa);
+}
+
+.result-card::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 4px;
+    background: linear-gradient(to right, #4a90e2, #63b3ed);
+    transition: height 0.3s ease;
+}
+
+.result-card:hover::before {
+    height: 8px;
+}
+
+.result-title {
+    color: #1a202c;
+    font-size: 1.875rem;
+    font-weight: 700;
+    margin-bottom: 1.5rem;
+    border-bottom: 3px solid #4a90e2;
+    padding-bottom: 0.75rem;
+    letter-spacing: -0.015em;
+}
+
+.result-item {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin: 1rem 0;
+    font-size: 1.125rem;
+    color: #2d3748;
+    line-height: 1.6;
+}
+
+.label {
+    font-weight: 600;
+    color: #2b6cb0;
+    text-align: left;
+    text-transform: uppercase;
+    font-size: 0.95rem;
+    letter-spacing: 0.05em;
+    flex: 0 0 auto;
+}
+
+.value {
+    color: #1a202c;
+    font-weight: 500;
+    text-align: right;
+    flex: 0 0 auto;
+}
+
+.value.confirmed-true {
+    color: #2f855a;
+    font-weight: 600;
+    background: #c6f6d5;
+    padding: 0.25rem 0.5rem;
+    border-radius: 0.375rem;
+}
+
+.value.confirmed-false {
+    color: #c53030;
+    font-weight: 600;
+    background: #fed7d7;
+    padding: 0.25rem 0.5rem;
+    border-radius: 0.375rem;
+}
+
+.error-message {
+    background: #fef2f2;
+    color: #9b2c2c;
+    padding: 1.75rem;
+    border-radius: 0.875rem;
+    margin: 1.25rem 0;
+    font-size: 1.125rem;
+    font-weight: 500;
+    border: 1px solid #e53e3e;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+}
+
+.centered-button {
+    display: block;
+    margin: 1rem auto;
+    background: #4a90e2;
+    color: white;
+    padding: 0.75rem 1.5rem;
+    border-radius: 0.5rem;
+    border: none;
+    font-size: 1rem;
+    font-weight: 600;
+    cursor: pointer;
+    transition: background 0.3s ease;
+    position: relative;
+    padding-left: 2.5rem;
+    width: 30%;
+}
+
+.centered-button:hover {
+    background: #2b6cb0;
+}
+
+.centered-button::after {
+    content: '🤔';
+    position: absolute;
+    left: 0.75rem;
+    top: 50%;
+    transform: translateY(-50%);
+    font-size: 1.2rem;
+}
+
+@media (max-width: 768px) {
+    .gradio-container {
+        padding: 1.5rem;
+    }
+    
+    h1 {
+        font-size: 2rem;
+    }
+    
+    .results-container {
+        padding: 1.5rem;
+    }
+    
+    .result-card {
+        padding: 1.5rem;
+    }
+    
+    .result-title {
+        font-size: 1.5rem;
+    }
+    
+    .result-item {
+        font-size: 1rem;
+        flex-direction: column;
+        align-items: flex-start;
+        gap: 0.5rem;
+    }
+    
+    .label, .value {
+        text-align: left;
+    }
+    
+    .section-title {
+        font-size: 1.25rem;
+    }
+    
+    .section-group {
+        padding: 1rem;
+    }
+    
+    .centered-button {
+        padding: 0.5rem 1rem;
+        font-size: 0.9rem;
+    }
+}
+
+/*Components for Gradio App*/
+    .quote-container {
+        border-left: 5px solid #007bff;
+        padding-left: 15px;
+        margin-bottom: 15px;
+        font-style: italic;
+    }
+    .attribution p {
+        margin: 10px 0;
+    }
+    .badge {
+        display: inline-block;
+        border-radius: 4px;
+        text-decoration: none;
+        font-size: 14px;
+        transition: background-color 0.3s;
+    }
+    .badge:hover {
+        background-color: #0056b3;
+    }
+    .badge img {
+        vertical-align: middle;
+        margin-right: 5px;
+    }
+    .source {   
+        font-size: 14px;
+    }
+/* Gradio Examples */
+.example-table {
+    width: 100%;
+    max-width: 800px;
+}
+.example-table img {
+    max-width: 100px;
+    height: auto;
+}
+.example-table .cell {
+    vertical-align: middle;
+    text-align: center;
+}

src/third_party/edgeface/.gitignore

@@ -0,0 +1,11 @@
+.venv/
+*.pyc
+__pycache__/
+*.pyo
+*.pyd
+.Python
+*.egg-info/
+dist/
+build/
+*.sqlite3
+checkpoints/yolo11_face_detection

src/third_party/edgeface/.python-version

@@ -0,0 +1 @@
+3.11.11

src/third_party/edgeface/LICENSE

@@ -0,0 +1,30 @@
+BSD 3-Clause License
+
+Copyright (c) 2024, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza,
+Ketan Kotwal, Sébastien Marcel
+Idiap Research Institute, Martigny 1920, Switzerland.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

src/third_party/edgeface/README.md

@@ -0,0 +1,126 @@
+
+
+# EdgeFace: Efficient Face Recognition Model for Edge Devices
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-lfw)](https://paperswithcode.com/sota/lightweight-face-recognition-on-lfw?p=edgeface-efficient-face-recognition-model-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-calfw)](https://paperswithcode.com/sota/lightweight-face-recognition-on-calfw?p=edgeface-efficient-face-recognition-model-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-cplfw)](https://paperswithcode.com/sota/lightweight-face-recognition-on-cplfw?p=edgeface-efficient-face-recognition-model-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-cfp-fp)](https://paperswithcode.com/sota/lightweight-face-recognition-on-cfp-fp?p=edgeface-efficient-face-recognition-model-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-agedb-30)](https://paperswithcode.com/sota/lightweight-face-recognition-on-agedb-30?p=edgeface-efficient-face-recognition-model-for)	
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-ijb-b)](https://paperswithcode.com/sota/lightweight-face-recognition-on-ijb-b?p=edgeface-efficient-face-recognition-model-for)	
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/edgeface-efficient-face-recognition-model-for/lightweight-face-recognition-on-ijb-c)](https://paperswithcode.com/sota/lightweight-face-recognition-on-ijb-c?p=edgeface-efficient-face-recognition-model-for)	
+
+[![arXiv](https://img.shields.io/badge/cs.CV-arXiv%3A2307.01838-009d81v2.svg)](https://arxiv.org/abs/2307.01838v2)
+[![HF-Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-orange)](https://huggingface.co/spaces/Idiap/EdgeFace)
+[![HF-Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-green)](https://huggingface.co/collections/Idiap/edgeface-67f500eded03ecd8be56e63e)
+
+
+This repository contain inference code and pretrained models to use [**EdgeFace: Efficient Face Recognition Model for Edge Devices**](https://ieeexplore.ieee.org/abstract/document/10388036/), 
+which is the **winning entry** in *the compact track of ["EFaR 2023: Efficient Face Recognition Competition"](https://arxiv.org/abs/2308.04168) organised at the IEEE International Joint Conference on Biometrics (IJCB) 2023*. For the complete source code of training and evaluation, please check the [official repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface).
+
+
+![EdgeFace](assets/edgeface.png)
+
+## Installation
+```bash
+pip install -r requirements.txt
+```
+**Note:** If cannot `import cv2`, run above CLI in Linux
+
+```bash
+chmod +x packages.txt
+sudo ./packages.txt
+```
+
+## Inference
+The following code shows how to use the model for inference:
+```python
+import torch
+from torchvision import transforms
+from face_alignment import align
+from backbones import get_model
+
+# load model
+model_name="edgeface_s_gamma_05" # or edgeface_xs_gamma_06
+model=get_model(model_name)
+checkpoint_path=f'checkpoints/{model_name}.pt'
+model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')) # Load state dict
+model.eval() # Call eval() on the model object
+
+transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ])
+
+paths = 'tests/test_images/Elon_Musk.jpg'
+batch_size = len(paths) if isinstance(paths, (list, tuple)) else 1
+
+# Align faces (assuming align.get_aligned_face returns a list of tuples)
+aligned_result = align.get_aligned_face(paths, algorithm='yolo')
+
+transformed_inputs = [transform(result[1]) for result in aligned_result]
+transformed_inputs = torch.stack(transformed_inputs)
+
+# Extract embeddings
+embeddings = model(transformed_inputs)
+print(embeddings.shape)  # Expected: torch.Size([batch_size, 512])
+```
+
+
+
+## Pre-trained models
+- EdgeFace-s (gamma=0.5): available in [`checkpoints/edgeface_s_gamma_05.pt`](checkpoints/edgeface_s_gamma_05.pt)
+- EdgeFace-xs (gamma=0.6): available in [`checkpoints/edgeface_xs_gamma_06.pt`](checkpoints/edgeface_xs_gamma_06.pt)
+
+
+
+## Performance
+The performance of each model is reported in Table 2 of the [paper](https://arxiv.org/pdf/2307.01838v2.pdf):
+
+![performance](assets/benchmark.png)
+
+
+## :rocket: New! Using EdgeFace Models via `torch.hub`
+
+### Available Models on `torch.hub`
+
+- `edgeface_base`
+- `edgeface_s_gamma_05`
+- `edgeface_xs_q`
+- `edgeface_xs_gamma_06`
+- `edgeface_xxs`
+- `edgeface_xxs_q`
+
+**NOTE:** Models with `_q` are quantised and require less storage.
+
+### Loading EdgeFace Models with `torch.hub`
+
+You can load the models using `torch.hub` as follows:
+
+```python
+import torch
+model = torch.hub.load('otroshi/edgeface', 'edgeface_xs_gamma_06', source='github', pretrained=True)
+model.eval()
+```
+
+### Performance benchmarks of different variants of EdgeFace
+
+| Model               | MPARAMS| MFLOPs |    LFW(%)    |    CALFW(%)  |   CPLFW(%)   |   CFP-FP(%)  |   AgeDB30(%) |
+|:--------------------|-------:|-------:|:-------------|:-------------|:-------------|:-------------|:-------------|
+| edgeface_base       |  18.23 |1398.83 | 99.83 ± 0.24 | 96.07 ± 1.03 | 93.75 ± 1.16 | 97.01 ± 0.94 | 97.60 ± 0.70 |
+| edgeface_s_gamma_05 |   3.65 | 306.12 | 99.78 ± 0.27 | 95.55 ± 1.05 | 92.48 ± 1.42 | 95.74 ± 1.09 | 97.03 ± 0.85 |
+| edgeface_xs_gamma_06|   1.77 | 154.00 | 99.73 ± 0.35 | 95.28 ± 1.37 | 91.58 ± 1.42 | 94.71 ± 1.07 | 96.08 ± 0.95 |
+| edgeface_xxs        |   1.24 |  94.72 | 99.57 ± 0.33 | 94.83 ± 0.98 | 90.27 ± 0.93 | 93.63 ± 0.99 | 94.92 ± 1.15 |
+
+## Reference
+If you use this repository, please cite the following paper, which is [published](https://ieeexplore.ieee.org/abstract/document/10388036/) in the IEEE Transactions on Biometrics, Behavior, and Identity Science (IEEE T-BIOM). The PDF version of the paper is available as [pre-print on arxiv](https://arxiv.org/pdf/2307.01838v2.pdf). The complete source code for reproducing all experiments in the paper (including training and evaluation) is also publicly available in the [official repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface).
+
+
+```bibtex
+@article{edgeface,
+  title={Edgeface: Efficient face recognition model for edge devices},
+  author={George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien},
+  journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
+  year={2024}
+}
+```

src/third_party/edgeface/backbones/__init__.py

@@ -0,0 +1,37 @@
+"""
+===============================================================================
+Author: Anjith George
+Institution: Idiap Research Institute, Martigny, Switzerland.
+
+Copyright (C) 2023 Anjith George
+
+This software is distributed under the terms described in the LICENSE file 
+located in the parent directory of this source code repository. 
+
+For inquiries, please contact the author at [email protected]
+===============================================================================
+"""
+from .timmfr import get_timmfrv2, replace_linear_with_lowrank_2
+
+import torch
+
+def get_model(name, **kwargs):
+
+    if name=='edgeface_xs_gamma_06':
+        return replace_linear_with_lowrank_2(get_timmfrv2('edgenext_x_small', batchnorm=False), rank_ratio=0.6)
+    elif name=='edgeface_xs_q':
+        model= get_timmfrv2('edgenext_x_small', batchnorm=False)
+        model = torch.quantization.quantize_dynamic(model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
+        return model
+    elif  name=='edgeface_xxs':
+        return get_timmfrv2('edgenext_xx_small', batchnorm=False)
+    elif  name=='edgeface_base':
+        return get_timmfrv2('edgenext_base', batchnorm=False)
+    elif name=='edgeface_xxs_q':
+        model=get_timmfrv2('edgenext_xx_small', batchnorm=False)
+        model = torch.quantization.quantize_dynamic(model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
+        return model   
+    elif name=='edgeface_s_gamma_05':
+        return replace_linear_with_lowrank_2(get_timmfrv2('edgenext_small', batchnorm=False), rank_ratio=0.5)
+    else:
+        raise ValueError()

src/third_party/edgeface/backbones/timmfr.py

@@ -0,0 +1,78 @@
+"""
+===============================================================================
+Author: Anjith George
+Institution: Idiap Research Institute, Martigny, Switzerland.
+
+Copyright (C) 2023 Anjith George
+
+This software is distributed under the terms described in the LICENSE file 
+located in the parent directory of this source code repository. 
+
+For inquiries, please contact the author at [email protected]
+===============================================================================
+"""
+
+
+
+import timm
+import torch
+import torch.nn as nn
+import math
+
+class LoRaLin(nn.Module):
+    def __init__(self, in_features, out_features, rank, bias=True):
+        super(LoRaLin, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.rank = rank
+        self.linear1 = nn.Linear(in_features, rank, bias=False)
+        self.linear2 = nn.Linear(rank, out_features, bias=bias)
+
+    def forward(self, input):
+        x = self.linear1(input)
+        x = self.linear2(x)
+        return x
+
+def replace_linear_with_lowrank_recursive_2(model, rank_ratio=0.2):
+    for name, module in model.named_children():
+        if isinstance(module, nn.Linear) and 'head' not in name:
+            in_features = module.in_features
+            out_features = module.out_features
+            rank = max(2,int(min(in_features, out_features) * rank_ratio))
+            bias=False
+            if module.bias is not None:
+                bias=True
+            lowrank_module = LoRaLin(in_features, out_features, rank, bias)
+
+            setattr(model, name, lowrank_module)
+        else:
+            replace_linear_with_lowrank_recursive_2(module, rank_ratio)
+
+def replace_linear_with_lowrank_2(model, rank_ratio=0.2):
+    replace_linear_with_lowrank_recursive_2(model, rank_ratio)
+    return model
+
+
+        
+class TimmFRWrapperV2(nn.Module):
+    """
+    Wraps timm model
+    """
+    def __init__(self, model_name='edgenext_x_small', featdim=512, batchnorm=False):
+        super().__init__()
+        self.featdim = featdim
+        self.model_name = model_name
+        
+        self.model = timm.create_model(self.model_name)
+        self.model.reset_classifier(self.featdim)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+def get_timmfrv2(model_name, **kwargs):
+    """
+    Create an instance of TimmFRWrapperV2 with the specified `model_name` and additional arguments passed as `kwargs`.
+    """
+    return TimmFRWrapperV2(model_name=model_name, **kwargs)

src/third_party/edgeface/ckpts/edgeface_s_gamma_05.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc59abda2e8580399fd115a1eeb07e1f21156196db604b884407bcf0f17efb07
+size 14695737

src/third_party/edgeface/ckpts/edgeface_xs_gamma_06.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ae7504cd9aee0a5d52c2115fd2eb66b0985dd1730f40134b5854e0cb658ce16
+size 7170425

src/third_party/edgeface/face_alignment/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Minchul Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/third_party/edgeface/face_alignment/README.md

@@ -0,0 +1 @@
+Face alignment script is from [AdaFace](https://github.com/mk-minchul/AdaFace) repositpry: https://github.com/mk-minchul/AdaFace/tree/master/face_alignment

src/third_party/edgeface/face_alignment/align.py

@@ -0,0 +1,58 @@
+import sys
+import os
+import torch
+from . import mtcnn
+from .face_yolo import face_yolo_detection
+import argparse
+from PIL import Image
+from tqdm import tqdm
+import random
+from datetime import datetime
+
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+mtcnn_model = mtcnn.MTCNN(device=DEVICE, crop_size=(112, 112))
+
+def add_padding(pil_img, top, right, bottom, left, color=(0,0,0)):
+    width, height = pil_img.size
+    new_width = width + right + left
+    new_height = height + top + bottom
+    result = Image.new(pil_img.mode, (new_width, new_height), color)
+    result.paste(pil_img, (left, top))
+    return result
+
+def handle_image_mtcnn(img_path, pil_img):
+        img = Image.open(img_path).convert('RGB') if pil_img is None else pil_img
+        assert isinstance(img, Image.Image), 'Face alignment requires PIL image or path'
+        try:
+            bboxes, faces = mtcnn_model.align_multi(img, limit=1)
+            return bboxes[0], faces[0]
+        except Exception as e:
+            print(f'Face detection failed: {e}')
+            return None, None
+
+def get_aligned_face(image_path_or_image_paths, rgb_pil_image=None, algorithm='mtcnn'):
+    if algorithm=='mtcnn':
+        if isinstance(image_path_or_image_paths, list):
+            results = [handle_image_mtcnn(path, rgb_pil_image) for path in image_path_or_image_paths]
+            return results
+        elif isinstance(image_path_or_image_paths, str):
+            return [handle_image_mtcnn(image_path_or_image_paths, rgb_pil_image)]
+        else:
+            raise TypeError("image_path_or_image_paths must be a list or string") 
+
+    elif algorithm=='yolo':
+        if isinstance(image_path_or_image_paths, list):
+            image_paths = image_path_or_image_paths
+            results = face_yolo_detection(image_paths,
+                        # yolo_model_path="ckpts/yolo_face_detection/model.pt",
+                        use_batch=True, device=DEVICE)
+        elif isinstance(image_path_or_image_paths, str):
+            image_paths = [image_path_or_image_paths]
+            results = face_yolo_detection(image_paths,
+                        # yolo_model_path="ckpts/yolo_face_detection/model.pt",
+                        use_batch=True, device=DEVICE)
+        else:
+            raise TypeError("image_path_or_image_paths must be a list or string") 
+        results = list(results)
+    return results

src/third_party/edgeface/face_alignment/face_yolo.py

@@ -0,0 +1,149 @@
+from ultralytics import YOLO
+import cv2
+import os
+from PIL import Image
+import numpy as np
+import glob
+import sys
+import argparse
+import torch
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from utils import download_yolo_face_detection
+
+def initialize_yolo_model(yolo_model_path):
+    """Initialize YOLO model with specified device."""
+    # if device.startswith('cuda') and not torch.cuda.is_available():
+    #     print("Warning: CUDA not available, falling back to CPU.")
+    #     device = 'cpu'
+    if not os.path.exists(yolo_model_path):
+        download_yolo_face_detection.download_yolo_face_detection_model()
+    return YOLO(yolo_model_path)
+
+def process_image_results(image, image_rgb, boxes):
+    """Process bounding boxes and crop faces for a single image."""
+    bounding_boxes, cropped_faces = [], []
+    for box in boxes:
+        x1, y1, x2, y2 = map(int, box)
+        if x2 > x1 and y2 > y1 and x1 >= 0 and y1 >= 0 and x2 <= image.shape[1] and y2 <= image.shape[0]:
+            bounding_boxes.append([x1, y1, x2, y2])
+            cropped_face = image_rgb[y1:y2, x1:x2]
+            if cropped_face.size > 0:
+                pil_image = Image.fromarray(cropped_face).resize((112, 112), Image.Resampling.BILINEAR)
+                cropped_faces.append(pil_image)
+    return np.array(bounding_boxes, dtype=np.int32) if bounding_boxes else np.empty((0, 4), dtype=np.int32), cropped_faces
+
+def process_batch(model, image_paths, all_bounding_boxes, all_cropped_faces, device):
+    """Process images in batch mode using list comprehensions for efficiency."""
+    # Validate and load images, filter out invalid ones
+    valid_data = [(cv2.imread(path), path) for path in image_paths if os.path.exists(path)]
+    valid_images, valid_image_paths = zip(*[(img, path) for img, path in valid_data if img is not None]) if valid_data else ([], [])
+
+    # Append empty results for invalid images
+    for path in image_paths:
+        if not os.path.exists(path) or cv2.imread(path) is None:
+            all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32))
+            all_cropped_faces.append([])
+            print(f"Warning: {'not found' if not os.path.exists(path) else 'failed to load'} {path}. Skipping.")
+
+    # Process valid images
+    if valid_images:
+        images_rgb = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in valid_images]
+        results = model.predict(source=valid_image_paths, conf=0.25, iou=0.45, verbose=False, device=device)
+
+        # Process results with comprehension
+        for img, rgb, result in zip(valid_images, images_rgb, results):
+            bboxes, faces = process_image_results(img, rgb, result.boxes.xyxy.cpu().numpy())
+            all_bounding_boxes.append(bboxes)
+            all_cropped_faces.append(faces[0] if faces else [])
+
+def process_individual(model, image_paths, all_bounding_boxes, all_cropped_faces, device):
+    """Process images individually."""
+    for image_path in image_paths:
+        if not os.path.exists(image_path):
+            print(f"Warning: {image_path} not found. Skipping.")
+            all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32))
+            all_cropped_faces.append([])
+            continue
+        
+        image = cv2.imread(image_path)
+        if image is None:
+            print(f"Warning: Failed to load {image_path}. Skipping.")
+            all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32))
+            all_cropped_faces.append([])
+            continue
+        
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = model(image_path, conf=0.25, iou=0.45, verbose=False, device=device)
+        
+        for result in results:
+            boxes = result.boxes.xyxy.cpu().numpy()
+            bboxes, faces = process_image_results(image, image_rgb, boxes)
+            all_bounding_boxes.append(bboxes)
+            all_cropped_faces.append(faces[0] if faces else [])
+
+def face_yolo_detection(image_paths, yolo_model_path="./ckpts/yolo_face_detection/model.pt", use_batch=True, device='cuda'):
+    """Perform face detection using YOLOv11 with batch or individual processing on specified device."""
+    model = initialize_yolo_model(yolo_model_path)
+    all_bounding_boxes, all_cropped_faces = [], []
+    
+    if use_batch:
+        process_batch(model, image_paths, all_bounding_boxes, all_cropped_faces, device)
+    else:
+        process_individual(model, image_paths, all_bounding_boxes, all_cropped_faces, device)
+    
+    return zip(all_bounding_boxes, all_cropped_faces)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="YOLOv11 face detection")
+    parser.add_argument("--use-batch", action="store_true", default=True, help="Use batch processing (default: True)")
+    parser.add_argument("--image-dir", type=str, default="test/test_images", help="Input image directory")
+    parser.add_argument("--yolo-model-path", type=str, default="ckpts/yolo_face_detection/model.pt", help="YOLO model path")
+    parser.add_argument("--device", type=str, default="cuda", help="Device to run the model (e.g., 'cuda', 'cpu', 'cuda:0')")
+    
+    args = parser.parse_args()
+    
+    image_paths = (glob.glob(os.path.join(args.image_dir, "*.[jJ][pP][gG]")) + 
+                   glob.glob(os.path.join(args.image_dir, "*.[pP][nN][gG]")))
+    
+    if args.yolo_model_path:
+        yolo_model_path = args.yolo_model_path
+    else:
+        yolo_model_path = os.path.join("ckpts", "yolo_face_detection", "model.pt")
+
+    import time
+    t1 = time.time()
+    results = face_yolo_detection(image_paths, yolo_model_path, args.use_batch, args.device)
+    print("Time taken:", time.time() - t1)
+
+    # Optional: Save or process results
+    # for i, (bboxes, faces) in enumerate(results):
+    #     print(f"Image {i}: Bounding Boxes: {bboxes}")
+    #     for j, face in enumerate(faces):
+    #         face.save(f"face_{i}_{j}.png")
+
+    # Benchmarking (uncomment to use)
+    # import time
+    # num_runs = 50
+    # batch_times, individual_times = [], []
+    
+    # # Benchmark batch processing
+    # for _ in range(num_runs):
+    #     t1 = time.time()
+    #     face_yolo_detection(image_paths, yolo_model_path, use_batch=True, device=args.device)
+    #     batch_times.append(time.time() - t1)
+    
+    # # Benchmark individual processing
+    # for _ in range(num_runs):
+    #     t1 = time.time()
+    #     face_yolo_detection(image_paths, yolo_model_path, use_batch=False, device=args.device)
+    #     individual_times.append(time.time() - t1)
+    
+    # # Calculate and print average times
+    # avg_batch_time = sum(batch_times) / num_runs
+    # avg_individual_time = sum(individual_times) / num_runs
+    
+    # print(f"\nBenchmark Results (over {num_runs} runs):")
+    # print(f"Average Batch Processing Time: {avg_batch_time:.4f} seconds")
+    # print(f"Average Individual Processing Time: {avg_individual_time:.4f} seconds")

src/third_party/edgeface/face_alignment/mtcnn.py

@@ -0,0 +1,175 @@
+from typing import Tuple
+import numpy as np
+import torch
+from PIL import Image
+from torch.autograd import Variable
+
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
+from mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
+from mtcnn_pytorch.src.first_stage import run_first_stage
+from mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face
+
+
+class MTCNN():
+    def __init__(self, device: str = 'cuda:0', crop_size: Tuple[int, int] = (112, 112)):
+
+        assert device in ['cuda:0', 'cpu']
+        self.device = torch.device(device)
+        assert crop_size in [(112, 112), (96, 112)]
+        self.crop_size = crop_size
+
+        # change working dir to this file location to load npz files. Then switch back
+        cwd = os.getcwd()
+        os.chdir(os.path.dirname(__file__))
+
+        self.pnet = PNet().to(self.device)
+        self.rnet = RNet().to(self.device)
+        self.onet = ONet().to(self.device)
+        self.pnet.eval()
+        self.rnet.eval()
+        self.onet.eval()
+        self.refrence = get_reference_facial_points(default_square=crop_size[0] == crop_size[1])
+
+        self.min_face_size = 20
+        self.thresholds =  [0.6,0.7,0.9]
+        self.nms_thresholds = [0.7, 0.7, 0.7]
+        self.factor = 0.85
+
+
+        os.chdir(cwd)
+
+    def align(self, img):
+        _, landmarks = self.detect_faces(img, self.min_face_size, self.thresholds, self.nms_thresholds, self.factor)
+        facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
+        warped_face = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=self.crop_size)
+        return Image.fromarray(warped_face)
+
+    def align_multi(self, img, limit=None):
+        boxes, landmarks = self.detect_faces(img, self.min_face_size, self.thresholds, self.nms_thresholds, self.factor)
+        if limit:
+            boxes = boxes[:limit]
+            landmarks = landmarks[:limit]
+        faces = []
+        for landmark in landmarks:
+            facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
+            warped_face = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=self.crop_size)
+            faces.append(Image.fromarray(warped_face))
+        return boxes, faces
+
+    def detect_faces(self, image, min_face_size, thresholds, nms_thresholds, factor):
+        """
+        Arguments:
+            image: an instance of PIL.Image.
+            min_face_size: a float number.
+            thresholds: a list of length 3.
+            nms_thresholds: a list of length 3.
+
+        Returns:
+            two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
+            bounding boxes and facial landmarks.
+        """
+
+        # BUILD AN IMAGE PYRAMID
+        width, height = image.size
+        min_length = min(height, width)
+
+        min_detection_size = 12
+        # factor = 0.707  # sqrt(0.5)
+
+        # scales for scaling the image
+        scales = []
+
+        # scales the image so that
+        # minimum size that we can detect equals to
+        # minimum face size that we want to detect
+        m = min_detection_size / min_face_size
+        min_length *= m
+
+        factor_count = 0
+        while min_length > min_detection_size:
+            scales.append(m * factor**factor_count)
+            min_length *= factor
+            factor_count += 1
+
+        # STAGE 1
+
+        # it will be returned
+        bounding_boxes = []
+
+        with torch.no_grad():
+            # run P-Net on different scales
+            for s in scales:
+                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
+                bounding_boxes.append(boxes)
+
+            # collect boxes (and offsets, and scores) from different scales
+            bounding_boxes = [i for i in bounding_boxes if i is not None]
+            if len(bounding_boxes) == 0:
+                return [], []
+            bounding_boxes = np.vstack(bounding_boxes)
+
+            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
+            bounding_boxes = bounding_boxes[keep]
+
+            # use offsets predicted by pnet to transform bounding boxes
+            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
+            # shape [n_boxes, 5]
+
+            bounding_boxes = convert_to_square(bounding_boxes)
+            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
+
+            # STAGE 2
+
+            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
+            img_boxes = torch.FloatTensor(img_boxes).to(self.device)
+
+            output = self.rnet(img_boxes)
+            offsets = output[0].cpu().data.numpy()  # shape [n_boxes, 4]
+            probs = output[1].cpu().data.numpy()  # shape [n_boxes, 2]
+
+            keep = np.where(probs[:, 1] > thresholds[1])[0]
+            bounding_boxes = bounding_boxes[keep]
+            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
+            offsets = offsets[keep]
+
+            keep = nms(bounding_boxes, nms_thresholds[1])
+            bounding_boxes = bounding_boxes[keep]
+            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
+            bounding_boxes = convert_to_square(bounding_boxes)
+            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
+
+            # STAGE 3
+
+            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
+            if len(img_boxes) == 0:
+                return [], []
+            img_boxes = torch.FloatTensor(img_boxes).to(self.device)
+            output = self.onet(img_boxes)
+            landmarks = output[0].cpu().data.numpy()  # shape [n_boxes, 10]
+            offsets = output[1].cpu().data.numpy()  # shape [n_boxes, 4]
+            probs = output[2].cpu().data.numpy()  # shape [n_boxes, 2]
+
+            keep = np.where(probs[:, 1] > thresholds[2])[0]
+            bounding_boxes = bounding_boxes[keep]
+            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
+            offsets = offsets[keep]
+            landmarks = landmarks[keep]
+
+            # compute landmark points
+            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
+            height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
+            xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
+            landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
+            landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
+
+            bounding_boxes = calibrate_box(bounding_boxes, offsets)
+            keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
+            bounding_boxes = bounding_boxes[keep]
+            landmarks = landmarks[keep]
+
+        return bounding_boxes, landmarks

src/third_party/edgeface/face_alignment/mtcnn_pytorch/.gitignore

@@ -0,0 +1,3 @@
+.ipynb_checkpoints
+__pycache__
+

src/third_party/edgeface/face_alignment/mtcnn_pytorch/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Dan Antoshchenko
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/third_party/edgeface/face_alignment/mtcnn_pytorch/README.md

@@ -0,0 +1,26 @@
+# MTCNN
+
+`pytorch` implementation of **inference stage** of face detection algorithm described in  
+[Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://arxiv.org/abs/1604.02878).
+
+## Example
+![example of a face detection](images/example.png)
+
+## How to use it
+Just download the repository and then do this
+```python
+from src import detect_faces
+from PIL import Image
+
+image = Image.open('image.jpg')
+bounding_boxes, landmarks = detect_faces(image)
+```
+For examples see `test_on_images.ipynb`.
+
+## Requirements
+* pytorch 0.2
+* Pillow, numpy
+
+## Credit
+This implementation is heavily inspired by:
+* [pangyupo/mxnet_mtcnn_face_detection](https://github.com/pangyupo/mxnet_mtcnn_face_detection)  

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det1.prototxt

@@ -0,0 +1,177 @@
+name: "PNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 12
+input_dim: 12
+
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 10
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "conv2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+	  type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+
+
+layer {
+  name: "conv4-1"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 2
+    kernel_size: 1
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "conv4-2"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4
+    kernel_size: 1
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv4-1"
+  top: "prob1"
+}

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det2.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39b20f7a57bb8176cc9466cea4dfd52da6a6f876de60c7ab222a309f2d0ca08c
+size 407910

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det2.prototxt

@@ -0,0 +1,228 @@
+name: "RNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 24
+input_dim: 24
+
+
+##########################
+######################
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+  propagate_down: true
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+  propagate_down: true
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+####################################
+
+##################################
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+  propagate_down: true
+}
+###############################
+
+###############################
+
+layer {
+  name: "conv4"
+  type: "InnerProduct"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 128
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+
+layer {
+  name: "conv5-1"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5-1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 2
+    #kernel_size: 1
+    #stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv5-2"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 4
+    #kernel_size: 1
+    #stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv5-1"
+  top: "prob1"
+}

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det3.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d6098829a4d6d318f37cec42142465637fafe4c673f2e93b69495bf7ca23d2d
+size 1558412

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det3.prototxt

@@ -0,0 +1,294 @@
+name: "ONet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 48
+input_dim: 48
+##################################
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "prelu2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 64
+	kernel_size: 3
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 128
+	kernel_size: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+
+
+layer {
+  name: "conv5"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+	#kernel_size: 3
+	num_output: 256
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "drop5"
+  type: "Dropout"
+  bottom: "conv5"
+  top: "conv5"
+  dropout_param {
+    dropout_ratio: 0.25
+  }
+}
+layer {
+  name: "prelu5"
+  type: "PReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+
+
+layer {
+  name: "conv6-1"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    #kernel_size: 1
+	num_output: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6-2"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 4
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6-3"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 10
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv6-1"
+  top: "prob1"
+}

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det4.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23dfa3f889a8dd5d1ffe7429229270892bcb19221ede13aaac8896ea060bfb76
+size 3798152

src/third_party/edgeface/face_alignment/mtcnn_pytorch/caffe_models/det4.prototxt

@@ -0,0 +1,995 @@
+name: "LNet"
+input: "data"
+input_dim: 1
+input_dim: 15
+input_dim: 24
+input_dim: 24
+
+layer {
+  name: "slicer_data"
+  type: "Slice"
+  bottom: "data"
+  top: "data241"
+  top: "data242"
+  top: "data243"
+  top: "data244"
+  top: "data245"
+  slice_param {
+    axis: 1
+    slice_point: 3
+    slice_point: 6
+    slice_point: 9
+    slice_point: 12
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data241"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu1_1"
+  type: "PReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+
+}
+layer {
+  name: "pool1_1"
+  type: "Pooling"
+  bottom: "conv1_1"
+  top: "pool1_1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu2_1"
+  type: "PReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "pool2_1"
+  type: "Pooling"
+  bottom: "conv2_1"
+  top: "pool2_1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+  
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu3_1"
+  type: "PReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+##########################
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "data242"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu1_2"
+  type: "PReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+
+}
+layer {
+  name: "pool1_2"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "pool1_2"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu2_2"
+  type: "PReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+  
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "pool2_2"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu3_2"
+  type: "PReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+##########################
+##########################
+layer {
+  name: "conv1_3"
+  type: "Convolution"
+  bottom: "data243"
+  top: "conv1_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu1_3"
+  type: "PReLU"
+  bottom: "conv1_3"
+  top: "conv1_3"
+
+}
+layer {
+  name: "pool1_3"
+  type: "Pooling"
+  bottom: "conv1_3"
+  top: "pool1_3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2_3"
+  type: "Convolution"
+  bottom: "pool1_3"
+  top: "conv2_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu2_3"
+  type: "PReLU"
+  bottom: "conv2_3"
+  top: "conv2_3"
+}
+layer {
+  name: "pool2_3"
+  type: "Pooling"
+  bottom: "conv2_3"
+  top: "pool2_3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+  
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "pool2_3"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu3_3"
+  type: "PReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+##########################
+##########################
+layer {
+  name: "conv1_4"
+  type: "Convolution"
+  bottom: "data244"
+  top: "conv1_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu1_4"
+  type: "PReLU"
+  bottom: "conv1_4"
+  top: "conv1_4"
+
+}
+layer {
+  name: "pool1_4"
+  type: "Pooling"
+  bottom: "conv1_4"
+  top: "pool1_4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2_4"
+  type: "Convolution"
+  bottom: "pool1_4"
+  top: "conv2_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu2_4"
+  type: "PReLU"
+  bottom: "conv2_4"
+  top: "conv2_4"
+}
+layer {
+  name: "pool2_4"
+  type: "Pooling"
+  bottom: "conv2_4"
+  top: "pool2_4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+  
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "pool2_4"
+  top: "conv3_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu3_4"
+  type: "PReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+##########################
+##########################
+layer {
+  name: "conv1_5"
+  type: "Convolution"
+  bottom: "data245"
+  top: "conv1_5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu1_5"
+  type: "PReLU"
+  bottom: "conv1_5"
+  top: "conv1_5"
+
+}
+layer {
+  name: "pool1_5"
+  type: "Pooling"
+  bottom: "conv1_5"
+  top: "pool1_5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2_5"
+  type: "Convolution"
+  bottom: "pool1_5"
+  top: "conv2_5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu2_5"
+  type: "PReLU"
+  bottom: "conv2_5"
+  top: "conv2_5"
+}
+layer {
+  name: "pool2_5"
+  type: "Pooling"
+  bottom: "conv2_5"
+  top: "pool2_5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+  
+}
+layer {
+  name: "conv3_5"
+  type: "Convolution"
+  bottom: "pool2_5"
+  top: "conv3_5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu3_5"
+  type: "PReLU"
+  bottom: "conv3_5"
+  top: "conv3_5"
+}
+##########################
+layer { 
+	name: "concat" 
+	bottom: "conv3_1" 
+	bottom: "conv3_2" 
+	bottom: "conv3_3" 
+	bottom: "conv3_4" 
+	bottom: "conv3_5" 
+	top: "conv3" 
+	type: "Concat" 
+	concat_param { 
+		axis: 1 
+	} 
+}
+##########################
+layer {
+  name: "fc4"
+  type: "InnerProduct"
+  bottom: "conv3"
+  top: "fc4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 256
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "fc4"
+  top: "fc4"
+}
+############################
+layer {
+  name: "fc4_1"
+  type: "InnerProduct"
+  bottom: "fc4"
+  top: "fc4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 64
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4_1"
+  type: "PReLU"
+  bottom: "fc4_1"
+  top: "fc4_1"
+}
+layer {
+  name: "fc5_1"
+  type: "InnerProduct"
+  bottom: "fc4_1"
+  top: "fc5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+	  #type: "constant"
+      #value: 0
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+#########################
+layer {
+  name: "fc4_2"
+  type: "InnerProduct"
+  bottom: "fc4"
+  top: "fc4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 64
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4_2"
+  type: "PReLU"
+  bottom: "fc4_2"
+  top: "fc4_2"
+}
+layer {
+  name: "fc5_2"
+  type: "InnerProduct"
+  bottom: "fc4_2"
+  top: "fc5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+	  #type: "constant"
+      #value: 0
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+#########################
+layer {
+  name: "fc4_3"
+  type: "InnerProduct"
+  bottom: "fc4"
+  top: "fc4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 64
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4_3"
+  type: "PReLU"
+  bottom: "fc4_3"
+  top: "fc4_3"
+}
+layer {
+  name: "fc5_3"
+  type: "InnerProduct"
+  bottom: "fc4_3"
+  top: "fc5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+	  #type: "constant"
+      #value: 0
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+#########################
+layer {
+  name: "fc4_4"
+  type: "InnerProduct"
+  bottom: "fc4"
+  top: "fc4_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 64
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4_4"
+  type: "PReLU"
+  bottom: "fc4_4"
+  top: "fc4_4"
+}
+layer {
+  name: "fc5_4"
+  type: "InnerProduct"
+  bottom: "fc4_4"
+  top: "fc5_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+	  #type: "constant"
+      #value: 0
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+#########################
+layer {
+  name: "fc4_5"
+  type: "InnerProduct"
+  bottom: "fc4"
+  top: "fc4_5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 64
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+  
+}
+layer {
+  name: "prelu4_5"
+  type: "PReLU"
+  bottom: "fc4_5"
+  top: "fc4_5"
+}
+layer {
+  name: "fc5_5"
+  type: "InnerProduct"
+  bottom: "fc4_5"
+  top: "fc5_5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+	  #type: "constant"
+      #value: 0
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+#########################
+

src/third_party/edgeface/face_alignment/mtcnn_pytorch/extract_weights_from_caffe_models.py

@@ -0,0 +1,47 @@
+import caffe
+import numpy as np
+
+"""
+The purpose of this script is to convert pretrained weights taken from
+official implementation here:
+https://github.com/kpzhang93/MTCNN_face_detection_alignment/tree/master/code/codes/MTCNNv2
+to required format.
+
+In a nutshell, it just renames and transposes some of the weights.
+You don't have to use this script because weights are already in `src/weights`.
+"""
+
+
+def get_all_weights(net):
+    all_weights = {}
+    for p in net.params:
+        if 'conv' in p:
+            name = 'features.' + p
+            if '-' in p:
+                s = list(p)
+                s[-2] = '_'
+                s = ''.join(s)
+                all_weights[s + '.weight'] = net.params[p][0].data
+                all_weights[s + '.bias'] = net.params[p][1].data
+            elif len(net.params[p][0].data.shape) == 4:
+                all_weights[name + '.weight'] = net.params[p][0].data.transpose((0, 1, 3, 2))
+                all_weights[name + '.bias'] = net.params[p][1].data
+            else:
+                all_weights[name + '.weight'] = net.params[p][0].data
+                all_weights[name + '.bias'] = net.params[p][1].data
+        elif 'prelu' in p.lower():
+            all_weights['features.' + p.lower() + '.weight'] = net.params[p][0].data
+    return all_weights
+
+
+# P-Net
+net = caffe.Net('caffe_models/det1.prototxt', 'caffe_models/det1.caffemodel', caffe.TEST)
+np.save('src/weights/pnet.npy', get_all_weights(net))
+
+# R-Net
+net = caffe.Net('caffe_models/det2.prototxt', 'caffe_models/det2.caffemodel', caffe.TEST)
+np.save('src/weights/rnet.npy', get_all_weights(net))
+
+# O-Net
+net = caffe.Net('caffe_models/det3.prototxt', 'caffe_models/det3.caffemodel', caffe.TEST)
+np.save('src/weights/onet.npy', get_all_weights(net))

src/third_party/edgeface/face_alignment/mtcnn_pytorch/refine_faces.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:06:15.533290Z",
+     "start_time": "2018-07-21T07:06:15.509560Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from src import detect_faces, show_bboxes\n",
+    "from PIL import Image\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from src.align_trans import get_reference_facial_points, warp_and_crop_face\n",
+    "import mxnet as mx\n",
+    "import io\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:08:15.237357Z",
+     "start_time": "2018-07-21T07:08:15.214563Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "face_folder = Path('/home/f/learning/Dataset/faces_vgg_112x112')\n",
+    "bin_path = face_folder/'train.rec'\n",
+    "idx_path = face_folder/'train.idx'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:08:20.176501Z",
+     "start_time": "2018-07-21T07:08:17.337626Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "imgrec = mx.recordio.MXIndexedRecordIO(str(idx_path), str(bin_path), 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:10:25.708722Z",
+     "start_time": "2018-07-21T07:10:25.687476Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEADER(flag=0, label=2.0, id=813, id2=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=112x112 at 0x7FCAB5C7C048>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i =813\n",
+    "\n",
+    "img_info = imgrec.read_idx(i)\n",
+    "\n",
+    "header, img = mx.recordio.unpack(img_info)\n",
+    "\n",
+    "encoded_jpg_io = io.BytesIO(img)\n",
+    "\n",
+    "image = Image.open(encoded_jpg_io)\n",
+    "\n",
+    "print(header)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:10:26.732578Z",
+     "start_time": "2018-07-21T07:10:26.711066Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(112, 112)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:10:29.714824Z",
+     "start_time": "2018-07-21T07:10:29.676756Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bounding_boxes, landmarks = detect_faces(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:10:30.404858Z",
+     "start_time": "2018-07-21T07:10:30.386340Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[ 13.36201936,   5.58984986,  78.93511893, 104.44713098,\n",
+       "           0.99996698]]),\n",
+       " array([[45.040733, 73.22949 , 67.01588 , 46.294598, 68.35203 , 47.975132,\n",
+       "         46.75182 , 68.91486 , 85.37722 , 84.38674 ]], dtype=float32))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bounding_boxes,landmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:14:20.172835Z",
+     "start_time": "2018-07-21T07:14:20.138160Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "FaceWarpException",
+     "evalue": "In File /root/Notebooks/face/mtcnn-pytorch/src/align_trans.py:FaceWarpException('No paddings to do, output_size must be None or [ 96 112]',)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFaceWarpException\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-36-1da710ed1190>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mlandmark\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlandmarks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mfacial5points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mdst_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwarp_and_crop_face\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mface\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfacial5points\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrop_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m112\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m112\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Notebooks/face/mtcnn-pytorch/src/align_trans.py\u001b[0m in \u001b[0;36mwarp_and_crop_face\u001b[0;34m(src_img, facial_pts, reference_pts, crop_size, align_type)\u001b[0m\n\u001b[1;32m    258\u001b[0m                                                         \u001b[0minner_padding_factor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                                                         \u001b[0mouter_padding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m                                                         default_square)\n\u001b[0m\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0mref_pts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreference_pts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Notebooks/face/mtcnn-pytorch/src/align_trans.py\u001b[0m in \u001b[0;36mget_reference_facial_points\u001b[0;34m(output_size, inner_padding_factor, outer_padding, default_square)\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m             raise FaceWarpException(\n\u001b[0;32m--> 104\u001b[0;31m                 'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;31m# check output size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFaceWarpException\u001b[0m: In File /root/Notebooks/face/mtcnn-pytorch/src/align_trans.py:FaceWarpException('No paddings to do, output_size must be None or [ 96 112]',)"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "faces = []\n",
+    "img_cv2 = np.array(image)[...,::-1]\n",
+    "for i in tqdm(range(len(bounding_boxes))):\n",
+    "    box = bounding_boxes[i][:4].astype(np.int32).tolist()\n",
+    "    for idx, coord in enumerate(box[:2]):\n",
+    "        if coord > 1:\n",
+    "            box[idx] -= 1\n",
+    "    if box[2] + 1 < img_cv2.shape[1]:\n",
+    "        box[2] += 1\n",
+    "    if box[3] + 1 < img_cv2.shape[0]:\n",
+    "        box[3] += 1\n",
+    "    face = img_cv2[box[1]:box[3],box[0]:box[2]]\n",
+    "    landmark = landmarks[i]\n",
+    "    facial5points = [[landmark[j] - box[0],landmark[j+5] - box[1]] for j in range(5)]\n",
+    "    dst_img = warp_and_crop_face(face,facial5points, crop_size=(112,112))\n",
+    "    faces.append(Image.fromarray(dst_img[...,::-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:21:45.873749Z",
+     "start_time": "2018-07-21T07:21:45.857902Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "reference_pts = get_reference_facial_points(default_square= True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:22:21.544120Z",
+     "start_time": "2018-07-21T07:22:21.517479Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/Notebooks/face/mtcnn-pytorch/src/matlab_cp2tform.py:90: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  r, _, _, _ = lstsq(X, U)\n"
+     ]
+    }
+   ],
+   "source": [
+    "dst_img = warp_and_crop_face(face, facial5points, reference_pts, crop_size=(112,112))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-21T07:22:31.344783Z",
+     "start_time": "2018-07-21T07:22:31.313710Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=112x112 at 0x7FCAB5CB4438>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image.fromarray(dst_img[...,::-1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/third_party/edgeface/face_alignment/mtcnn_pytorch/src/__init__.py

@@ -0,0 +1,2 @@
+from .visualization_utils import show_bboxes
+from .detector import detect_faces

src/third_party/edgeface/face_alignment/mtcnn_pytorch/src/align_trans.py

@@ -0,0 +1,304 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr 24 15:43:29 2017
+@author: zhaoy
+"""
+import numpy as np
+import cv2
+
+# from scipy.linalg import lstsq
+# from scipy.ndimage import geometric_transform  # , map_coordinates
+
+from mtcnn_pytorch.src.matlab_cp2tform import get_similarity_transform_for_cv2
+
+# reference facial points, a list of coordinates (x,y)
+REFERENCE_FACIAL_POINTS = [
+    [30.29459953,  51.69630051],
+    [65.53179932,  51.50139999],
+    [48.02519989,  71.73660278],
+    [33.54930115,  92.3655014],
+    [62.72990036,  92.20410156]
+]
+
+DEFAULT_CROP_SIZE = (96, 112)
+
+
+class FaceWarpException(Exception):
+    def __str__(self):
+        return 'In File {}:{}'.format(
+            __file__, super.__str__(self))
+
+
+def get_reference_facial_points(output_size=None,
+                                inner_padding_factor=0.0,
+                                outer_padding=(0, 0),
+                                default_square=False):
+    """
+    Function:
+    ----------
+        get reference 5 key points according to crop settings:
+        0. Set default crop_size:
+            if default_square: 
+                crop_size = (112, 112)
+            else: 
+                crop_size = (96, 112)
+        1. Pad the crop_size by inner_padding_factor in each side;
+        2. Resize crop_size into (output_size - outer_padding*2),
+            pad into output_size with outer_padding;
+        3. Output reference_5point;
+    Parameters:
+    ----------
+        @output_size: (w, h) or None
+            size of aligned face image
+        @inner_padding_factor: (w_factor, h_factor)
+            padding factor for inner (w, h)
+        @outer_padding: (w_pad, h_pad)
+            each row is a pair of coordinates (x, y)
+        @default_square: True or False
+            if True:
+                default crop_size = (112, 112)
+            else:
+                default crop_size = (96, 112);
+        !!! make sure, if output_size is not None:
+                (output_size - outer_padding) 
+                = some_scale * (default crop_size * (1.0 + inner_padding_factor))
+    Returns:
+    ----------
+        @reference_5point: 5x2 np.array
+            each row is a pair of transformed coordinates (x, y)
+    """
+    #print('\n===> get_reference_facial_points():')
+
+    #print('---> Params:')
+    #print('            output_size: ', output_size)
+    #print('            inner_padding_factor: ', inner_padding_factor)
+    #print('            outer_padding:', outer_padding)
+    #print('            default_square: ', default_square)
+
+    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
+    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
+
+    # 0) make the inner region a square
+    if default_square:
+        size_diff = max(tmp_crop_size) - tmp_crop_size
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += size_diff
+
+    #print('---> default:')
+    #print('              crop_size = ', tmp_crop_size)
+    #print('              reference_5pts = ', tmp_5pts)
+
+    if (output_size and
+            output_size[0] == tmp_crop_size[0] and
+            output_size[1] == tmp_crop_size[1]):
+        #print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))
+        return tmp_5pts
+
+    if (inner_padding_factor == 0 and
+            outer_padding == (0, 0)):
+        if output_size is None:
+            #print('No paddings to do: return default reference points')
+            return tmp_5pts
+        else:
+            raise FaceWarpException(
+                'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
+
+    # check output size
+    if not (0 <= inner_padding_factor <= 1.0):
+        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
+
+    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0)
+            and output_size is None):
+        output_size = tmp_crop_size * \
+            (1 + inner_padding_factor * 2).astype(np.int32)
+        output_size += np.array(outer_padding)
+        #print('              deduced from paddings, output_size = ', output_size)
+
+    if not (outer_padding[0] < output_size[0]
+            and outer_padding[1] < output_size[1]):
+        raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
+                                'and outer_padding[1] < output_size[1])')
+
+    # 1) pad the inner region according inner_padding_factor
+    #print('---> STEP1: pad the inner region according inner_padding_factor')
+    if inner_padding_factor > 0:
+        size_diff = tmp_crop_size * inner_padding_factor * 2
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += np.round(size_diff).astype(np.int32)
+
+    #print('              crop_size = ', tmp_crop_size)
+    #print('              reference_5pts = ', tmp_5pts)
+
+    # 2) resize the padded inner region
+    #print('---> STEP2: resize the padded inner region')
+    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
+    #print('              crop_size = ', tmp_crop_size)
+    #print('              size_bf_outer_pad = ', size_bf_outer_pad)
+
+    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
+        raise FaceWarpException('Must have (output_size - outer_padding)'
+                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
+
+    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
+    #print('              resize scale_factor = ', scale_factor)
+    tmp_5pts = tmp_5pts * scale_factor
+#    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
+#    tmp_5pts = tmp_5pts + size_diff / 2
+    tmp_crop_size = size_bf_outer_pad
+    #print('              crop_size = ', tmp_crop_size)
+    #print('              reference_5pts = ', tmp_5pts)
+
+    # 3) add outer_padding to make output_size
+    reference_5point = tmp_5pts + np.array(outer_padding)
+    tmp_crop_size = output_size
+    #print('---> STEP3: add outer_padding to make output_size')
+    #print('              crop_size = ', tmp_crop_size)
+    #print('              reference_5pts = ', tmp_5pts)
+
+    #print('===> end get_reference_facial_points\n')
+
+    return reference_5point
+
+
+def get_affine_transform_matrix(src_pts, dst_pts):
+    """
+    Function:
+    ----------
+        get affine transform matrix 'tfm' from src_pts to dst_pts
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points matrix, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points matrix, each row is a pair of coordinates (x, y)
+    Returns:
+    ----------
+        @tfm: 2x3 np.array
+            transform matrix from src_pts to dst_pts
+    """
+
+    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
+    n_pts = src_pts.shape[0]
+    ones = np.ones((n_pts, 1), src_pts.dtype)
+    src_pts_ = np.hstack([src_pts, ones])
+    dst_pts_ = np.hstack([dst_pts, ones])
+
+#    #print(('src_pts_:\n' + str(src_pts_))
+#    #print(('dst_pts_:\n' + str(dst_pts_))
+
+    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
+
+#    #print(('np.linalg.lstsq return A: \n' + str(A))
+#    #print(('np.linalg.lstsq return res: \n' + str(res))
+#    #print(('np.linalg.lstsq return rank: \n' + str(rank))
+#    #print(('np.linalg.lstsq return s: \n' + str(s))
+
+    if rank == 3:
+        tfm = np.float32([
+            [A[0, 0], A[1, 0], A[2, 0]],
+            [A[0, 1], A[1, 1], A[2, 1]]
+        ])
+    elif rank == 2:
+        tfm = np.float32([
+            [A[0, 0], A[1, 0], 0],
+            [A[0, 1], A[1, 1], 0]
+        ])
+
+    return tfm
+
+
+def warp_and_crop_face(src_img,
+                       facial_pts,
+                       reference_pts=None,
+                       crop_size=(96, 112),
+                       align_type='smilarity'):
+    """
+    Function:
+    ----------
+        apply affine transform 'trans' to uv
+    Parameters:
+    ----------
+        @src_img: 3x3 np.array
+            input image
+        @facial_pts: could be
+            1)a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        @reference_pts: could be
+            1) a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        or
+            3) None
+            if None, use default reference facial points
+        @crop_size: (w, h)
+            output face image size
+        @align_type: transform type, could be one of
+            1) 'similarity': use similarity transform
+            2) 'cv2_affine': use the first 3 points to do affine transform,
+                    by calling cv2.getAffineTransform()
+            3) 'affine': use all points to do affine transform
+    Returns:
+    ----------
+        @face_img: output face image with size (w, h) = @crop_size
+    """
+
+    if reference_pts is None:
+        if crop_size[0] == 96 and crop_size[1] == 112:
+            reference_pts = REFERENCE_FACIAL_POINTS
+        else:
+            default_square = False
+            inner_padding_factor = 0
+            outer_padding = (0, 0)
+            output_size = crop_size
+
+            reference_pts = get_reference_facial_points(output_size,
+                                                        inner_padding_factor,
+                                                        outer_padding,
+                                                        default_square)
+
+    ref_pts = np.float32(reference_pts)
+    ref_pts_shp = ref_pts.shape
+    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
+        raise FaceWarpException(
+            'reference_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if ref_pts_shp[0] == 2:
+        ref_pts = ref_pts.T
+
+    src_pts = np.float32(facial_pts)
+    src_pts_shp = src_pts.shape
+    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
+        raise FaceWarpException(
+            'facial_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if src_pts_shp[0] == 2:
+        src_pts = src_pts.T
+
+#    #print('--->src_pts:\n', src_pts
+#    #print('--->ref_pts\n', ref_pts
+
+    if src_pts.shape != ref_pts.shape:
+        raise FaceWarpException(
+            'facial_pts and reference_pts must have the same shape')
+
+    if align_type is 'cv2_affine':
+        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
+#        #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
+    elif align_type is 'affine':
+        tfm = get_affine_transform_matrix(src_pts, ref_pts)
+#        #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm))
+    else:
+        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
+#        #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm))
+
+#    #print('--->Transform matrix: '
+#    #print(('type(tfm):' + str(type(tfm)))
+#    #print(('tfm.dtype:' + str(tfm.dtype))
+#    #print( tfm
+
+    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
+
+    return face_img