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.gitattributes

@@ -37,3 +37,4 @@ SPIGA/spiga/data/annotations/merlrav/train.json filter=lfs diff=lfs merge=lfs -t
 SPIGA/spiga/data/annotations/wflw/train.json filter=lfs diff=lfs merge=lfs -text
 SPIGA/spiga/eval/results/merlrav/results_merlrav_test.json filter=lfs diff=lfs merge=lfs -text
 SPIGA/spiga/eval/results/wflw/results_wflw_test.json filter=lfs diff=lfs merge=lfs -text
+Protector_Cromwell_style.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,17 @@
+import gradio as gr
+from pred_color import *
+
+example_sample = [
+    "babyxiang_ai.png"
+]
+
+def pred_func(img):
+    out = single_pred_features(img)
+    if type(out) == type({}):
+        return out["spiga_seg"]
+
+gr=gr.Interface(fn=pred_func, inputs=['image',],
+outputs=[gr.Image(label='output').style(height=512)],
+examples=example_sample if example_sample else None,
+)
+gr.launch(share=False)

control_requirements.txt

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+accelerate
+torchvision
+transformers>=4.25.1
+ftfy
+tensorboard
+datasets
+torch
+git+https://github.com/huggingface/diffusers
+matplotlib>=3.2.1
+numpy>=1.18.2
+opencv-python-headless
+Pillow>=7.0.0
+torch>=1.4.0
+torchvision>=0.5.0
+torchaudio
+scipy
+scikit-learn
+datasets
+retinaface-py>=0.0.2
+gradio

pred_color.py

@@ -0,0 +1,128 @@
+###
+'''
+!git clone https://huggingface.co/spaces/radames/SPIGA-face-alignment-headpose-estimator
+!cp -r SPIGA-face-alignment-headpose-estimator/SPIGA .
+!pip install -r SPIGA/requirements.txt
+!pip install datasets
+!pip install retinaface-py>=0.0.2
+!huggingface-cli login
+'''
+
+import sys
+sys.path.insert(0, "SPIGA")
+
+import numpy as np
+
+from datasets import load_dataset
+from spiga.inference.config import ModelConfig
+from spiga.inference.framework import SPIGAFramework
+
+processor = SPIGAFramework(ModelConfig("300wpublic"))
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from matplotlib.path import Path
+import PIL
+
+def get_patch(landmarks, color='lime', closed=False):
+    contour = landmarks
+    ops = [Path.MOVETO] + [Path.LINETO]*(len(contour)-1)
+    facecolor = (0, 0, 0, 0)      # Transparent fill color, if open
+    if closed:
+        contour.append(contour[0])
+        ops.append(Path.CLOSEPOLY)
+        facecolor = color
+    path = Path(contour, ops)
+    return patches.PathPatch(path, facecolor=facecolor, edgecolor=color, lw=4)
+
+# Draw to a buffer.
+
+def conditioning_from_landmarks(landmarks, size=512):
+    # Precisely control output image size
+    dpi = 72
+    fig, ax = plt.subplots(1, figsize=[size/dpi, size/dpi], tight_layout={'pad':0})
+    fig.set_dpi(dpi)
+
+    black = np.zeros((size, size, 3))
+    ax.imshow(black)
+
+    face_patch = get_patch(landmarks[0:17])
+    l_eyebrow = get_patch(landmarks[17:22], color='yellow')
+    r_eyebrow = get_patch(landmarks[22:27], color='yellow')
+    nose_v = get_patch(landmarks[27:31], color='orange')
+    nose_h = get_patch(landmarks[31:36], color='orange')
+    l_eye = get_patch(landmarks[36:42], color='magenta', closed=True)
+    r_eye = get_patch(landmarks[42:48], color='magenta', closed=True)
+    outer_lips = get_patch(landmarks[48:60], color='cyan', closed=True)
+    inner_lips = get_patch(landmarks[60:68], color='blue', closed=True)
+
+    ax.add_patch(face_patch)
+    ax.add_patch(l_eyebrow)
+    ax.add_patch(r_eyebrow)
+    ax.add_patch(nose_v)
+    ax.add_patch(nose_h)
+    ax.add_patch(l_eye)
+    ax.add_patch(r_eye)
+    ax.add_patch(outer_lips)
+    ax.add_patch(inner_lips)
+
+    plt.axis('off')
+
+    fig.canvas.draw()
+    buffer, (width, height) = fig.canvas.print_to_buffer()
+    assert width == height
+    assert width == size
+
+    buffer = np.frombuffer(buffer, np.uint8).reshape((height, width, 4))
+    buffer = buffer[:, :, 0:3]
+    plt.close(fig)
+    return PIL.Image.fromarray(buffer)
+
+import retinaface
+import spiga.demo.analyze.track.retinasort.config as cfg
+
+config = cfg.cfg_retinasort
+device = "cpu"
+face_detector = retinaface.RetinaFaceDetector(model=config['retina']['model_name'],
+                                              device=device,
+                                              extra_features=config['retina']['extra_features'],
+                                              cfg_postreat=config['retina']['postreat'])
+
+import cv2
+Image = PIL.Image
+import os
+def single_pred_features(image):
+    if type(image) == type("") and os.path.exists(image):
+        image = Image.open(image).convert("RGB")
+    elif hasattr(image, "shape"):
+        image = Image.fromarray(image).convert("RGB")
+    else:
+        image = image.convert("RGB")
+    image = image.resize((512, 512))
+    cv2_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    face_detector.set_input_shape(image.size[1], image.size[0])
+    features = face_detector.inference(image)
+    if features:
+        bboxes = features['bbox']
+        bboxes_n = []
+        for bbox in bboxes:
+            x1, y1, x2, y2 = bbox[:4]
+            bbox_wh = [x1, y1, x2-x1, y2-y1]
+            bboxes_n.append(bbox_wh)
+        face_features = processor.inference(cv2_image, bboxes_n)
+        landmarks = face_features["landmarks"][0]
+        face_features["spiga"] = landmarks
+        face_features['spiga_seg'] = conditioning_from_landmarks(landmarks)
+        return face_features
+
+if __name__ == "__main__":
+    from datasets import load_dataset, Dataset
+    ds = load_dataset("svjack/facesyntheticsspigacaptioned_en_zh_1")
+    dss = ds["train"]
+
+    xiangbaobao = PIL.Image.open("babyxiang.png")
+    out = single_pred_features(xiangbaobao.resize((512, 512)))
+    out["spiga_seg"]
+
+    out = single_pred_features(dss[0]["image"])
+    out["spiga_seg"]