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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+samples/god.jpg filter=lfs diff=lfs merge=lfs -text
+samples/road.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,106 @@
+
+import torch
+from transformers import pipeline
+
+from PIL import Image
+
+import cv2
+import numpy as np
+
+from random import choice
+import io
+
+detector50 = pipeline(model="facebook/detr-resnet-50")
+
+detector101 = pipeline(model="facebook/detr-resnet-101")
+
+
+import gradio as gr
+
+COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
+            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
+            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
+
+fdic = {
+    "family" : "Impact",
+    "style" : "italic",
+    "size" : 15,
+    "color" : "yellow",
+    "weight" : "bold"
+}
+
+
+def get_figure(in_pil_img, in_results):
+    # Convert PIL image to OpenCV format
+    img_cv2 = np.array(in_pil_img)
+    img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_RGB2BGR)
+    
+    for prediction in in_results:
+        selected_color = choice(COLORS)
+        color = tuple(int(selected_color[i:i+2], 16) for i in (1, 3, 5))  # Convert hex color to RGB tuple
+
+        x, y = prediction['box']['xmin'], prediction['box']['ymin']
+        w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
+
+        # Draw bounding box using OpenCV
+        img_cv2 = cv2.rectangle(img_cv2, (x, y), (x+w, y+h), color, 2)
+        text = f"{prediction['label']}: {round(prediction['score']*100, 1)}%"
+        img_cv2 = cv2.putText(img_cv2, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
+
+    # Convert back to PIL format
+    img_pil = Image.fromarray(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
+    return img_pil
+
+
+def infer(model, in_pil_img):
+
+    results = None
+    if model == "detr-resnet-101":
+        results = detector101(in_pil_img)
+    else:
+        results = detector50(in_pil_img)
+
+    output_pil_img = get_figure(in_pil_img, results)
+
+    output_pil_img.save("output.jpg")
+     
+    return output_pil_img
+
+
+with gr.Blocks(title="DETR Object Detection using openCV",
+                    css=".gradio-container {background:lightyellow;}"
+               ) as demo:
+    #sample_index = gr.State([])
+
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">ObjecTron🪄</div>""")
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">
+            A object detection app using OpenCV, Huggingface-transformers, detr-resnet and Gradio </div>""")
+    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
+
+    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">2-a. Select an example below</h4>""")
+    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
+
+    with gr.Row():
+        input_image = gr.Image(label="Input image", type="pil")
+        output_image = gr.Image(label="Output image with predicted instances", type="pil")
+
+    gr.Examples(['samples/god.jpg','samples/road.jpg','samples/cats.jpg','samples/detectron.png','samples/dogandcat.jpg'], inputs=input_image)
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">3. Then, click the button below to predict and see the magic!!!</h4>""")
+
+    send_btn = gr.Button("Expecto Patronum 🪄")
+    send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
+    gr.HTML("""<ul>""")
+    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR by facebookresearch</a>""")
+    gr.HTML("""</ul>""")
+
+
+#demo.queue()
+demo.launch(debug=True)

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+timm
+opencv-python
+pillow