the APP

app.py

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+import gradio as gr
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image
+import torch
+import cv2
+import numpy as np
+
+def process_image(input_image):
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    yellow = (0, 255, 255)  # BGR
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    stroke = 2
+
+    # Convert PIL image to OpenCV format
+    img = np.array(input_image)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    # Process the image
+    inputs = processor(images=input_image, return_tensors="pt")
+    outputs = model(**inputs)
+    target_sizes = torch.tensor([input_image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        cv2.rectangle(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), yellow, stroke)
+        cv2.putText(img, model.config.id2label[label.item()], (int(box[0]), int(box[1]-10)), font, 1, yellow, stroke, cv2.LINE_AA)
+
+    # Convert back to PIL image
+    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+# Create Gradio interface
+iface = gr.Interface(fn=process_image, inputs=gr.inputs.Image(), outputs="image")
+iface.launch()

requirements.txt

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+torch
+transformers
+timm
+opencv-python
+gradio