the APP

app.py

@@ -1,34 +1,45 @@
-import gradio as gr
+import cv2
+import torch
 from transformers import DetrImageProcessor, DetrForObjectDetection
 from PIL import Image
-import torch
-import cv2
+import gradio as gr
 import numpy as np
 
-def process_image(input_image):
+# Function for DETR object detection
+def inf(_, webcam_image):
+    # Initialize model and processor
     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
+    yellow = (0, 255, 255)  # in BGR
     stroke = 2
 
-    # Convert PIL image to OpenCV format
-    img = np.array(input_image)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    # Convert the webcam image to the correct format
+    img = cv2.cvtColor(webcam_image, cv2.COLOR_BGR2RGB)
+    pil_image = Image.fromarray(img)
 
-    # Process the image
-    inputs = processor(images=input_image, return_tensors="pt")
+    # Process the image with DETR
+    inputs = processor(images=pil_image, return_tensors="pt")
     outputs = model(**inputs)
-    target_sizes = torch.tensor([input_image.size[::-1]])
+    target_sizes = torch.tensor([pil_image.size[::-1]])
     results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
 
+    # Draw bounding boxes and labels
     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)
+        cv2.rectangle(webcam_image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), yellow, stroke)
+        cv2.putText(webcam_image, model.config.id2label[label.item()], (int(box[0]), int(box[1]-10)), cv2.FONT_HERSHEY_SIMPLEX, 1, yellow, stroke, cv2.LINE_AA)
+
+    # Return the processed image
+    return webcam_image
 
-    # Convert back to PIL image
-    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+# Gradio interface with webcam support
+demo = gr.Interface(
+    inf,
+    [
+        gr.Markdown("## Real-Time Object Detection"),
+        gr.Image(source="webcam", streaming=True)
+    ],
+    "image",
+    live=True
+)
+demo.launch(server_name="0.0.0.0", share=True)
 
-# Create Gradio interface
-iface = gr.Interface(fn=process_image, inputs=gr.inputs.Image(), outputs="image")
-iface.launch()