the REAL APP

app.py

@@ -1,45 +1,42 @@
-import cv2
-import torch
+import gradio as gr
 from transformers import DetrImageProcessor, DetrForObjectDetection
 from PIL import Image
-import gradio as gr
+import torch
+import cv2
 import numpy as np
 
-# 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)  # in BGR
-    stroke = 2
+# Initialize the model and processor
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-    # Convert the webcam image to the correct format
-    img = cv2.cvtColor(webcam_image, cv2.COLOR_BGR2RGB)
+def process_frame(webcam_image):
+    # Convert the webcam image from Gradio to the format expected by the model
+    img = cv2.cvtColor(np.array(webcam_image), cv2.COLOR_RGB2BGR)
     pil_image = Image.fromarray(img)
 
-    # Process the image with DETR
+    # Process the image
     inputs = processor(images=pil_image, return_tensors="pt")
     outputs = model(**inputs)
     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
+    # Draw bounding boxes and labels on the image
     for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        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)
+        box = [int(round(i, 0)) for i in box.tolist()]
+        cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), (0, 255, 255), 2)
+        label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}"
+        cv2.putText(img, label_text, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
 
-    # Return the processed image
-    return webcam_image
+    # Convert back to RGB for Gradio display
+    processed_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(processed_image)
 
-# Gradio interface with webcam support
+# Gradio interface
 demo = gr.Interface(
-    inf,
-    [
-        gr.Markdown("## Real-Time Object Detection"),
-        gr.Image(source="webcam", streaming=True)
-    ],
-    "image",
+    fn=process_frame,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs="image",
     live=True
 )
-demo.launch(server_name="0.0.0.0", share=True)
 
+demo.launch()