Create app.py

app.py

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+import gradio as gr
+import cv2
+from transformers import YolosImageProcessor, YolosForObjectDetection
+from PIL import Image
+import torch
+
+# Load model and processor
+model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
+image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
+
+def process_frame(frame):
+    # Resize the frame to reduce processing time
+    frame = cv2.resize(frame, (640, 360))  # downscaling the frame
+    
+    # Convert the frame (numpy array) to PIL image
+    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    
+    # Prepare the image for the model
+    inputs = image_processor(images=image, return_tensors="pt")
+    
+    # Perform object detection
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Post-process the outputs to extract bounding boxes and labels
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
+    
+    # Draw the bounding boxes on the original frame
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
+        cv2.putText(frame, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}", 
+                    (int(box[0]), int(box[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+    
+    return frame
+
+def video_object_detection(video):
+    cap = cv2.VideoCapture(video)
+    processed_frames = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Optionally skip frames to speed up processing
+        # if int(cap.get(cv2.CAP_PROP_POS_FRAMES)) % 2 == 0:  # Process every 2nd frame
+        processed_frame = process_frame(frame)
+        processed_frames.append(processed_frame)
+
+    cap.release()
+
+    # Convert processed frames to a video for display
+    height, width, _ = processed_frames[0].shape
+    output_video = cv2.VideoWriter('/tmp/output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 20, (width, height))
+
+    for frame in processed_frames:
+        output_video.write(frame)
+
+    output_video.release()
+
+    return '/tmp/output.mp4'
+
+# Create Gradio interface with live=True
+iface = gr.Interface(fn=video_object_detection, inputs="video", outputs="video", title="YOLOs-Tiny Video Detection", live=True)
+iface.launch()