Create app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import pytesseract
+
+# Load the trained YOLOv5 model (replace 'best.pt' with your actual model path)
+model = torch.hub.load('ultralytics/yolov5:v6.0', 'custom', path='runs/train/exp/weights/yolov10n.pt')
+
+def process_video(input_video):
+    # Read video frames
+    cap = cv2.VideoCapture(input_video.name)
+    output_video = "output.mp4"
+
+    # Get video details
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+
+    out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Use YOLO model to detect license plates
+        results = model(frame)
+        detected_boxes = results.xyxy[0]  # Bounding boxes, confidence scores, and class IDs
+
+        # Loop through all the detected bounding boxes
+        for box in detected_boxes:
+            x1, y1, x2, y2, conf, cls = map(int, box[:6])  # Extract bounding box coordinates and confidence
+            if conf > 0.5:  # You can adjust the confidence threshold as needed
+                # Draw the bounding box on the frame
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
+
+                # Optionally, draw the confidence score and label (use class names for 3 classes)
+                if cls == 0:
+                    label = "Analog License Plate"
+                elif cls == 1:
+                    label = "Digital License Plate"
+                elif cls == 2:
+                    label = "Non-License Plate"
+                else:
+                    label = "Unknown"
+                
+                # Draw label and confidence on frame
+                cv2.putText(frame, f"{label}: {conf:.2f}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+                # Optionally, collect the bounding box coordinates for further processing (e.g., OCR)
+                license_plate = frame[y1:y2, x1:x2]
+                # Convert to grayscale for better OCR results
+                gray_license_plate = cv2.cvtColor(license_plate, cv2.COLOR_BGR2GRAY)
+
+                # Use Tesseract OCR to extract text from Bangla license plates (adjust config as needed)
+                text = pytesseract.image_to_string(gray_license_plate, config="--psm 6 -l ben")  # 'ben' is for Bangla
+                print(f"Detected License Plate Text: {text.strip()}")
+
+        # Write the annotated frame to output video
+        out.write(frame)
+
+    cap.release()
+    out.release()
+    return output_video
+
+# Create Gradio Interface
+interface = gr.Interface(fn=process_video, inputs=gr.inputs.Video(), outputs=gr.outputs.Video())
+interface.launch()