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| import gradio as gr | |
| import torch | |
| import cv2 | |
| import pytesseract | |
| import numpy as np | |
| from PIL import Image | |
| from ultralytics import YOLO | |
| # Load model | |
| model = YOLO("/home/user/app/best.pt") | |
| # Label map | |
| label_map = {0: "Analog", 1: "Digital", 2: "Non-LP"} | |
| def process_frame(frame): | |
| # Resize to YOLO input shape | |
| input_img = cv2.resize(frame, (640, 640)) | |
| results = model(input_img)[0] | |
| detections = results.boxes.data.cpu().numpy() | |
| extracted_texts = [] | |
| confidences = [] | |
| for det in detections: | |
| if len(det) < 6: | |
| continue | |
| x1, y1, x2, y2, conf, cls = det | |
| x1, y1, x2, y2 = map(int, [x1, y1, x2, y2]) | |
| label = label_map.get(int(cls), "Unknown") | |
| percent = f"{conf * 100:.2f}%" | |
| # Draw box and label on image | |
| cv2.rectangle(input_img, (x1, y1), (x2, y2), (255, 0, 0), 2) | |
| cv2.putText(input_img, f"{label}: {percent}", (x1, y1 - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) | |
| # OCR | |
| cropped = frame[y1:y2, x1:x2] # Use original frame for OCR | |
| if cropped.size > 0: | |
| gray = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY) | |
| text = pytesseract.image_to_string(gray, config="--psm 6 -l ben") | |
| extracted_texts.append(text.strip()) | |
| confidences.append(percent) | |
| # Convert to PIL | |
| annotated = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB) | |
| pil_img = Image.fromarray(annotated) | |
| return pil_img, "\n".join(extracted_texts), ", ".join(confidences) | |
| def process_input(input_file): | |
| file_path = input_file.name | |
| if file_path.endswith(('.mp4', '.avi', '.mov')): | |
| cap = cv2.VideoCapture(file_path) | |
| ret, frame = cap.read() | |
| cap.release() | |
| if not ret: | |
| return None, "Couldn't read video", "" | |
| else: | |
| frame = cv2.imread(file_path) | |
| if frame is None: | |
| return None, "Invalid image", "" | |
| return process_frame(frame) | |
| interface = gr.Interface( | |
| fn=process_input, | |
| inputs=gr.File(type="filepath", label="Upload Image or Video"), | |
| outputs=[ | |
| gr.Image(type="pil", label="Detected Output"), | |
| gr.Textbox(label="Detected Text (Bangla)"), | |
| gr.Textbox(label="Confidence (%)") | |
| ], | |
| title="YOLOv10n License Plate Detector (Bangla)", | |
| description="Upload an image or video. Detects license plates and extracts Bangla text using OCR." | |
| ) | |
| interface.launch() | |