import cv2 import torch from PIL import Image, ImageDraw import gradio as gr import pandas as pd from transformers import pipeline model = torch.hub.load('ultralytics/yolov5', 'yolov5s') translator = pipeline("translation_en_to_ar", model="Helsinki-NLP/opus-mt-en-ar") def detect_and_draw_image(input_image): results = model(input_image) detections = results.xyxy[0].numpy() draw = ImageDraw.Draw(input_image) counts = {} for detection in detections: xmin, ymin, xmax, ymax, conf, class_id = detection label = model.names[int(class_id)] counts[label] = counts.get(label, 0) + 1 draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=2) draw.text((xmin, ymin), f"{label}: {conf:.2f}", fill="white") translated_labels = translator(list(counts.keys())) df = pd.DataFrame({ 'Label (English)': list(counts.keys()), 'Label (Arabic)': [t['translation_text'] for t in translated_labels], 'Object Count': list(counts.values()) }) return input_image, df def detect_and_draw_video(video_path): cap = cv2.VideoCapture(video_path) frames = [] overall_counts = {} while cap.isOpened(): ret, frame = cap.read() if not ret: break frame = cv2.resize(frame, (640, 480)) results = model(frame) detections = results.xyxy[0].numpy() for detection in detections: xmin, ymin, xmax, ymax, conf, class_id = detection label = model.names[int(class_id)] overall_counts[label] = overall_counts.get(label, 0) + 1 cv2.rectangle(frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 0, 0), 2) cv2.putText(frame, f"{label}: {conf:.2f}", (int(xmin), int(ymin) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) frames.append(frame) cap.release() output_path = 'output.mp4' out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), 20.0, (640, 480)) for frame in frames: out.write(frame) out.release() translated_labels = translator(list(overall_counts.keys())) df = pd.DataFrame({ 'Label (English)': list(overall_counts.keys()), 'Label (Arabic)': [t['translation_text'] for t in translated_labels], 'Object Count': list(overall_counts.values()) }) return output_path, df image_interface = gr.Interface( fn=detect_and_draw_image, inputs=gr.Image(type="pil", label="Upload Image"), outputs=[gr.Image(type="pil"), gr.Dataframe(label="Object Counts")], title="Object Detection for Images", description="Upload an image to see the objects detected and their counts." ) video_interface = gr.Interface( fn=detect_and_draw_video, inputs=gr.Video(label="Upload Video"), outputs=[gr.Video(label="Processed Video"), gr.Dataframe(label="Object Counts")], title="Object Detection for Videos", description="Upload a video to see the objects detected and their counts." ) app = gr.TabbedInterface([image_interface, video_interface], ["Image Detection", "Video Detection"]) app.launch(debug=True)