Spaces:
Running
Running
| 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.", | |
| gr.Examples(['assets/MessiVsAlhilal.jpg', 'assets/Manhattan002_0.webp'], inputs=input_image) | |
| ) | |
| 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) | |