import torch import numpy as np import gradio as gr import cv2 import time import os from pathlib import Path # Create cache directory for models os.makedirs("models", exist_ok=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Load YOLOv5 Model model_path = Path("models/yolov5n.pt") if model_path.exists(): print(f"Loading model from cache: {model_path}") model = torch.hub.load("ultralytics/yolov5", "custom", path=str(model_path), source="local").to(device) else: print("Downloading YOLOv5n model and caching...") model = torch.hub.load("ultralytics/yolov5", "yolov5n", pretrained=True).to(device) torch.save(model.state_dict(), model_path) # Configure model model.conf = 0.5 model.iou = 0.5 model.classes = None if device.type == "cuda": model.half() else: torch.set_num_threads(os.cpu_count()) model.eval() # Generate colors for bounding boxes np.random.seed(42) colors = np.random.uniform(0, 255, size=(len(model.names), 3)) def detect_objects(image): if image is None: return None output_image = image.copy() results = model(image, size=640) detections = results.pred[0].cpu().numpy() for *xyxy, conf, cls in detections: x1, y1, x2, y2 = map(int, xyxy) class_id = int(cls) color = colors[class_id].tolist() cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 3, lineType=cv2.LINE_AA) label = f"{model.names[class_id]} {conf:.2f}" cv2.putText(output_image, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2) return output_image def process_video(video_path): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return "Error: Could not open video file." frame_width = int(cap.get(3)) frame_height = int(cap.get(4)) fps = cap.get(cv2.CAP_PROP_FPS) fourcc = cv2.VideoWriter_fourcc(*'mp4v') output_path = "output_video.mp4" out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height)) while cap.isOpened(): ret, frame = cap.read() if not ret: break img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) results = model(img, size=640) detections = results.pred[0].cpu().numpy() for *xyxy, conf, cls in detections: x1, y1, x2, y2 = map(int, xyxy) class_id = int(cls) color = colors[class_id].tolist() cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3, lineType=cv2.LINE_AA) label = f"{model.names[class_id]} {conf:.2f}" cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2) out.write(frame) cap.release() out.release() return output_path # Gradio Interface with gr.Blocks(title="YOLOv5 Object Detection") as demo: gr.Markdown("# YOLOv5 Object Detection (Image & Video)") with gr.Tab("Image Detection"): img_input = gr.Image(label="Upload Image", type="numpy") img_output = gr.Image(label="Detected Objects", type="numpy") img_submit = gr.Button("Detect Objects") img_submit.click(fn=detect_objects, inputs=img_input, outputs=img_output) with gr.Tab("Video Detection"): vid_input = gr.Video(label="Upload Video") vid_output = gr.Video(label="Processed Video") vid_submit = gr.Button("Process Video") vid_submit.click(fn=process_video, inputs=vid_input, outputs=vid_output) demo.launch()