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| import cv2 | |
| import numpy as np | |
| import datetime | |
| import gradio as gr | |
| # Ensure these files are available in the Hugging Face Space working directory | |
| net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") | |
| layer_names = net.getLayerNames() | |
| output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()] | |
| classes = [] | |
| with open("coco.names", "r") as f: | |
| classes = [line.strip() for line in f.readlines()] | |
| def detect_objects(image): | |
| height, width, channels = image.shape | |
| blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False) | |
| net.setInput(blob) | |
| outs = net.forward(output_layers) | |
| class_ids = [] | |
| confidences = [] | |
| boxes = [] | |
| for out in outs: | |
| for detection in out: | |
| scores = detection[5:] | |
| class_id = np.argmax(scores) | |
| confidence = scores[class_id] | |
| if confidence > 0.5: | |
| center_x = int(detection[0] * width) | |
| center_y = int(detection[1] * height) | |
| w = int(detection[2] * width) | |
| h = int(detection[3] * height) | |
| x = int(center_x - w / 2) | |
| y = int(center_y - h / 2) | |
| boxes.append([x, y, w, h]) | |
| confidences.append(float(confidence)) | |
| class_ids.append(class_id) | |
| indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) | |
| return [(boxes[i], class_ids[i], confidences[i]) for i in range(len(boxes)) if i in indexes] | |
| def process_image(image): | |
| image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | |
| detections = detect_objects(image) | |
| for (box, class_id, confidence) in detections: | |
| x, y, w, h = box | |
| label = str(classes[class_id]) | |
| color = (0, 255, 0) if label == "person" else (0, 0, 255) | |
| cv2.rectangle(image, (x, y), (x + w, y + h), color, 2) | |
| cv2.putText(image, f'{label} {confidence:.2f}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2) | |
| return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| def capture_and_process(): | |
| cap = cv2.VideoCapture(0) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| processed_frame = process_image(frame) | |
| yield processed_frame | |
| cap.release() | |
| # Define Gradio interface | |
| with gr.Blocks() as iface: | |
| gr.Markdown("# YOLO Object Detection") | |
| gr.Markdown("## Real-time object detection using YOLO") | |
| with gr.Tab("Live Camera Feed"): | |
| gr.Markdown("Press the button to start the live camera feed with real-time object detection.") | |
| live_output = gr.Image(type="numpy", label="Live Camera Feed") | |
| gr.Button("Start Live Camera").click(capture_and_process, outputs=live_output) | |
| with gr.Tab("Upload Image"): | |
| gr.Markdown("Upload an image and the YOLO model will detect objects in the image, highlighting humans.") | |
| image_input = gr.Image(type="numpy", label="Upload an image") | |
| image_output = gr.Image(type="numpy", label="Detected objects") | |
| image_input.upload(process_image, inputs=image_input, outputs=image_output) | |
| # Launch Gradio interface | |
| iface.launch() | |