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| from ultralytics import YOLOv10 as YOLO | |
| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import settings | |
| import matplotlib.pyplot as plt | |
| def load_model(model_path): | |
| model = YOLO(model_path) | |
| return model | |
| def _display_detected_frames(conf, model, st_frame, image): | |
| if isinstance(image, dict): | |
| st.error("Invalid image format: 'dict' object received.") | |
| return | |
| # Convert image to RGB format for processing with OpenCV | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| results = model(image_rgb, conf=conf) | |
| # Get bounding boxes, labels, and confidences | |
| boxes = results[0].boxes.xyxy.cpu().numpy() | |
| labels = results[0].boxes.cls.cpu().numpy() | |
| confidences = results[0].boxes.conf.cpu().numpy() | |
| # Category dictionary | |
| category_dict = { | |
| 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', | |
| 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', | |
| 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', | |
| 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', | |
| 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', | |
| 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', | |
| 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', | |
| 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', | |
| 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', | |
| 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', | |
| 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', | |
| 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', | |
| 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', | |
| 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', | |
| 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', | |
| 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' | |
| } | |
| # Initialize colors | |
| num_classes = len(category_dict) | |
| colors = plt.cm.get_cmap('hsv', num_classes) | |
| # Prepare annotations | |
| for box, label, confidence in zip(boxes, labels, confidences): | |
| x1, y1, x2, y2 = box.astype(int) | |
| label_name = category_dict[int(label)] | |
| confidence_text = f"{label_name} {confidence:.2f}" | |
| class_color = colors(int(label) / num_classes)[:3] | |
| class_color = [int(c * 255) for c in class_color] | |
| # Draw bounding boxes and labels on the image | |
| cv2.rectangle(image, (x1, y1), (x2, y2), class_color, 2) | |
| font_scale = 1.0 | |
| thickness = 2 | |
| (text_width, text_height), baseline = cv2.getTextSize(confidence_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness) | |
| cv2.rectangle(image, (x1, y1 - text_height - 10), (x1 + text_width, y1), class_color, -1) | |
| cv2.putText(image, confidence_text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness) | |
| # Display the annotated image in Streamlit | |
| st_frame.image(image, caption='Detected Image', channels="RGB", use_column_width=True) | |
| def play_stored_video(conf, model): | |
| source_vid = st.sidebar.selectbox("Choose a video...", settings.VIDEOS_DICT.keys()) | |
| if st.sidebar.button('Detect Video Objects'): | |
| try: | |
| vid_cap = cv2.VideoCapture(str(settings.VIDEOS_DICT.get(source_vid))) | |
| st_frame = st.empty() | |
| while vid_cap.isOpened(): | |
| success, image = vid_cap.read() | |
| if success: | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB | |
| _display_detected_frames(conf, model, st_frame, image_rgb) | |
| else: | |
| vid_cap.release() | |
| break | |
| except Exception as e: | |
| st.sidebar.error("Error loading video: " + str(e)) | |