石頭智能洗地機A10 Plus體驗：雙向自清潔治好了我的懶癌
一、前言和介紹專為家庭請假懶人而生的石頭科技在近日又帶來了自己的全新旗艦新品，石頭智能洗地機A10 Plus。從這個產品名上就不難看出，這次石頭推出的并不是常見的掃地機器
目標檢測：目標檢測是指在圖像或視頻幀中識別和定位特定目標，并使用邊界框來確定它們的位置。YOLO（You Only Look Once）是一種高效的單階段目標檢測算法，以其快速的處理速度和較高的準確性而聞名。與傳統的兩階段檢測算法相比，YOLO的優勢在于它能夠一次性處理整個圖像，從而實現實時目標檢測，這在自動駕駛、視頻監控和機器人導航等應用中尤為重要。
目標跟蹤：目標跟蹤則關注于在視頻序列中連續跟蹤已識別的目標。SORT（Simple Online and Realtime Tracking）算法因其簡單性和實時性而被廣泛應用于目標跟蹤任務。其通過預測目標的運動軌跡并實時更新其位置，有效實現了目標的連續跟蹤。結合YOLO進行檢測和SORT進行跟蹤，可以實現目標的連續監控和分析，確保在整個視頻序列中的準確和一致的跟蹤。項目 我們將使用YOLOv8m（中等版本）、OpenCV和SORT進行目標檢測，以確保準確性和效率，來計算通過我們視頻中特定區域的車輛數量。
本項目旨在通過結合使用YOLOv8m（一種中等復雜度的YOLO變體）、OpenCV（一個開源的計算機視覺庫）和SORT算法，實現對視頻中特定區域內通過的車輛數量的準確計算。這一過程不僅確保了目標檢測的準確性，也提高了整個系統的效率。
 video = cv2.VideoCapture('traffic.mp4')width = 1280height = 720line_left_road_x1 = 256line_left_road_x2 = 500line_left_road_y = 472line_right_road_x1 = 672line_right_road_x2 = 904line_right_road_y = 472vehicle_left_road_id_count = []vehicle_right_road_id_count = []while True: success, frame = video.read() if not success: break frame = cv2.resize(frame, (width, height)) image_region = cv2.bitwise_and(frame, mask) results = model(image_region, stream=True) detections = [] cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 0, 255)) cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 0, 255)) for result in results: for box in result.boxes: class_name = class_names[int(box.cls[0])] if not class_name in class_names_goal: continue confidence = round(float(box.conf[0]) * 100, 2) if confidence < 30: continue x1, y1, x2, y2 = box.xyxy[0] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) detections.append([x1, y1, x2, y2, float(box.conf[0])]) tracked_objects = tracker.update(np.array(detections)) for obj in tracked_objects: x1, y1, x2, y2, obj_id = [int(i) for i in obj] confidence_pos_x1 = max(0, x1) confidence_pos_y1 = max(36, y1) cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 2) cvzone.putTextRect(frame, f'ID: {obj_id}', (confidence_pos_x1, confidence_pos_y1), 1, 1) center_x = (x1 + x2) // 2 center_y = (y1 + y2) // 2 if line_left_road_y - 10 < center_y < line_left_road_y + 10 and line_left_road_x1 < center_x < line_left_road_x2: if not obj_id in vehicle_left_road_id_count: vehicle_left_road_id_count.append(obj_id) cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 255, 0), 2) if line_right_road_y - 10 < center_y < line_right_road_y + 10 and line_right_road_x1 < center_x < line_right_road_x2: if not obj_id in vehicle_right_road_id_count: vehicle_right_road_id_count.append(obj_id) cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 255, 0), 2) cvzone.putTextRect(frame, f'Car Left Road Count: {len(vehicle_left_road_id_count)}', (50, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31)) cvzone.putTextRect(frame, f'Car Right Road Count: {len(vehicle_right_road_id_count)}', (width - 460, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31)) cv2.imshow('Image', frame) if cv2.waitKey(1) & 0xFF == ord('q'): breakvideo.release()cv2.destroyAllWindows()</code></pre><h4>6. 結果</h4><p style=)
源碼：https://github.com/VladeMelo/collaborative-filtering
本文鏈接：http://www.tebozhan.com/showinfo-26-112706-0.html一個簡單的車輛目標檢測和跟蹤示例
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