Redmi Buds 4開箱簡評：才199還有降噪 可以無腦入
在上個月舉辦的Redmi Note11T Pro系列新機發布會上，除了兩款手機新品之外，Redmi還帶來了兩款TWS真無線藍牙耳機產品，Redmi Buds 4和Redmi Buds 4 Pro，此前我們在Redmi Note11T
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.www897cc.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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