7月安卓手機(jī)好評(píng)榜：三星S23Ultra好評(píng)率第一
性能榜和性價(jià)比榜之后，我們來(lái)看最后的安卓手機(jī)好評(píng)榜，數(shù)據(jù)來(lái)源安兔兔評(píng)測(cè)，收集時(shí)間2023年7月1日至7月31日，僅限國(guó)內(nèi)市場(chǎng)。第一名：三星Galaxy S23 Ultra好評(píng)率：95.71%在即將迎來(lái)新
目標(biāo)檢測(cè)：目標(biāo)檢測(cè)是指在圖像或視頻幀中識(shí)別和定位特定目標(biāo)，并使用邊界框來(lái)確定它們的位置。YOLO（You Only Look Once）是一種高效的單階段目標(biāo)檢測(cè)算法，以其快速的處理速度和較高的準(zhǔn)確性而聞名。與傳統(tǒng)的兩階段檢測(cè)算法相比，YOLO的優(yōu)勢(shì)在于它能夠一次性處理整個(gè)圖像，從而實(shí)現(xiàn)實(shí)時(shí)目標(biāo)檢測(cè)，這在自動(dòng)駕駛、視頻監(jiān)控和機(jī)器人導(dǎo)航等應(yīng)用中尤為重要。
目標(biāo)跟蹤：目標(biāo)跟蹤則關(guān)注于在視頻序列中連續(xù)跟蹤已識(shí)別的目標(biāo)。SORT（Simple Online and Realtime Tracking）算法因其簡(jiǎn)單性和實(shí)時(shí)性而被廣泛應(yīng)用于目標(biāo)跟蹤任務(wù)。其通過(guò)預(yù)測(cè)目標(biāo)的運(yùn)動(dòng)軌跡并實(shí)時(shí)更新其位置，有效實(shí)現(xiàn)了目標(biāo)的連續(xù)跟蹤。結(jié)合YOLO進(jìn)行檢測(cè)和SORT進(jìn)行跟蹤，可以實(shí)現(xiàn)目標(biāo)的連續(xù)監(jiān)控和分析，確保在整個(gè)視頻序列中的準(zhǔn)確和一致的跟蹤。項(xiàng)目 我們將使用YOLOv8m（中等版本）、OpenCV和SORT進(jìn)行目標(biāo)檢測(cè)，以確保準(zhǔn)確性和效率，來(lái)計(jì)算通過(guò)我們視頻中特定區(qū)域的車(chē)輛數(shù)量。
本項(xiàng)目旨在通過(guò)結(jié)合使用YOLOv8m（一種中等復(fù)雜度的YOLO變體）、OpenCV（一個(gè)開(kāi)源的計(jì)算機(jī)視覺(jué)庫(kù)）和SORT算法，實(shí)現(xiàn)對(duì)視頻中特定區(qū)域內(nèi)通過(guò)的車(chē)輛數(shù)量的準(zhǔn)確計(jì)算。這一過(guò)程不僅確保了目標(biāo)檢測(cè)的準(zhǔn)確性，也提高了整個(gè)系統(tǒng)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(jié)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(gè)簡(jiǎn)單的車(chē)輛目標(biāo)檢測(cè)和跟蹤示例
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