171 lines
4.1 KiB
Python
171 lines
4.1 KiB
Python
# YOLOv8
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from ultralytics import YOLO
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# supervision
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import supervision as sv
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from supervision.draw.color import Color
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# OpenCV
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import cv2
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# Line Selector
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from line_selector import LineSelector
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# time date
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from datetime import datetime
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import time
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# 视频源
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source = "line.mp4"
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source = "rtmp://10.0.0.21:1935/live/picam3"
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# 读取背景图像
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cap = cv2.VideoCapture(source)
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fps = cap.get(cv2.CAP_PROP_FPS)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# 输出视频
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########## 配置是否录制 ##########
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record = True
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if record:
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output = "output2.mp4"
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out = cv2.VideoWriter(output, cv2.VideoWriter_fourcc(*"avc1"), fps / 2, (width, height))
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out.set(cv2.VIDEOWRITER_PROP_QUALITY, 80)
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# 读取背景图像
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ret, frame = cap.read()
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if ret:
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cv2.imwrite("background.png", frame)
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selector = LineSelector("background.png")
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selector.draw_image_and_get_coordinates()
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if selector.success:
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coordinates = selector.get_coordinates()
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print("线段坐标: ", coordinates)
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else:
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print("未选择线段坐标")
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cap.release()
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exit(0)
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cap.release()
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# 加载目标检测模型
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print("正在加载模型...")
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model = YOLO("yolov8x.pt")
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# 越线检测位置
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LINE_START = sv.Point(coordinates[0], coordinates[1])
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LINE_END = sv.Point(coordinates[2], coordinates[3])
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line_counter = sv.LineZone(start=LINE_START, end=LINE_END)
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# 线的可视化配置
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line_color = Color(r=224, g=57, b=151)
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line_annotator = sv.LineZoneAnnotator(
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thickness=2, text_thickness=2, text_scale=1, color=line_color, text_offset=2.0, custom_in_text="North", custom_out_text="South"
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)
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# 目标检测可视化配置
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box_annotator = sv.BoxAnnotator(thickness=2, text_thickness=2, text_scale=1)
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# 起始时间
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start_time = time.perf_counter()
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# 限定帧数
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########## 配置是否限定帧数 ##########
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frame_limit = False
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frame_limit_upper = 43000
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frame_count = 0
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# 逐帧跟踪
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for result in model.track(source, device=0, verbose=False, stream=True):
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# 限定帧数
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frame_count += 1
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if frame_count == frame_limit_upper and frame_limit:
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print(f"已经到达{frame_limit_upper}帧，停止运行")
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break
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# 获取原始图像
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frame = result.orig_img
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# 用 supervision 解析预测结果
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detections = sv.Detections.from_ultralytics(result)
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## 过滤掉某些类别
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# detections = detections[(detections.class_id != 60) & (detections.class_id != 0)]
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# 解析追踪ID
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if result.boxes.id is None:
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continue
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detections.tracker_id = result.boxes.id.numpy().astype(int)
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# 获取每个目标的：追踪ID、类别名称、置信度
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class_ids = detections.class_id # 类别ID
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confidences = detections.confidence # 置信度
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tracker_ids = detections.tracker_id # 多目标追踪ID
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labels = [
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"#{} {} {:.1f}".format(
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tracker_ids[i], model.names[class_ids[i]], confidences[i] * 100
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)
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for i in range(len(class_ids))
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]
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# 绘制目标检测可视化结果
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frame = box_annotator.annotate(scene=frame, detections=detections, labels=labels)
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# 越线检测
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line_counter.trigger(detections=detections)
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line_annotator.annotate(frame=frame, line_counter=line_counter)
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# 显示日期
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cv2.putText(
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frame,
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datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(255, 255, 255),
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2,
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cv2.LINE_AA,
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)
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# 结束时间
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end_time = time.perf_counter()
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# 计算帧率
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fps = 1.0 / (end_time - start_time)
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# 显示帧率
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cv2.putText(
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frame,
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"FPS: {:.2f}".format(fps),
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(10, 60),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(255, 255, 255),
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2,
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cv2.LINE_AA,
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)
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# 保存视频
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if record:
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out.write(frame)
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# 显示结果
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cv2.imshow("Frame", frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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# 起始时间
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start_time = time.perf_counter()
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# 释放资源
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if record:
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out.release()
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cv2.destroyAllWindows()
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