falldetect-赏金猎人

2 老年人的远程智能诊疗/falldetect-赏金猎人/Dockerfile

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+FROM openvino/ubuntu18_dev:2020.1
+USER root
+ADD sources.list /etc/apt
+
+
+
+
+RUN pip3 install  -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip
+RUN pip3 install  -i https://pypi.tuna.tsinghua.edu.cn/simple numpy exitstatus opencv-python
+
+RUN apt update && apt install -y libsm6 libxext6 sudo libpython3.6
+
+RUN mkdir /app
+VOLUME /app
+
+CMD ["/bin/bash"]

2 老年人的远程智能诊疗/falldetect-赏金猎人/README.md

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+# 跌倒检测
+本项目使用 OpenVINO toolkit 的人体姿势预训练模型 [human-pose-estimation-0001](https://docs.openvinotoolkit.org/latest/_models_intel_human_pose_estimation_0001_description_human_pose_estimation_0001.html) 进行跌倒检测
+
+# 应用场景
+- 老年人跌倒检测。
+
+# 检测原理
+- 使用 opencv 从摄像头、视频文件读取每一帧视频，并判断 头、颈部、肩膀的位置
+- 对比每一帧视频，当发现位置为水平时判定为跌倒
+- 将判定跌倒的视频帧标注，并显示或输出为视频
+
+# 使用技术
+- ECS、linux、python、opencv 等
+
+# 使用 docker 编译镜像
+```
+docker build -t falldetect .
+docker run -it --rm -v "$PWD:/app" falldetect
+```
+
+# 运行
+```
+# 首先初始化环境
+cd /opt/intel/openvino
+source bin/setupvars.sh
+
+# 确认环境变量已经设置了 openvino 路径
+echo $PYTHONPATH 
+
+# 执行代码
+cd /app
+python3 fall_detection.py -i example/demo.mp4 
+```
+
+# 团队介绍
+- 邱本超	[email protected]
+- 黄凯	 [email protected]
+- 李伟	 [email protected]
+- 孙涛	 [email protected]
+
+# 项目示例
+- 请参考example中的示例视频
+
+

2 老年人的远程智能诊疗/falldetect-赏金猎人/build.bat

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+docker build -t falldetect .

2 老年人的远程智能诊疗/falldetect-赏金猎人/build.sh

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+docker build -t falldetect .

2 老年人的远程智能诊疗/falldetect-赏金猎人/fall_detection.py

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+import argparse
+import logging as log
+import os
+import sys
+
+import cv2
+from exitstatus import ExitStatus
+
+from openvino.inference_engine import IENetwork, IEPlugin
+
+JOINT_COLORS = [
+    (0, 0, 255),
+    (0, 0, 128), (255, 255, 255), (0, 255, 0), (0, 0, 255),
+    (192, 192, 192), (128, 0, 255), (0, 128, 128), (255, 255, 255),
+    (128, 128, 0), (128, 128, 128), (0, 255, 0), (0, 0, 255),
+    (255, 255, 0), (255, 0, 255), (0, 255, 255), (0, 0, 128)
+]
+
+"""
+Pose Data Points
+
+Nose 0, Neck 1, Right Shoulder 2, Right Elbow 3, Right Wrist 4,
+Left Shoulder 5, Left Elbow 6, Left Wrist 7, Right Hip 8,
+Right Knee 9, Right Ankle 10, Left Hip 11, Left Knee 12,
+LAnkle 13, Right Eye 14, Left Eye 15, Right Ear 16,
+Left Ear 17, Background 18
+"""
+POSE_POINTS_NUMBER = 18
+POSE_PAIRS = [
+    [1, 0], [1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7],
+    [1, 8], [8, 9], [9, 10], [1, 11], [11, 12], [12, 13],
+    [0, 14], [0, 15], [14, 16], [15, 17]
+]
+
+
+def parse_args(parse_this=None) -> argparse.Namespace:
+    """Parse user command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="从摄像头或视频文件检测人体跌倒"
+    )
+    parser.add_argument(
+        "-i",
+        "--input",
+        help="""
+            视频文件或 cam . 'cam' for capturing
+            video stream from internal camera.
+        """,
+        required=True,
+        type=str
+    )
+    parser.add_argument(
+        "-mp",
+        "--model_precision",
+        help="""The precision of the human pose model.
+            Default is 32-bit integer.
+        """,
+        choices=('FP16', 'FP32'),
+        default='FP32'
+    )
+    parser.add_argument(
+        "-l",
+        "--cpu_extension",
+        help="""MKLDNN (CPU)-targeted custom layers.Absolute path to a shared
+            library with the kernels impl.
+        """,
+        type=str,
+        default=None
+    )
+    parser.add_argument(
+        "-pp",
+        "--plugin_dir",
+        help="Path to a plugin folder",
+        type=str,
+        default=None
+    )
+    parser.add_argument(
+        "-d",
+        "--device",
+        help="""Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD
+            is acceptable. Demo will look for a suitable plugin for device
+            specified (CPU by default)
+        """,
+        default="CPU",
+        type=str
+    )
+    return parser.parse_args(parse_this)
+
+
+def main():
+    """Accept arguments and perform the inference on entry"""
+    # 配置 Log
+    log.basicConfig(
+        format="[ %(levelname)s ] %(message)s",
+        level=log.INFO,
+        stream=sys.stdout
+    )
+
+    # 解析命令行参数
+    args = parse_args()
+    log.info("Start")
+
+    # Plugin initialization for specified device
+    log.info("Initializing plugin for {} device...".format(args.device))
+    plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
+    if args.cpu_extension and 'CPU' in args.device:
+        plugin.add_cpu_extension(args.cpu_extension)
+
+    # Load model
+    model_xml = os.path.join(
+        os.getcwd(),
+        "models",
+        "human-pose-estimation-0001",
+        args.model_precision,
+        "human-pose-estimation-0001.xml"
+    )
+    model_bin = os.path.splitext(model_xml)[0] + ".bin"
+    log.info("Reading IR...")
+    net = IENetwork(model=model_xml, weights=model_bin)
+
+    if plugin.device == "CPU":
+        supported_layers = plugin.get_supported_layers(net)
+        not_supported_layers = [
+            l for l in net.layers.keys() if l not in supported_layers
+        ]
+        if len(not_supported_layers) != 0:
+            log.error(
+                """Following layers are not supported by the plugin
+                for specified device {}:\n {}""".
+                format(plugin.device, ', '.join(not_supported_layers))
+            )
+            log.error(
+                """Please try to specify cpu extensions library
+                    path in demo's command line parameters using -l
+                    or --cpu_extension command line argument
+                """
+            )
+            sys.exit(1)
+    input_blob = next(iter(net.inputs))
+
+    log.info("Loading IR to the plugin...")
+    exec_net = plugin.load(network=net, num_requests=2)
+
+    # Read and pre-process input image
+    n, c, h, w = net.inputs[input_blob].shape
+    del net
+    if args.input == 'cam':
+        input_stream = 0
+    else:
+        input_stream = args.input
+        assert os.path.isfile(args.input), "Specified input file doesn't exist"
+
+    cap = cv2.VideoCapture(input_stream)
+    # Grab the shape of the input
+    width = int(cap.get(3))
+    height = int(cap.get(4))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    font_scale = round(height/360)
+    font_thickness = round(3*font_scale)
+
+    cur_request_id = 0
+    next_request_id = 1
+
+    # Fall Detection variables
+    previous_head_avg_position = 0
+    previous_head_detection_frame = 0
+    last_fall_detected_frame = 0
+    # Fall Detection threshold speed is depedent of the frame height
+    fall_threshold = 0.04 * height
+    framerate_threshold = round(fps/5.0)
+    fall_detected_text_position = (20, round(0.15*height))
+
+    ret, frame = cap.read()
+    frame_number = 0
+
+    out_file = None
+    if args.input != 'cam':
+        out_filename = os.path.splitext(input_stream)[0] + '_output.mp4'
+        out_file = cv2.VideoWriter(
+            out_filename,
+            cv2.VideoWriter_fourcc(*"mp4v"),
+            fps,
+            (width, height)
+        )
+        log.info("Evaluating video file stream...")
+    else:
+        log.info("Evaluating webcam stream...")
+
+    while cap.isOpened():
+        ret, next_frame = cap.read()
+        if not ret:
+            break
+
+        # Pre-process inputs
+        in_frame = cv2.resize(next_frame, (w, h))
+        in_frame = in_frame.transpose((2, 0, 1))
+        in_frame = in_frame.reshape((n, c, h, w))
+
+        # Inference
+        exec_net.start_async(
+            request_id=next_request_id,
+            inputs={input_blob: in_frame}
+        )
+        if exec_net.requests[cur_request_id].wait(-1) == 0:
+            # Parse detection results of the current request
+            res = exec_net.requests[cur_request_id].outputs
+            kp_heatmaps = res['Mconv7_stage2_L2']
+
+            threshold = 0.5
+            points = []
+            head_elements_y_pos = []
+
+            for i in range(POSE_POINTS_NUMBER):
+                # confidence map of corresponding body's part.
+                probMap = kp_heatmaps[0, i, :, :]
+
+                # Find global maxima of the probMap.
+                minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
+
+                # Scale the point to fit on the original image
+                x = frame.shape[1] / probMap.shape[1] * point[0]
+                y = frame.shape[0] / probMap.shape[0] * point[1]
+
+                # Add point if the probability is greater than the threshold
+                if prob > threshold:
+                    point = (int(x), int(y))
+
+                    # If point is a component of the head (including neck and
+                    # sholders) append to the header elemnts
+                    if (
+                        i == 0 or
+                        i == 1 or
+                        i == 2 or
+                        i == 5 or
+                        i == 14 or
+                        i == 15 or
+                        i == 16 or
+                        i == 17
+                    ):
+                        head_elements_y_pos.append(point[1])
+
+                    points.append(point)
+                else:
+                    points.append(None)
+
+            # Draw Skeleton
+            for num, pair in enumerate(POSE_PAIRS):
+                partA = pair[0]
+                partB = pair[1]
+                if points[partA] and points[partB]:
+                    cv2.line(
+                        frame,
+                        points[partA],
+                        points[partB],
+                        JOINT_COLORS[num],
+                        3
+                    )
+
+            # Calculate head average position from its components
+            if(len(head_elements_y_pos) > 0):
+                head_avg_position = sum(head_elements_y_pos)
+                head_avg_position /= len(head_elements_y_pos)
+                # log.info(head_avg_position)
+
+                # Compare previous head position
+                # to detect if falling
+                if (
+                    previous_head_detection_frame and
+                    (head_avg_position - previous_head_avg_position) >
+                        fall_threshold and
+                    (frame_number - previous_head_detection_frame) <
+                        framerate_threshold
+                ):
+                    # print("Fall detected.")
+                    last_fall_detected_frame = frame_number
+
+                previous_head_avg_position = head_avg_position
+                previous_head_detection_frame = frame_number
+
+            # Draw Fall Detection Text if last fall event
+            # ocurred max 2 seconds ago
+            if (
+                last_fall_detected_frame and
+                (frame_number - last_fall_detected_frame) <= 2*fps
+            ):
+                cv2.putText(
+                    frame,
+                    "FALL DETECTED!",
+                    fall_detected_text_position,
+                    cv2.FONT_HERSHEY_COMPLEX, font_scale,
+                    (0, 0, 255),
+                    font_thickness,
+                    cv2.LINE_AA
+                )
+
+        # If webcam mode
+        if out_file:
+            out_file.write(frame)
+        else:
+            cv2.imshow("Detection Results", frame)
+
+        cur_request_id, next_request_id = next_request_id, cur_request_id
+        frame = next_frame
+
+        # Increment frame number
+        frame_number += 1
+
+        key = cv2.waitKey(1)
+        if key == 27:
+            break
+
+    if out_file:
+        # Release the out writer, capture, and destroy any OpenCV windows
+        out_file.release()
+        out_filename = os.path.splitext(input_stream)[0] + '_output.mp4'
+        log.info("Finished. %s saved." % (out_filename))
+    else:
+        log.info("Finished.")
+        cv2.destroyAllWindows()
+
+    return ExitStatus.success
+
+if __name__ == '__main__':
+    sys.exit(main())