最终成功果展示

最终成功果展示

import time
from collections import deque
import requests
import cv2
import numpy as np
import mediapipe as mp

from stgcn.stgcn import STGCN
from PIL import Image, ImageDraw, ImageFont
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
KEY_JOINTS = [
mp_pose.PoseLandmark.NOSE,
mp_pose.PoseLandmark.LEFT_SHOULDER,
mp_pose.PoseLandmark.RIGHT_SHOULDER,
mp_pose.PoseLandmark.LEFT_ELBOW,
mp_pose.PoseLandmark.RIGHT_ELBOW,
mp_pose.PoseLandmark.LEFT_WRIST,
mp_pose.PoseLandmark.RIGHT_WRIST,
mp_pose.PoseLandmark.LEFT_HIP,
mp_pose.PoseLandmark.RIGHT_HIP,
mp_pose.PoseLandmark.LEFT_KNEE,
mp_pose.PoseLandmark.RIGHT_KNEE,
mp_pose.PoseLandmark.LEFT_ANKLE,
mp_pose.PoseLandmark.RIGHT_ANKLE
]

POSE_CONNECTIONS = [(6, 4), (4, 2), (2, 13), (13, 1), (5, 3), (3, 1), (12, 10),
(10, 8), (8, 2), (11, 9), (9, 7), (7, 1), (13, 0)]

POINT_COLORS = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), (0, 255, 255)] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck

LINE_COLORS = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50), (77, 255, 222),
(77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77), (77, 222, 255),
(255, 156, 127), (0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]

ACTION_MODEL_MAX_FRAMES = 30
class FallDetection:
def init(self):
self.isOk = True
self.action_model = STGCN(weight_file='./weights/tsstg-model.pth', device='cpu')
self.joints_list = deque(maxlen=ACTION_MODEL_MAX_FRAMES)

def draw_skeleton(self, frame, pts):
l_pair = POSE_CONNECTIONS
p_color = POINT_COLORS

line_color = LINE_COLORS

part_line = {}
pts = np.concatenate((pts, np.expand_dims((pts[1, :] + pts[2, :]) / 2, 0)), axis=0)
for n in range(pts.shape[0]):
    if pts[n, 2] <= 0.05:
        continue
    cor_x, cor_y = int(pts[n, 0]), int(pts[n, 1])
    part_line[n] = (cor_x, cor_y)
    cv2.circle(frame, (cor_x, cor_y), 3, p_color[n], -1)
    # cv2.putText(frame, str(n), (cor_x+10, cor_y+10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1)

for i, (start_p, end_p) in enumerate(l_pair):
    if start_p in part_line and end_p in part_line:
        start_xy = part_line[start_p]
        end_xy = part_line[end_p]
        cv2.line(frame, start_xy, end_xy, line_color[i], int(1*(pts[start_p, 2] + pts[end_p, 2]) + 3))
return frame

def cv2_add_chinese_text(self, img, text, position, textColor=(0, 255, 0), textSize=30):
if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 创建一个可以在给定图像上绘图的对象
draw = ImageDraw.Draw(img)
# 字体的格式
fontStyle = ImageFont.truetype(
"./fonts/MSYH.ttc", textSize, encoding="utf-8")
# 绘制文本
draw.text(position, text, textColor, font=fontStyle)
# 转换回OpenCV格式
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

def detect(self):
# Initialize the webcam capture.
cap = cv2.VideoCapture(0) # 使用0表示默认摄像头
# cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800) # 解决问题的关键！！！
# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 600)
# cap.set(cv2.CAP_PROP_FPS, 30)

image_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
image_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_num = 0
print(image_h, image_w)

with mp_pose.Pose(
        min_detection_confidence=0.7,
        min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        fps_time = time.time()
        frame_num += 1
        success, image = cap.read()
        if not success:
            print("Ignoring empty camera frame.")
            # 如果是实时摄像头，不需要使用'continue'
            continue

        # 提高性能
        image.flags.writeable = False
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        results = pose.process(image)

        if not results.pose_landmarks:
            continue

        # 识别骨骼点
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        # mp_drawing.draw_landmarks(
        #     image,
        #     results.pose_landmarks,
        #     mp_pose.POSE_CONNECTIONS,
        #     landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())

        landmarks = results.pose_landmarks.landmark
        joints = np.array([[landmarks[joint].x * image_w,
                            landmarks[joint].y * image_h,
                            landmarks[joint].visibility]
                           for joint in KEY_JOINTS])
        # 人体框
        box_l, box_r = int(joints[:, 0].min())-50, int(joints[:, 0].max())+50
        box_t, box_b = int(joints[:, 1].min())-100, int(joints[:, 1].max())+100

        self.joints_list.append(joints)

        # 识别动作
        action = ''
        clr = (0, 255, 0)
        # 30帧数据预测动作类型
        if len(self.joints_list) == ACTION_MODEL_MAX_FRAMES:
            pts = np.array(self.joints_list, dtype=np.float32)
            out = self.action_model.predict(pts, (image_w, image_h))
            action_name = self.action_model.class_names[out[0].argmax()]
            action = '{}: {:.2f}%'.format(action_name, out[0].max() * 100)
            print(action)
            if action_name == 'Fall Down':
                if self.isOk == True:
                    requests.get('http://localhost:8080/book/isokfalse')
                    self.isOk = False

                clr = (255, 0, 0)
                action = '摔倒'
            elif action_name == 'Walking':
                clr = (255, 128, 0)
                action = '行走'
            else:
                action = ''

        # 绘制骨骼点和动作类别
        image = self.draw_skeleton(image, self.joints_list[-1])
        image = cv2.rectangle(image, (box_l, box_t), (box_r, box_b), (255, 0, 0), 1)
        image = self.cv2_add_chinese_text(image, f'当前状态：{action}', (box_l + 10, box_t + 10), clr, 40)
        image = cv2.putText(image, f'FPS: {int(1.0 / (time.time() - fps_time))}',
                            (50, 50), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2)
        # Flip the image horizontally for a selfie-view display.
        cv2.imshow('MediaPipe Pose', image)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    # Release the resources.
    cap.release()
    cv2.destroyAllWindows()

if name == 'main':
FallDetection().detect()

import time
from collections import deque
import cv2
import numpy as np
import mediapipe as mp
from stgcn.stgcn import STGCN
from PIL import Image, ImageDraw, ImageFont
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
KEY_JOINTS = [
mp_pose.PoseLandmark.NOSE,
mp_pose.PoseLandmark.LEFT_SHOULDER,
mp_pose.PoseLandmark.RIGHT_SHOULDER,
mp_pose.PoseLandmark.LEFT_ELBOW,
mp_pose.PoseLandmark.RIGHT_ELBOW,
mp_pose.PoseLandmark.LEFT_WRIST,
mp_pose.PoseLandmark.RIGHT_WRIST,
mp_pose.PoseLandmark.LEFT_HIP,
mp_pose.PoseLandmark.RIGHT_HIP,
mp_pose.PoseLandmark.LEFT_KNEE,
mp_pose.PoseLandmark.RIGHT_KNEE,
mp_pose.PoseLandmark.LEFT_ANKLE,
mp_pose.PoseLandmark.RIGHT_ANKLE
]
POSE_CONNECTIONS = [(6, 4), (4, 2), (2, 13), (13, 1), (5, 3), (3, 1), (12, 10),
(10, 8), (8, 2), (11, 9), (9, 7), (7, 1), (13, 0)]
POINT_COLORS = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), (0, 255, 255)] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
LINE_COLORS = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50), (77, 255, 222),
(77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77), (77, 222, 255),
(255, 156, 127), (0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]
ACTION_MODEL_MAX_FRAMES = 30
class FallDetection:
def init(self):
self.action_model = STGCN(weight_file='./weights/tsstg-model.pth', device='cpu')
self.joints_list = deque(maxlen=ACTION_MODEL_MAX_FRAMES)
def draw_skeleton(self, frame, pts):
l_pair = POSE_CONNECTIONS
p_color = POINT_COLORS
line_color = LINE_COLORS
part_line = {}
pts = np.concatenate((pts, np.expand_dims((pts[1, :] + pts[2, :]) / 2, 0)), axis=0)
for n in range(pts.shape[0]):
if pts[n, 2] <= 0.05:
continue
cor_x, cor_y = int(pts[n, 0]), int(pts[n, 1])
part_line[n] = (cor_x, cor_y)
cv2.circle(frame, (cor_x, cor_y), 3, p_color[n], -1)

cv2.putText(frame, str(n), (cor_x+10, cor_y+10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1)

for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
cv2.line(frame, start_xy, end_xy, line_color[i], int(1*(pts[start_p, 2] + pts[end_p, 2]) + 3))
return frame
def cv2_add_chinese_text(self, img, text, position, textColor=(0, 255, 0), textSize=30):
if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

创建一个可以在给定图像上绘图的对象

draw = ImageDraw.Draw(img)

字体的格式

fontStyle = ImageFont.truetype(
"./fonts/MSYH.ttc", textSize, encoding="utf-8")

绘制文本

draw.text(position, text, textColor, font=fontStyle)

转换回OpenCV格式

return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
def detect(self):

Initialize the webcam capture.

cap = cv2.VideoCapture('./video.mp4')
image_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
image_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_num = 0
print(image_h, image_w)
with mp_pose.Pose(
min_detection_confidence=0.7,
min_tracking_confidence=0.5) as pose:
while cap.isOpened():
fps_time = time.time()
frame_num += 1
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")

If loading a video, use 'break' instead of 'continue'.

continue

提高性能

image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = pose.process(image)
if not results.pose_landmarks:
continue

识别骨骼点

image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

mp_drawing.draw_landmarks(

image,

results.pose_landmarks,

mp_pose.POSE_CONNECTIONS,

landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())

landmarks = results.pose_landmarks.landmark
joints = np.array([[landmarks[joint].x * image_w,
landmarks[joint].y * image_h,
landmarks[joint].visibility]
for joint in KEY_JOINTS])

人体框

box_l, box_r = int(joints[:, 0].min())-50, int(joints[:, 0].max())+50
box_t, box_b = int(joints[:, 1].min())-100, int(joints[:, 1].max())+100
self.joints_list.append(joints)

识别动作

action = ''
clr = (0, 255, 0)

30帧数据预测动作类型

if len(self.joints_list) == ACTION_MODEL_MAX_FRAMES:
pts = np.array(self.joints_list, dtype=np.float32)
out = self.action_model.predict(pts, (image_w, image_h))
action_name = self.action_model.class_names[out[0].argmax()]
action = '{}: {:.2f}%'.format(action_name, out[0].max() * 100)
print(action)
if action_name == 'Fall Down':
clr = (255, 0, 0)
action = '摔倒'
elif action_name == 'Walking':
clr = (255, 128, 0)
action = '行走'
else:
action = ''

绘制骨骼点和动作类别

image = self.draw_skeleton(image, self.joints_list[-1])
image = cv2.rectangle(image, (box_l, box_t), (box_r, box_b), (255, 0, 0), 1)
image = self.cv2_add_chinese_text(image, f'当前状态：{action}', (box_l + 10, box_t + 10), clr, 40)
image = cv2.putText(image, f'FPS: {int(1.0 / (time.time() - fps_time))}',
(50, 50), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2)

Flip the image horizontally for a selfie-view display.

cv2.imshow('MediaPipe Pose', image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break

Release the resources.

cap.release()
cv2.destroyAllWindows()
if name == 'main':
FallDetection().detect()