最終成功果展示

最終成功果展示

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)): # 判斷是否OpenCV圖片型別
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 建立一個可以在給定影像上繪圖的物件
draw = ImageDraw.Draw(img)
# 字型的格式
fontStyle = ImageFont.truetype(
"./fonts/MSYH.ttc", textSize, encoding="utf-8")
# 繪製文字
draw.text(position, text, textColor, font=fontStyle)
# 轉換回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表示預設攝像頭
# cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800) # 解決問題的關鍵！！！
# 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.")
            # 如果是實時攝像頭，不需要使用'continue'
            continue

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

        if not results.pose_landmarks:
            continue

        # 識別骨骼點
        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])
        # 人體框
        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)

        # 識別動作
        action = ''
        clr = (0, 255, 0)
        # 30幀資料預測動作型別
        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 = ''

        # 繪製骨骼點和動作類別
        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'當前狀態：{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)): # 判斷是否OpenCV圖片型別
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

建立一個可以在給定影像上繪圖的物件

draw = ImageDraw.Draw(img)

字型的格式

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

繪製文字

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

轉換回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

提高效能

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

識別骨骼點

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])

人體框

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)

識別動作

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

30幀資料預測動作型別

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 = ''

繪製骨骼點和動作類別

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'當前狀態：{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()