最终版

.idea/code.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.7 (graduation_project)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (graduation_project2)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (graduation_project)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (graduation_project2)" project-jdk-type="Python SDK" />
 </project>

detect.py

@@ -53,7 +53,7 @@ class YOLOV5(object):
                  iou_thres=0.45,
                  classes=None,
                  imgsz=640,
-                 weights="./yolov5s.pt"):
+                 weights="C:/Users/Dinger/Desktop/work/lesson/graduation_project/graduation-project/resource/yolov5s.pt"):
         # 超参数设置
         self.conf_thres = conf_thres  # 置信度阈值
         self.iou_thres = iou_thres  # iou阈值

identify.py

@@ -101,6 +101,8 @@ class Identify:
         self.last_control_flag = 0
         self.page_up_count = 0
         self.page_down_count = 0
+        self.end_count = 0
+        self.pencil_count = 0
         self.step_up = 0
         self.step_down = 0
         self.last_wrist_point = (0, 0)
@@ -142,15 +144,15 @@ class Identify:
             # self.ml_identify.infer(image=self.image)
             # continue
             # self.catch_person_flag = True
+            # if not self.catch_person_flag:
+            # self.catch_person_flag = True
             if not self.catch_person_flag:
-                # self.catch_person_flag = True
                 self.person_results.clear()
                 self.left_hands.clear()
                 self.right_hands.clear()
                 self.deal_with_image()
                 self.find_points()
                 x1, y1, x2, y2 = self.find_target_person()
-
                 # print("no no no no no no no no no no no no no")
             else:
                 # print("in True")
@@ -223,7 +225,7 @@ class Identify:
         if not judge_zero(self.is_right_finger_straight):
             return False
         if util.Util.get_distance(self.left_hand_points[6], self.right_hand_points[6]) / \
-                util.Util.get_distance(self.left_hand_points[5], self.left_hand_points[9]) > 2:
+                util.Util.get_distance(self.left_hand_points[5], self.left_hand_points[9]) > 2.5:
             return False
         return True
 
@@ -291,6 +293,8 @@ class Identify:
             if not person_result[2]:
                 continue
             standard_distance = util.Util.get_distance(person_result[1][7], person_result[1][8])
+            if standard_distance == 0:
+                continue
             if util.Util.get_distance(person_result[1][4], person_result[2][4]) / standard_distance > 2:
                 continue
             if util.Util.get_distance(person_result[1][8], person_result[2][8]) / standard_distance > 1:
@@ -454,7 +458,15 @@ class Identify:
         return False
 
     def judge_end(self):
-        if self.left_hand_flag and self.right_hand_flag and judge_zero():
+        if not self.left_hand_flag:
+            self.end_count = 0
+            return False
+        if not judge_zero(self.is_left_finger_straight):
+            self.end_count = 0
+            return False
+        self.end_count += 1
+        if self.end_count > 10:
+            self.end_count = 0
             return True
         return False
 
@@ -464,11 +476,22 @@ class Identify:
         if not self.judge_one(self.is_right_finger_straight):
             return False
         standard_distance = util.Util.get_distance(self.left_hand_points[7], self.right_hand_points[8])
+        if standard_distance == 0:
+            return False
         # print(util.Util.get_distance(self.left_hand_points[8], self.right_hand_points[8]) / standard_distance)
         if util.Util.get_distance(self.left_hand_points[8], self.right_hand_points[8]) / standard_distance < 1:
             return True
         return False
 
+    # def judge_pencil(self):
+    #     if not self.judge_one(self.is_left_finger_straight):
+    #         return False
+    #     if self.right_hand_flag:
+    #         return False
+    #     self.pencil_count += 1
+    #     if self.pencil_count > 5:
+    #         self.pencil_count = 0
+
     def judge_control(self):
         if self.is_identify:
             # print("len = " + str(len(self.identify_results.multi_handedness)))
@@ -489,7 +512,7 @@ class Identify:
             elif self.judge_one(self.is_left_finger_straight) and judge_zero(self.is_right_finger_straight):
                 # print("5")
                 return 5
-            elif self.left_hand_flag and judge_zero(self.is_left_finger_straight):
+            elif self.judge_end():
                 # print("6")
                 return 6
             elif self.judge_system_over():

main.py

@@ -1,3 +1,4 @@
+#!C:\Users\Dinger\anaconda3\envs\graduation_project2\python.exe
 import multiprocessing
 import sys
 import tkinter
@@ -24,8 +25,8 @@ def control_page_down():
 
 
 def control_ppt_begin():
-    win32api.keybd_event(116, 0, 0, 0)  # 代表按下f键
     win32api.keybd_event(16, 0, 0, 0)  # 代表按下f键
+    win32api.keybd_event(116, 0, 0, 0)  # 代表按下f键
     time.sleep(0.02)
     win32api.keybd_event(116, 0, win32con.KEYEVENTF_KEYUP, 0)  # 释放f键
     win32api.keybd_event(16, 0, win32con.KEYEVENTF_KEYUP, 0)  # 释放f键
@@ -103,11 +104,6 @@ def control_thread(control_number, array, over_flag):
                 control_ppt_end()
                 step = 0
 
-        # if control_flag == 5:
-        #     control_open_pencil()
-        # if control_flag == 6:
-        #     control_draw()
-
 
 def identify_thread(control_number, array, over_flag):
     identify = Identify(control_number, array)
@@ -116,29 +112,16 @@ def identify_thread(control_number, array, over_flag):
 
 def open_file():
     file_path = askopenfilename(title=u'选择文件')
-    # file_path = askopenfilename(title=u'选择文件', initialdir=(os.path.expanduser('H:/')))
     if not open_ppt(file_path):
         return False
     control_number = multiprocessing.Value('i', 0)
     array = multiprocessing.Array('i', 4)
     over_flag = multiprocessing.Value('i', 0)
-    # array2 = multiprocessing.Array()
     p1 = multiprocessing.Process(target=identify_thread, args=(control_number, array, over_flag))
     p2 = multiprocessing.Process(target=control_thread, args=(control_number, array, over_flag))
-    # p3 = multiprocessing.Process(target=show_thread, args=(value, array))
     p1.start()
-    # p1.terminate()
     p2.start()
     return True
-    # p3.start()
-    # identify_t = threading.Thread(target=identify_thread)
-    # print("control_flag1 = " + str(control_flag))
-    # control_t = threading.Thread(target=control_thread)
-    # print("control_flag2 = " + str(control_flag))
-    # identify_t.setDaemon(True)
-    # control_t.setDaemon(True)
-    # identify_t.start()
-    # control_t.start()
 
 
 def judge_ppt(file_path):
@@ -166,15 +149,10 @@ def open_ppt(file_path):
     return True
 
 
-# def make_button(window):
-
-
 if __name__ == '__main__':
     window = tkinter.Tk()
     window.title("会议PPT选择")
     window.geometry("200x100")
     bt1 = tkinter.Button(window, text='打开文件', width=15, height=15, command=open_file)
     bt1.pack()
-    # show_text = tkinter.Text(window, height=2)
-    # show_text.pack()
     window.mainloop()

models/yolo.py

@@ -301,24 +301,25 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
+    print("in yolo")
-    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
+    # parser = argparse.ArgumentParser()
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    # parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
-    parser.add_argument('--profile', action='store_true', help='profile model speed')
+    # parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    opt = parser.parse_args()
+    # parser.add_argument('--profile', action='store_true', help='profile model speed')
-    opt.cfg = check_yaml(opt.cfg)  # check YAML
+    # opt = parser.parse_args()
-    print_args(FILE.stem, opt)
+    # opt.cfg = check_yaml(opt.cfg)  # check YAML
-    set_logging()
+    # print_args(FILE.stem, opt)
-    device = select_device(opt.device)
+    # set_logging()
-
+    # device = select_device(opt.device)
-    # Create model
+    #
-    model = Model(opt.cfg).to(device)
+    # # Create model
-    model.train()
+    # model = Model(opt.cfg).to(device)
-
+    # model.train()
-    # Profile
+    #
-    if opt.profile:
+    # # Profile
-        img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
+    # if opt.profile:
-        y = model(img, profile=True)
+    #     img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
+    #     y = model(img, profile=True)
 
     # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
     # from torch.utils.tensorboard import SummaryWriter

models/yolov5l.yaml

@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
-   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 6, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 3, C3, [1024]],
-   [-1, 1, SPPF, [1024, 5]],  # 9
-  ]
-
-# YOLOv5 v6.0 head
-head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 13
-
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
-
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
-
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
-
-   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]

models/yolov5m.yaml

@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80  # number of classes
-depth_multiple: 0.67  # model depth multiple
-width_multiple: 0.75  # layer channel multiple
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
-   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 6, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 3, C3, [1024]],
-   [-1, 1, SPPF, [1024, 5]],  # 9
-  ]
-
-# YOLOv5 v6.0 head
-head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 13
-
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
-
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
-
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
-
-   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]

models/yolov5n.yaml

@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80  # number of classes
-depth_multiple: 0.33  # model depth multiple
-width_multiple: 0.25  # layer channel multiple
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
-   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 6, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 3, C3, [1024]],
-   [-1, 1, SPPF, [1024, 5]],  # 9
-  ]
-
-# YOLOv5 v6.0 head
-head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 13
-
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
-
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
-
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
-
-   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]

models/yolov5x.yaml

@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80  # number of classes
-depth_multiple: 1.33  # model depth multiple
-width_multiple: 1.25  # layer channel multiple
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
-   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 6, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 3, C3, [1024]],
-   [-1, 1, SPPF, [1024, 5]],  # 9
-  ]
-
-# YOLOv5 v6.0 head
-head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 13
-
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
-
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
-
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
-
-   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]

test.py

@@ -1,165 +0,0 @@
-import cv2
-import mediapipe as mp
-import detect
-import numpy as np
-# !/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Time    : 2021/04/08
-# @Author  : Devil_Xiao
-# Purpose: This script is used to change the video_speed
-# You can choose to specify the fps,
-# or you can choose to change the multiple of the original playback speed
-
-import cv2
-from cv2 import VideoWriter, VideoWriter_fourcc
-import argparse
-
-
-def video_speed(video_root, out_root, fps=None, scale=1):
-    """When fps and scale are specified at the same time, fps is the dominant"""
-    # cap = cv2.VideoCapture(video_root)
-    cap = cv2.VideoCapture(0)
-    video_width = int(cap.get(3))
-    video_height = int(cap.get(4))
-    print(video_width)
-    print(video_height)
-    # fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
-    # if fps:
-    #     videoWriter = cv2.VideoWriter(out_root, fourcc, fps, (video_width, video_height))
-    # else:
-    #     fps = int(cap.get(cv2.CAP_PROP_FPS) * scale)
-    #     videoWriter = cv2.VideoWriter(out_root, fourcc, fps, (video_width, video_height))
-    flag = cap.isOpened()
-    mp_drawing = mp.solutions.drawing_utils
-    mp_drawing_styles = mp.solutions.drawing_styles
-    mp_hands = mp.solutions.hands
-    mp_poses = mp.solutions.pose
-    hands = mp_hands.Hands(
-        static_image_mode=False,
-        max_num_hands=2,
-        min_detection_confidence=0.75,
-        min_tracking_confidence=0.75)
-    poses = mp_poses.Pose(
-        min_detection_confidence=0.5,
-        min_tracking_confidence=0.5)
-
-    # yolov5 = detect.YOLOV5()
-    # blank_image = np.zeros((1000, 1000, 3), np.uint8)
-    # blank_image.fill(255)
-    while True:
-        ret, frame = cap.read()
-        # blank_image = frame.copy()
-        # blank_image.fill(255)
-        # image = detect.run(image_input=frame)
-        # result = yolov5.infer(image=frame)
-        # print("")
-
-        frame = cv2.flip(frame, 1)
-        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        # frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-        results = hands.process(frame_rgb)
-        # results = poses.process(image)
-        # print("*********************************")
-        # if results.pose_landmarks:
-        #     for
-        # print(results.pose_landmarks.landmark[17])
-        # mp_drawing.draw_landmarks(
-        #     blank_image,
-        #     results.pose_landmarks,
-        #     mp_poses.POSE_CONNECTIONS,
-        #     landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
-        # if results.
-        # print(results.multi_hand_landmarks)
-        if results.multi_handedness:
-            # if results.multi_handedness[0].classification[0].label == "Left":
-            for hand_landmarks in results.multi_hand_landmarks:
-                # 关键点可视化
-                mp_drawing.draw_landmarks(
-                    frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
-        # if results.multi_handedness[0].classification[0].label == "Right":
-        #     print(results.multi_handedness[0].classification[0])
-        cv2.imshow('MediaPipe Hands', frame)
-        # cv2.imshow('black_image', blank_image)
-        # videoWriter.write(blank_image)
-        # count = count - 1
-        cv2.imwrite("picture3.jpg", frame)
-        if cv2.waitKey(3000) & 0xFF == 27:
-            break
-    cap.release()
-    # videoWriter.release()
-    # while (flag):
-    #     flag, frame = cap.read()
-    #     videoWriter.write(frame)
-    # videoWriter.release()
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--video_name', type=str, default=r'video.mp4', help='original video name')
-    parser.add_argument('--result_name', type=str, default=r'result.mp4', help='result name')
-    parser.add_argument('--fps', type=int, default=None, help='Specify the playback frame rate')
-    parser.add_argument('--scale', type=float, default='0.5', help='Change the original video speed')
-    opt = parser.parse_args()
-    print(opt)
-    video_speed(opt.video_name, opt.result_name, opt.fps, 2)
-
-# if __name__ == '__main__':
-#     parser = argparse.ArgumentParser()
-#     parser.add_argument('--video_name', type=str, default=r'video.mp4', help='original video name')
-#     parser.add_argument('--result_name', type=str, default=r'result.mp4', help='result name')
-#     parser.add_argument('--fps', type=int, default=None, help='Specify the playback frame rate')
-#     parser.add_argument('--scale', type=float, default='0.5', help='Change the original video speed')
-#     opt = parser.parse_args()
-#     mp_drawing = mp.solutions.drawing_utils
-#     mp_drawing_styles = mp.solutions.drawing_styles
-#     mp_hands = mp.solutions.hands
-#     mp_poses = mp.solutions.pose
-#     hands = mp_hands.Hands(
-#         static_image_mode=False,
-#         max_num_hands=2,
-#         min_detection_confidence=0.75,
-#         min_tracking_confidence=0.75)
-#     poses = mp_poses.Pose(
-#         min_detection_confidence=0.5,
-#         min_tracking_confidence=0.5)
-#     cap = cv2.VideoCapture("D:\\Program Files\\JiJiDown\\Download\\Thank_you.mp4")
-#     yolov5 = detect.YOLOV5()
-#     blank_image = np.zeros((1000, 1000, 3), np.uint8)
-#     blank_image.fill(255)
-#     while True:
-#         ret, frame = cap.read()
-#         blank_image = np.zeros((1000, 1000, 3), np.uint8)
-#         blank_image.fill(255)
-#         # image = detect.run(image_input=frame)
-#         # result = yolov5.infer(image=frame)
-#         # print("")
-#         # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#         # frame = cv2.flip(frame, 1)
-#         image = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-#         # results = hands.process(image)
-#         results = poses.process(image)
-#         # print("*********************************")
-#         # if results.pose_landmarks:
-#         #     for
-#         # print(results.pose_landmarks.landmark[17])
-#         mp_drawing.draw_landmarks(
-#             blank_image,
-#             results.pose_landmarks,
-#             mp_poses.POSE_CONNECTIONS,
-#             landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
-#         # if results.
-#         # print(results.multi_hand_landmarks)
-#         # if results.multi_handedness:
-#         #     # if results.multi_handedness[0].classification[0].label == "Left":
-#         #     for hand_landmarks in results.multi_hand_landmarks:
-#         #         # 关键点可视化
-#         #         mp_drawing.draw_landmarks(
-#         #             frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
-#         #     if results.multi_handedness[0].classification[0].label == "Right":
-#         #         print(results.multi_handedness[0].classification[0])
-#         cv2.imshow('MediaPipe Hands', frame)
-#         cv2.imshow('black_image', blank_image)
-#         # cv2.imwrite("picture3.jpg", frame)
-#         if cv2.waitKey(1) & 0xFF == 27:
-#             break
-#     cap.release()

util.py

@@ -27,7 +27,8 @@ class Util:
     def get_distance(point1, point2):
         return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)
 
-    def is_in_rectangle(target_point, rectangle):
+    def \
+            is_in_rectangle(target_point, rectangle):
         if target_point[0] > rectangle[0] and target_point[0] < rectangle[2] and target_point[1] > rectangle[1] and \
                 target_point[1] < rectangle[3]:
             return True