collect face by dlib

Class/collect_data/get_faces_from_camera.py

@@ -0,0 +1,178 @@
+# 进行人脸录入 / face register
+# 录入多张人脸 / support multi-faces
+
+import dlib         # 人脸处理的库 Dlib
+import numpy as np  # 数据处理的库 Numpy
+import cv2          # 图像处理的库 OpenCV
+import os           # 读写文件
+import shutil       # 读写文件
+import time
+
+# Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+
+class Face_Register:
+    def __init__(self):
+        self.path_photos_from_camera = "data/data_faces_from_camera/"
+        self.font = cv2.FONT_ITALIC
+
+        self.existing_faces_cnt = 0     # 已录入的人脸计数器
+        self.ss_cnt = 0                 # 录入 personX 人脸时图片计数器
+        self.faces_cnt = 0              # 录入人脸计数器
+
+        # 之后用来控制是否保存图像的 flag
+        self.save_flag = 1
+        # 之后用来检查是否先按 'n' 再按 's'，即先新建文件夹再保存
+        self.press_n_flag = 0
+
+        self.frame_time = 0
+        self.frame_start_time = 0
+        self.fps = 0
+
+    # 新建保存人脸图像文件
+    def pre_work_mkdir(self):
+        if os.path.isdir(self.path_photos_from_camera):
+            pass
+        else:
+            os.mkdir(self.path_photos_from_camera)
+
+    # 删除之前存的人脸数据文件夹
+    def pre_work_del_old_face_folders(self):
+        # 删除之前存的人脸数据文件夹
+        folders_rd = os.listdir(self.path_photos_from_camera)
+        for i in range(len(folders_rd)):
+            shutil.rmtree(self.path_photos_from_camera+folders_rd[i])
+        if os.path.isfile("data/features_all.csv"):
+            os.remove("data/features_all.csv")
+
+    # 如果有之前录入的人脸, 在之前 person_x 的序号按照 person_x+1 开始录入
+    def check_existing_faces_cnt(self):
+        if os.listdir("data/data_faces_from_camera/"):
+            # 获取已录入的最后一个人脸序号
+            person_list = os.listdir("data/data_faces_from_camera/")
+            person_num_list = []
+            for person in person_list:
+                person_num_list.append(int(person.split('_')[-1]))
+            self.existing_faces_cnt = max(person_num_list)
+
+        # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入
+        # Start from person_1
+        else:
+            self.existing_faces_cnt = 0
+
+    # 获取处理之后 stream 的帧数
+    def update_fps(self):
+        now = time.time()
+        self.frame_time = now - self.frame_start_time
+        self.fps = 1.0 / self.frame_time
+        self.frame_start_time = now
+
+    # 生成的 cv2 window 上面添加说明文字
+    def draw_note(self, img_rd):
+        # 添加说明
+        cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
+                    cv2.LINE_AA)
+        cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+
+    # 获取人脸
+    def process(self, stream):
+        # 1. 新建储存人脸图像文件目录
+        # self.pre_work_mkdir()
+
+        # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件
+        self.pre_work_del_old_face_folders()
+
+        # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件
+        self.check_existing_faces_cnt()
+
+        while stream.isOpened():
+            flag, img_rd = stream.read()        # Get camera video stream
+            kk = cv2.waitKey(1)
+            faces = detector(img_rd, 0)         # Use dlib face detector
+
+            # 4. 按下 'n' 新建存储人脸的文件夹
+            if kk == ord('n'):
+                self.existing_faces_cnt += 1
+                current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt)
+                os.makedirs(current_face_dir)
+                print('\n')
+                print("新建的人脸文件夹 / Create folders: ", current_face_dir)
+
+                self.ss_cnt = 0              # 将人脸计数器清零
+                self.press_n_flag = 1        # 已经按下 'n'
+
+            # 5. 检测到人脸
+            if len(faces) != 0:
+                # 矩形框
+                for k, d in enumerate(faces):
+                    # 计算矩形框大小
+                    height = (d.bottom() - d.top())
+                    width = (d.right() - d.left())
+                    hh = int(height/2)
+                    ww = int(width/2)
+
+                    # 6. 判断人脸矩形框是否超出 480x640
+                    if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0):
+                        cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
+                        color_rectangle = (0, 0, 255)
+                        save_flag = 0
+                        if kk == ord('s'):
+                            print("请调整位置 / Please adjust your position")
+                    else:
+                        color_rectangle = (255, 255, 255)
+                        save_flag = 1
+
+                    cv2.rectangle(img_rd,
+                                  tuple([d.left() - ww, d.top() - hh]),
+                                  tuple([d.right() + ww, d.bottom() + hh]),
+                                  color_rectangle, 2)
+
+                    # 7. 根据人脸大小生成空的图像
+                    img_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
+
+                    if save_flag:
+                        # 8. 按下 's' 保存摄像头中的人脸到本地
+                        if kk == ord('s'):
+                            # 检查有没有先按'n'新建文件夹
+                            if self.press_n_flag:
+                                self.ss_cnt += 1
+                                for ii in range(height*2):
+                                    for jj in range(width*2):
+                                        img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj]
+                                cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
+                                print("写入本地 / Save into：", str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg")
+                            else:
+                                print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
+                self.faces_cnt = len(faces)
+
+            # 9. 生成的窗口添加说明文字
+            self.draw_note(img_rd)
+
+            # 10. 按下 'q' 键退出
+            if kk == ord('q'):
+                break
+
+            self.update_fps()
+            cv2.namedWindow("camera", 1)
+            cv2.imshow("camera", img_rd)
+
+    def run(self):
+        cap = cv2.VideoCapture(0)
+        self.process(cap)
+
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+def main():
+    Face_Register_con = Face_Register()
+    Face_Register_con.run()
+
+
+if __name__ == '__main__':
+    main()

Class/live/Live_BroadCast.py

@@ -1,3 +1,5 @@
+# 进行摄像头直播推流 / live_broadcast
+
 import cv2 as cv
 import time
 import subprocess as sp
@@ -35,6 +37,7 @@ class stream_pusher(object):
     # 对获取的帧做一些画面处理的方法，返回完成处理的帧。
     def __frame_handle__(self, raw_frame, text, shape1, shape2):
         # 帧用cv2进行一些处理，比如写上文本，画矩形等
+        cv.putText(raw_frame, text.split('.')[0], (20, 40), cv.FONT_ITALIC, 0.8, (0, 0, 255), 1, cv.LINE_AA)
         return (raw_frame)
 
     # 向服务器推送
@@ -85,14 +88,13 @@ if __name__ == '__main__':
 
     rtmpUrl = "rtmp://39.97.124.237:1984/wodelive"  # 用vcl等直播软件播放时，也用这个地址
     raw_q = multiprocessing.Queue()  # 定义一个向推流对象传入帧及其他信息的队列
-    time_q=multiprocessing.Queue()
 
     my_pusher = stream_pusher(rtmp_url=rtmpUrl, raw_frame_q=raw_q)  # 实例化一个对象
     my_pusher.run()  # 让这个对象在后台推送视频流
 
     while True:
         _, raw_frame = cap.read()
-        info = (raw_frame, '2', '3', '4')  # 把需要送入队列的内容进行封装
+        info = (raw_frame, str(datetime.datetime.now()), '3', '4')  # 把需要送入队列的内容进行封装
         # if not raw_q.full():  # 如果队列没满
         raw_q.put(info)  # 送入队列
         cv.waitKey(1)