def show_camera(self):
start = time.time()
ret, frame = self.cap.read()
if ret:
if self.__flag_mode == 1:
self.infoBox.setText(u'当前为人体姿态估计模式')
humans = estimator.inference(frame) # 返回heatMat pafMat空间地址
# get pose info
# return frame, joints, bboxes, xcenter
pose = TfPoseVisualizer.draw_pose_rgb(frame, humans) # pose绘制在画面上了
# recognize the action framewise
show = framewise_recognize(pose, action_classifier) # 返回绘制好的frame
# elif self.__flag_mode == 2:
# self.infoBox.setText(u'当前为多人跟踪模式')
# humans = poseEstimator.inference(show)
# show, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(show, humans, imgcopy=False)
# height = show.shape[0]
# width = show.shape[1]
# if bboxes:
# result = np.array(bboxes)
# det = result[:, 0:5]
# det[:, 0] = det[:, 0] * width
# det[:, 1] = det[:, 1] * height
# det[:, 2] = det[:, 2] * width
# det[:, 3] = det[:, 3] * height
# trackers = self.tracker.update(det)
#
# for d in trackers:
# xmin = int(d[0])
# ymin = int(d[1])
# xmax = int(d[2])
# ymax = int(d[3])
# label = int(d[4])
# cv2.rectangle(show, (xmin, ymin), (xmax, ymax),
# (int(settings.c[label % 32, 0]),
# int(settings.c[label % 32, 1]),
# int(settings.c[label % 32, 2])), 4)
#
# elif self.__flag_mode == 3:
# self.infoBox.setText(u'当前为人体行为识别模式')
# humans = poseEstimator.inference(show)
# ori = np.copy(show)
# show, joints, bboxes, xcenter, sk= TfPoseEstimator.get_skeleton(show, humans, imgcopy=False)
# height = show.shape[0]
# width = show.shape[1]
# if bboxes:
# result = np.array(bboxes)
# det = result[:, 0:5]
# det[:, 0] = det[:, 0] * width
# det[:, 1] = det[:, 1] * height
# det[:, 2] = det[:, 2] * width
# det[:, 3] = det[:, 3] * height
# trackers = self.tracker.update(det)
# self.current = [i[-1] for i in trackers]
#
# if len(self.previous) > 0:
# for item in self.previous:
# if item not in self.current and item in self.data:
# del self.data[item]
# if item not in self.current and item in self.memory:
# del self.memory[item]
#
# self.previous = self.current
# for d in trackers:
# xmin = int(d[0])
# ymin = int(d[1])
# xmax = int(d[2])
# ymax = int(d[3])
# label = int(d[4])
# try:
# j = np.argmin(np.array([abs(i - (xmax + xmin) / 2.) for i in xcenter]))
# except:
# j = 0
# if joint_filter(joints[j]):
# joints[j] = joint_completion(joint_completion(joints[j]))
# if label not in self.data:
# self.data[label] = [joints[j]]
# self.memory[label] = 0
# else:
# self.data[label].append(joints[j])
#
# if len(self.data[label]) == settings.L:
# pred = actionPredictor().move_status(self.data[label])
# if pred == 0:
# pred = self.memory[label]
# else:
# self.memory[label] = pred
# self.data[label].pop(0)
#
# location = self.data[label][-1][1]
# if location[0] <= 30:
# location = (51, location[1])
# if location[1] <= 10:
# location = (location[0], 31)
#
# cv2.putText(show, settings.move_status[pred], (location[0] - 30, location[1] - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.8,
# (0, 255, 0), 2)
#
# cv2.rectangle(show, (xmin, ymin), (xmax, ymax),
# (int(settings.c[label % 32, 0]),
# int(settings.c[label % 32, 1]),
# int(settings.c[label % 32, 2])), 4)
#
show = cv.resize(frame, (settings.winWidth, settings.winHeight))
show = cv.cvtColor(show, cv.COLOR_BGR2RGB)
end = time.time()
self.fps = 1. / (end - start)
cv.putText(show, 'FPS: %.2f' % self.fps, (30, 30), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], QtGui.QImage.Format_RGB888)
self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage))
else:
self.cap.release()
self.timer_camera.stop()
if not args.type:
print('请输入具体采集的动作类型:-<')
else:
while cv.waitKey(1) < 0:
has_frame, show = cap.read()
if has_frame:
fps_count += 1
frame_count += 1
# pose estimation
humans = estimator.inference(show)
# get pose info
pose = TfPoseVisualizer.draw_pose_rgb(
show, humans) # return frame, joints, bboxes, xcenter
# recognize the action framewise
show = framewise_recognize(pose, action_classifier)
height, width = show.shape[:2]
# 显示实时FPS值
if (time.time() - start_time) > fps_interval:
# 计算这个interval过程中的帧数，若interval为1秒，则为FPS
realtime_fps = fps_count / (time.time() - start_time)
fps_count = 0 # 帧数清零
start_time = time.time()
fps_label = 'FPS:{0:.2f}'.format(realtime_fps)
cv.putText(show, fps_label, (width - 160, 25),
cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
# 显示检测到的人数
num_label = "Human: {0}".format(len(humans))
cv.putText(show, num_label, (5, height - 45),
#print("DEBUG:stage 3")
if has_frame:
fps_count += 1
frame_count += 1
# 灰度图像转为RGB图像
show = cv.cvtColor(show, cv.COLOR_GRAY2RGB)
# 姿势识别,human存储着标记好骨骼的数据（以图像的相对位置表示）
humans = estimator.inference(show)
# get pose info，pose中存储着绘制好关键节点的姿势数据，主要目的是计算出关节的实际坐标位置，绘制骨骼，并返回整理完后的数据
pose = TfPoseVisualizer.draw_pose_rgb(
show,
humans,
) # return frame, joints, bboxes, xcenter
# recognize the action framewise
show = framewise_recognize(pose, action_classifier, cap_Receptor)
height, width = show.shape[:2]
# 显示实时FPS值
if (time.time() - start_time) > fps_interval:
# 计算这个interval过程中的帧数，若interval为1秒，则为FPS
realtime_fps = fps_count / (time.time() - start_time)
fps_count = 0 # 帧数清零
start_time = time.time()
fps_label = 'FPS:{0:.2f}'.format(realtime_fps)
cv.putText(show, fps_label, (width - 160, 25), cv.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 3)
# 显示检测到的人数
num_label = "Human: {0}".format(len(humans))
cv.putText(show, num_label, (5, height - 45), cv.FONT_HERSHEY_SIMPLEX,
# pose estimation
humans = estimator.inference(show)
# get pose info
pose = TfPoseVisualizer.draw_pose_rgb(show, humans) # return frame, joints, bboxes, xcenter.
if(pose[-1] != []):
seq_data.append(pose)
if len(seq_data) == 10:
input_seq_data = seq_data
# print("################################ Make input data ################################")
# # print("################################ check ################################ ")
# recognize the action framewise
show = framewise_recognize(pose, action_classifier,input_seq_data)
if len(seq_data) == 10:
input_seq_data, seq_data = [], []
height, width = show.shape[:2]
# 실시간 FPS 값 표시
if (time.time() - start_time) > fps_interval:
# 이 interval 프로세스에서 프레임 수를 계산하고 interval이 1초이면 FPS
realtime_fps = fps_count / (time.time() - start_time)
fps_count = 0 # 프레임 수가 매우 적다.
start_time = time.time()
# fps_label = 'FPS:{0:.2f}'.format(realtime_fps)
# cv.putText(show, fps_label, (width-160, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
# 사람의 수를 표시
num_label = "Human: {0}".format(len(humans))
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_detection.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
canvas = np.zeros((250, 300, 3), dtype="uint8")
frameClone = frame.copy()
#pose
humans = estimator.inference(frameClone)
pose = TfPoseVisualizer.draw_pose_rgb(frameClone, humans)
frameClone = framewise_recognize(pose, action_classifier)
height, width = frameClone.shape[:2]
if len(faces) > 0:
faces = sorted(faces,
reverse=True,
key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
(fX, fY, fW, fH) = faces
# Extract the ROI of the face from the grayscale image, resize it to a fixed 48x48 pixels, and then prepare
# the ROI for classification via the CNN
roi = gray[fY:fY + fH, fX:fX + fW]
roi = cv2.resize(roi, (48, 48))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
