def __init__(self, arguments):
self.arguments = arguments
# Frame window dim
self.winWidth = 640
self.winHeight = 480
actionPredictor_params.__init__(self)
self.fps_time = 0
#self.step = 15
self.mode = {
'Pose Estimation': 'estimation',
'Tracking': 'tracking',
'Action Recognition': 'recognition'
}
w, h = model_wh(self.arguments.resize)
if w > 0 and h > 0:
self.estimator = TfPoseEstimator(get_graph_path(
self.arguments.model),
target_size=(w, h))
else:
self.estimator = TfPoseEstimator(get_graph_path(
self.arguments.model),
target_size=(432, 368))
self.cam = cv2.VideoCapture(self.arguments.camera)
# Tracker based on Sort
self.sort_max_age = 20
self.sort_min_hit = 3
self.tracker = Sort(self.sort_max_age, self.sort_min_hit)
def _perform_tracking_(self):
self.humans = self.estimator.inference(self.image)
self.image, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
self.image, self.humans, imgcopy=False)
height = self.image.shape[0]
width = self.image.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(
self.image, (xmin, ymin), (xmax, ymax),
(int(self.c[label % 32, 0]), int(
self.c[label % 32, 1]), int(self.c[label % 32, 2])), 4)
def load_model():
global poseEstimator
global sk_cnn_actionPredicter
poseEstimator = TfPoseEstimator(get_graph_path('mobilenet_thin'),
target_size=(432, 368))
sk_cnn_actionPredicter = sk_cnn.SkelCNN()
sk_cnn_actionPredicter.load_weights(
'./action_pre_sk_cnn/models/skel_cnn_model/sk-cnn.hdf5')
def load_model():
global poseEstimator
poseEstimator = TfPoseEstimator(get_graph_path('mobilenet_thin'),
target_size=(432, 368))
def show_camera(self):
start = time.time()
ret, frame = self.cap.read()
# if not ret:
# print('this should save the video')
# self.timer_camera.stop()
# self.out.release()
if ret:
show_s = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show_s, cv2.COLOR_BGR2RGB)
if self.__flag_mode == 1:
self.infoBox.setText(u'当前为人体Attitude Estimation模式')
humans = poseEstimator.inference(show)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
elif self.__flag_mode == 2:
self.infoBox.setText(u'当前为Multiplayer tracking模式')
humans = poseEstimator.inference(show)
show, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
show, humans, imgcopy=False)
show = TfPoseEstimator.draw_humans(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)
# This is the part to get the kt points
elif self.__flag_mode == 3:
self.infoBox.setText(u'当前为人体Behavior recognition模式')
humans = poseEstimator.inference(show)
ori = np.copy(show)
show, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
show, humans, imgcopy=False)
show = TfPoseEstimator.draw_humans(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)
self.out.write(show_s) # Write out frame to video
end = time.time()
self.fps = 1. / (end - start)
cv2.putText(show, 'FPS: %.2f' % self.fps, (30, 30),
cv2.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:
print('this should save the video')
self.timer_camera.stop()
self.out.release()
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 3 20:41:35 2019
@author: ASUS
"""
import cv2
import numpy as np
import settings
from pose.estimator import TfPoseEstimator
poseEstimator = None
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
humans = poseEstimator.inference(show)
print(humans)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 3 20:41:35 2019
@author: ASUS
"""
import cv2
import numpy as np
import settings
from pose.estimator import TfPoseEstimator
from pose.networks import get_graph_path
from imutils.video import VideoStream
poseEstimator = None
poseEstimator = TfPoseEstimator(get_graph_path('mobilenet_thin'), target_size=(432, 368))
cap=cv2.VideoCapture(0)
#cap = VideoStream(src='rtsp://*****:*****@192.168.51.162/PSIA/streaming/channels/102').start()
while True:
ret,frame=cap.read()
ret=True
if ret :
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
humans = poseEstimator.inference(show)
def show_camera(self):
start = time.time()
ret, frame = self.cap.read()
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
if ret:
if self.__flag_mode == 1:
self.infoBox.setText(u'当前为人体姿态估计模式')
humans = poseEstimator.inference(show)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
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 = 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) #有几个框就生成几个追踪器
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]):
pred = 0
joints[j] = joint_completion(
joint_completion(joints[j]))
if len(joints[j]) == 18:
if label not in self.data:
self.data[label] = [
joints[j]
] #self.data[label]:[{0:(xj1,yj1),1:(xj2,yj2),...,n:(xjn,yjn)},{},{}]
self.memory[label] = 0
else:
self.data[label].append(joints[j])
#print('len(self.data[label]):{0}'.format(len(self.data[label])))
if len(
self.data[label]
) == settings.L: #self.data[label]:[16,{18,key:(x,y)}]
for i in range(settings.L):
keys = self.data[label][i].keys()
keys = sorted(keys)
for key in keys:
self.one_frame.append(
np.array(self.data[label][i][key]))
if label not in self.pred_data:
self.pred_data[label] = [
np.array(self.one_frame)
]
else:
self.pred_data[label].append(
np.array(self.one_frame)
) #[[[x0 y0],..[x17 y17]],..[]]
self.one_frame = []
self.pred_data[label] = np.array(
self.pred_data[label])
pred = sk_cnn_actionPredicter.predict(
self.pred_data[label])
#print('预测动作下标:{0}'.format(pred))
self.pred_data[label] = []
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)
#print('预测动作类别:{0}'.format(settings.move_status[pred]))
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)
end = time.time()
self.fps = 1. / (end - start)
cv2.putText(show, 'FPS: %.2f' % self.fps, (30, 30),
cv2.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))
def show_camera(self, frame):
start = time.time()
# ret, frame = self.cap.read()
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
if ret:
if self.__flag_mode == 1:
humans = poseEstimator.inference(show)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
elif self.__flag_mode == 2:
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)
# This is the part to get the kt points
elif self.__flag_mode == 3:
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)
cv2.imshow('window', cv2.resize(show, (432, 368)))
self.out.write(show) # Write out frame to video
end = time.time()
def _perform_recognition_(self):
self.predictor = actionPredictor()
self.humans = self.estimator.inference(self.image)
self.image, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
self.image, self.humans, imgcopy=False)
height = self.image.shape[0]
width = self.image.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])
#logger.debug('label is: %d' % (label))
# Locate the current person object in current frame
# Iterated thru xcenter for finding minimum distance between object center coord
try:
j = np.argmin(
np.array(
[abs(i - (xmax + xmin) / 2.) for i in xcenter]))
except:
j = 0
# Check if major skeleton points are existing
if self._joint_filter_(joints[j]):
joints[j] = self._joint_complete_(
self._joint_complete_(joints[j]))
if label not in self.data:
#logger.debug('label is: %d' % (label))
self.data[label] = [joints[j]]
self.memory[label] = 0
else:
self.data[label].append(joints[j])
if len(self.data[label]) == self.step:
pred = self.predictor.move_status(self.data[label])
#logger.debug(len(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]
#location = functools.reduce(lambda x, y: x + y, self.data[label][:][1]) / len(self.data[label][:][1])
#location = sum(self.data[label][:][1]) / float(len(self.data[label][:][1]))
if location[0] <= 30:
location = (51, location[1])
if location[1] <= 10:
location = (location[0], 31)
cv2.putText(self.image, self.move_status[pred],
(location[0] - 30, location[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(255, 255, 255), 2)
cv2.rectangle(
self.image, (xmin, ymin), (xmax, ymax),
(int(self.c[label % 32, 0]), int(
self.c[label % 32, 1]), int(self.c[label % 32, 2])), 4)
def _perform_estimation_(self):
self.humans = self.estimator.inference(self.image)
self.image = TfPoseEstimator.draw_humans(self.image,
self.humans,
imgcopy=False)
class actions(object):
def __init__(self, arguments):
self.arguments = arguments
# Frame window dim
self.winWidth = 640
self.winHeight = 480
actionPredictor_params.__init__(self)
self.fps_time = 0
#self.step = 15
self.mode = {
'Pose Estimation': 'estimation',
'Tracking': 'tracking',
'Action Recognition': 'recognition'
}
w, h = model_wh(self.arguments.resize)
if w > 0 and h > 0:
self.estimator = TfPoseEstimator(get_graph_path(
self.arguments.model),
target_size=(w, h))
else:
self.estimator = TfPoseEstimator(get_graph_path(
self.arguments.model),
target_size=(432, 368))
self.cam = cv2.VideoCapture(self.arguments.camera)
# Tracker based on Sort
self.sort_max_age = 20
self.sort_min_hit = 3
self.tracker = Sort(self.sort_max_age, self.sort_min_hit)
def proceed(self):
self._read_frame_()
if self.ret_val and self.arguments.mode == self.mode['Pose Estimation']:
self._perform_estimation_()
elif self.ret_val and self.arguments.mode == self.mode['Tracking']:
self._perform_tracking_()
elif self.ret_val and self.arguments.mode == self.mode[
'Action Recognition']:
self._perform_recognition_()
else:
sys.exit(
'Abort...please choose correct action mode from "estimation" "tracking" "recognition"'
)
self._output_()
def _joint_filter_(self, joint):
if 1 not in joint:
return False
# Check exist of hip
if 8 not in joint and 11 not in joint:
return False
# Check exist of knee
if 9 not in joint and 12 not in joint:
return False
return True
def _joint_complete_(self, joint):
if 8 in joint and 11 not in joint:
joint[11] = joint[8]
elif 8 not in joint and 11 in joint:
joint[8] = joint[11]
if 9 in joint and 12 not in joint:
joint[12] = joint[9]
elif 9 not in joint and 12 in joint:
joint[9] = joint[12]
return joint
def _perform_estimation_(self):
self.humans = self.estimator.inference(self.image)
self.image = TfPoseEstimator.draw_humans(self.image,
self.humans,
imgcopy=False)
def _perform_tracking_(self):
self.humans = self.estimator.inference(self.image)
self.image, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
self.image, self.humans, imgcopy=False)
height = self.image.shape[0]
width = self.image.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(
self.image, (xmin, ymin), (xmax, ymax),
(int(self.c[label % 32, 0]), int(
self.c[label % 32, 1]), int(self.c[label % 32, 2])), 4)
def _perform_recognition_(self):
self.predictor = actionPredictor()
self.humans = self.estimator.inference(self.image)
self.image, joints, bboxes, xcenter, sk = TfPoseEstimator.get_skeleton(
self.image, self.humans, imgcopy=False)
height = self.image.shape[0]
width = self.image.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])
#logger.debug('label is: %d' % (label))
# Locate the current person object in current frame
# Iterated thru xcenter for finding minimum distance between object center coord
try:
j = np.argmin(
np.array(
[abs(i - (xmax + xmin) / 2.) for i in xcenter]))
except:
j = 0
# Check if major skeleton points are existing
if self._joint_filter_(joints[j]):
joints[j] = self._joint_complete_(
self._joint_complete_(joints[j]))
if label not in self.data:
#logger.debug('label is: %d' % (label))
self.data[label] = [joints[j]]
self.memory[label] = 0
else:
self.data[label].append(joints[j])
if len(self.data[label]) == self.step:
pred = self.predictor.move_status(self.data[label])
#logger.debug(len(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]
#location = functools.reduce(lambda x, y: x + y, self.data[label][:][1]) / len(self.data[label][:][1])
#location = sum(self.data[label][:][1]) / float(len(self.data[label][:][1]))
if location[0] <= 30:
location = (51, location[1])
if location[1] <= 10:
location = (location[0], 31)
cv2.putText(self.image, self.move_status[pred],
(location[0] - 30, location[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(255, 255, 255), 2)
cv2.rectangle(
self.image, (xmin, ymin), (xmax, ymax),
(int(self.c[label % 32, 0]), int(
self.c[label % 32, 1]), int(self.c[label % 32, 2])), 4)
def _read_frame_(self):
self.ret_val, self.image = self.cam.read()
self.image = cv2.resize(self.image, (self.winWidth, self.winHeight))
def _output_(self):
# Calculate frame averaging step
FPS = float(1.0 / (time.time() - self.fps_time))
#logger.debug('FPS: %f' % FPS)
#self.step = int(0.7 * FPS)
#logger.debug('step: %d' % self.step)
cv2.putText(self.image,
"FPS: %f" % (1.0 / (time.time() - self.fps_time)),
(10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255),
2)
cv2.imshow('tf-pose-estimation result', self.image)
self.fps_time = time.time()
def show_video(self):
self.video_event = True
start = time.time()
cap = cv2.VideoCapture('22_360p.mp4')
while (cap.isOpened()):
ret, frame = cap.read()
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
if ret:
if self.__flag_mode == 1:
self.infoBox.setText(u'현재 인간 포즈 추정 모드')
humans = poseEstimator.inference(show)
show = TfPoseEstimator.draw_humans(show,
humans,
imgcopy=False)
elif self.__flag_mode == 2:
self.infoBox.setText(u'현재 다중 인간 포즈 추정 모드')
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'현재 인간 행동 인식 모드')
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)
end = time.time()
self.fps = 1. / (end - start)
cv2.putText(show, 'FPS: %.2f' % self.fps, (30, 30),
cv2.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))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
def show_camera(self):
start = time.time()
ret, frame = self.cap.read()
show = cv2.resize(frame, (settings.winWidth, settings.winHeight))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
if ret:
if self.__flag_mode == 1:
self.infoBox.setText(u'当前为人体姿态估计模式')
humans = poseEstimator.inference(show)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
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)
show = TfPoseEstimator.draw_humans(show, humans, imgcopy=False)
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
alert = False
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)
## Use simsum.ttc to write Chinese.
b, g, r, a = 0, 255, 0, 0
img_pil = Image.fromarray(show)
draw = ImageDraw.Draw(img_pil)
# print('pred', pred)
if pred == 8:
draw.text((location[0] - 30, location[1] - 10), settings.move_status[pred], font=self.font, fill=(255, 0, 0, a))
alert = True
else:
draw.text((location[0] - 30, location[1] - 10), settings.move_status[pred], font=self.font, fill=(b, g, r, a))
show = np.array(img_pil)
# cv2.putText(show, settings.move_status[pred], (location[0] - 30, location[1] - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.8,
# (0, 255, 0), 2)
if alert:
colors = (255, 0, 0)
else:
colors = (int(settings.c[label % 32, 0]),
int(settings.c[label % 32, 1]),
int(settings.c[label % 32, 2]))
cv2.rectangle(show, (xmin, ymin), (xmax, ymax),
colors, 4)
if alert:
cv2.imwrite('files/alerts/fall_' + str(int(time.time())) + '.png', show)
end = time.time()
self.fps = 1. / (end - start)
cv2.putText(show, 'FPS: %.2f' % self.fps, (30, 30), cv2.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))
fps_time = 0
if __name__ == '__main__':
# 类别以及要保存的视频段长度
action = 'satnd'
clip_length = 90
root_path = '/home/dl1/datasets/actions/'
if not os.path.exists(root_path + action):
os.mkdir(root_path + action)
if not os.path.exists(root_path + action + '/txt/'):
os.mkdir(root_path + action + '/txt/')
os.mkdir(root_path + action + '/imgs/')
samples = len(os.listdir(root_path + action + '/txt/'))
sample_count = 1000 if samples == 0 else 1000 + samples
e = TfPoseEstimator(get_graph_path('mobilenet_thin'),
target_size=(432, 368))
cam = cv2.VideoCapture(0)
ret_val, image = cam.read()
joints = []
joints_imgs = []
while True:
ret_val, image = cam.read()
if ret_val:
humans = e.inference(image)
image, joint, *_, sk = TfPoseEstimator.get_humans(image,
humans,
imgcopy=False)
if joint:
if len(joints) < clip_length:
joints.append(joint[0])
joints_imgs.append(sk)
