def run(self):
# 初始化阶段不执行识别逻辑
if not self._event.isSet():
self.changeText.emit("The system is initializing.")
self._event.wait()
# 获取识别对象
self._gesture_rec = GestureRec(server_address=self._args.server_address, upload_size=UPLOAD_SIZE,
frame_distance=self._frame_distance[0], queue_draw=self._queue_draw, pool=self._pool)
frame_total = 0
upload_total = 0
if not self._cap_video.isOpened():
self._cap_video = cv2.VideoCapture(self._args.device)
self.changeText.emit("The system has been restarted.")
while self._cap_video.isOpened():
if self.flag == 1:
ret, frame = self._cap_video.read()
frame_total += 1
if ret:
upload_total = self._gesture_rec.check_upload(frame_total, upload_total, frame, self._is_upload)
category_dict = self._gesture_rec.check_draw_text()
if category_dict is not None:
print(category_dict)
# 计算剩余进度条值
divided = self._processbar_generator(category_dict)
divided_index = 0
# 显示非预测分类结果
if divided is None:
self.changeText.emit(list(category_dict)[0])
continue
# 显示文本
show_category = max(category_dict, key=category_dict.get)
self.changeText.emit(show_category)
if show_category in ["No gesture", "Doing other things"]:
for category in lists:
if category in ["No gesture", "Doing other things"]:
continue
self.changeProcessbar.emit((lists.index(category), 0))
continue
else:
# 显示分类图框框
self.changeBorder.emit(lists.index(show_category))
# 同时更新12个进度条
for category in lists:
if category in ["No gesture", "Doing other things"]:
continue
if category in category_dict:
self.changeProcessbar.emit((lists.index(category), category_dict[category]))
else:
try:
self.changeProcessbar.emit((lists.index(category), divided[divided_index]))
divided_index += 1
except Exception as e:
print(e)
self._show_frame(frame)
else:
# 初始化
self._is_upload = [False]
self.flag = True
self._show_picture("resource/white.jpg")
self.changeText.emit("Stop")
self._cap_video.release()
class Thread(QThread):
changePixmap = pyqtSignal(QPixmap)
changePicture = pyqtSignal(QPixmap)
changeProcessbar = pyqtSignal(tuple)
changeText = pyqtSignal(str)
listenkey = pyqtSignal(str)
changeBorder = pyqtSignal(int)
changeBorderToNormal = pyqtSignal()
def __init__(self):
super(Thread, self).__init__()
self._args = parser.parse_args()
# 监听键盘
self.listenkey.connect(self.keyPress)
# 获取摄像头句柄
self._cap_video = cv2.VideoCapture(self._args.device)
# 初始化事件
self._event = Event()
# 显示结果队列
self._queue_draw = queue.Queue()
# 开启线程池
self._pool = ThreadPoolExecutor(max_workers=POOL_SIZE)
# 获取上传帧间距
self._pool.submit(self._get_upload_distance, self._event)
# 子线程初始化
self._frame_distance = list()
# 上传标志位
self._is_upload = [False]
# 系统是否关闭的标志
self.flag = True
def run(self):
# 初始化阶段不执行识别逻辑
if not self._event.isSet():
self.changeText.emit("The system is initializing.")
self._event.wait()
# 获取识别对象
self._gesture_rec = GestureRec(server_address=self._args.server_address, upload_size=UPLOAD_SIZE,
frame_distance=self._frame_distance[0], queue_draw=self._queue_draw, pool=self._pool)
frame_total = 0
upload_total = 0
if not self._cap_video.isOpened():
self._cap_video = cv2.VideoCapture(self._args.device)
self.changeText.emit("The system has been restarted.")
while self._cap_video.isOpened():
if self.flag == 1:
ret, frame = self._cap_video.read()
frame_total += 1
if ret:
upload_total = self._gesture_rec.check_upload(frame_total, upload_total, frame, self._is_upload)
category_dict = self._gesture_rec.check_draw_text()
if category_dict is not None:
print(category_dict)
# 计算剩余进度条值
divided = self._processbar_generator(category_dict)
divided_index = 0
# 显示非预测分类结果
if divided is None:
self.changeText.emit(list(category_dict)[0])
continue
# 显示文本
show_category = max(category_dict, key=category_dict.get)
self.changeText.emit(show_category)
if show_category in ["No gesture", "Doing other things"]:
for category in lists:
if category in ["No gesture", "Doing other things"]:
continue
self.changeProcessbar.emit((lists.index(category), 0))
continue
else:
# 显示分类图框框
self.changeBorder.emit(lists.index(show_category))
# 同时更新12个进度条
for category in lists:
if category in ["No gesture", "Doing other things"]:
continue
if category in category_dict:
self.changeProcessbar.emit((lists.index(category), category_dict[category]))
else:
try:
self.changeProcessbar.emit((lists.index(category), divided[divided_index]))
divided_index += 1
except Exception as e:
print(e)
self._show_frame(frame)
else:
# 初始化
self._is_upload = [False]
self.flag = True
self._show_picture("resource/white.jpg")
self.changeText.emit("Stop")
self._cap_video.release()
def _processbar_generator(self, category_dict):
"""剩余进度条生成随机值
算法：
1. 构造一个N+1项的数组，第一项为0，最后一项为max
2. 在[1, L)中随机选取N-1个不重复的正整数，并排序
3. 所有的数组相邻两项的差值即为生成值
"""
total = 100 - sum(category_dict.values())
if total > 100:
return None
nums = 12 - len(category_dict)
if "No gesture" in category_dict:
nums += 1
# 当总值少于分配个数时，直接返回
if total < nums:
res = [0] * nums
res[random.randint(0, nums - 1)] += total
return res
divided = []
stick = [0] + random.sample(range(0, total), nums - 1) + [total]
stick.sort()
for i in range((len(stick) - 1)):
divided.append((stick[i + 1] - stick[i]))
return divided
def keyPress(self, str):
if str == "S":
if self._is_upload[0]:
# 动作结束，预测
self._is_upload[0] = False
self._gesture_rec.end_action()
else:
# 动作开始，上传图片
self.changeBorderToNormal.emit()
self.changeText.emit("Start")
self._is_upload[0] = True
self._gesture_rec.start_action()
def _show_frame(self, frame):
"""帧画面显示于QT界面"""
try:
rgbImage = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1], rgbImage.shape[0], QImage.Format_RGB888)
convertToQtFormat = QPixmap.fromImage(convertToQtFormat)
self.changePixmap.emit(convertToQtFormat)
except Exception as e:
pass
def _show_picture(self, picture):
try:
img = cv2.imread(picture)
rgbImage = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1], rgbImage.shape[0], QImage.Format_RGB888)
convertToQtFormat = QPixmap.fromImage(convertToQtFormat)
self.changePicture.emit(convertToQtFormat)
except Exception as e:
pass
def _get_upload_distance(self, event):
"""计算保存帧画面间距"""
# Number of frames to capture
num_frames = 120
print("Initialization...")
print("Capturing {0} frames".format(num_frames))
# Start time
start = time.time()
# Grab a few frames
for i in range(0, num_frames):
ret, frame = self._cap_video.read()
# End time
end = time.time()
# Time elapsed
seconds = end - start
print("Time taken : {0} seconds".format(seconds))
# Calculate frames per second
fps = num_frames / seconds
distance = math.ceil(fps / 5)
print("Estimated frames per second : {0}".format(fps))
print("Save distance: %s" % distance)
self._frame_distance.append(distance)
self.changeText.emit("System initialization is complete.")
event.set()
def stop(self):
self.flag = 0
class Thread(QThread):
changePixmap = pyqtSignal(QPixmap)
changeOpencv = pyqtSignal(QPixmap)
changePicture = pyqtSignal(QPixmap)
changeProcessbar = pyqtSignal(tuple)
changeText = pyqtSignal(str)
changeBorder = pyqtSignal(int)
changeBorderToNormal = pyqtSignal()
def __init__(self):
super(Thread, self).__init__()
self._args = parser.parse_args()
# 获取摄像头句柄
self._cap_video = cv2.VideoCapture(self._args.device)
# 显示结果队列
self._queue_draw = queue.Queue()
# 初始化事件
self._event = Event()
# 开启线程池
self._pool = ThreadPoolExecutor(max_workers=POOL_SIZE)
# 子线程初始化
self._frame_distance = list()
self._pool.submit(self._get_upload_distance, self._event)
# 选择的帧差方法
self._method = self._args.method
# 阈值
self._threshold = self._args.threshold
if self._method == "knn":
self._detector = KNNMotionDetector()
self._es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
else:
self._detector = Mog2MotionDetector()
self._mask_analyser = BinaryMaskAnalyser()
# 系统启动标志
self._new = True
# 系统是否关闭的标志
self.flag = True
# 上传标志位
self._is_upload = [None]
def run(self):
# 初始化阶段不执行识别逻辑
if not self._event.isSet():
self.changeText.emit("系统正在初始化，请稍等...")
self._event.wait()
# 获取识别对象
self._gesture_rec = GestureRec(
server_address=self._args.server_address,
upload_size=UPLOAD_SIZE,
frame_distance=self._frame_distance[0],
queue_draw=self._queue_draw,
pool=self._pool)
frame_total = 0
upload_total = 0
if not self._cap_video.isOpened():
self._cap_video = cv2.VideoCapture(self._args.device)
self.changeText.emit("系统已重启")
while self._cap_video.isOpened():
if self.flag == 1:
ret, frame = self._cap_video.read()
frame_total += 1
if self._method == "knn":
mask = self._detector.returnMask(frame)
# dilated = cv2.dilate(mask, self._es, iterations=2) # 形态学膨胀
else:
mask = self._detector.returnGreyscaleMask(frame)
mask = cv2.merge([mask, mask, mask])
if ret:
upload_total = self._gesture_rec.check_upload(
frame_total, upload_total, frame, self._is_upload)
category_dict = self._gesture_rec.check_draw_text()
if category_dict is not None:
print(category_dict)
# 计算剩余进度条值
divided = self._processbar_generator(category_dict)
divided_index = 0
# 显示非预测分类结果
if divided is None:
self.changeText.emit(list(category_dict)[0])
continue
# 显示文本
show_category = max(category_dict,
key=category_dict.get)
self.changeText.emit(show_category)
if show_category == "非手势动作":
for category in lists:
if category == "非手势动作":
continue
self.changeProcessbar.emit(
(lists.index(category), 0))
continue
else:
# 显示分类图框框
self.changeBorder.emit(lists.index(show_category))
# 同时更新12个进度条
for category in lists:
if category == "非手势动作":
continue
if category in category_dict:
self.changeProcessbar.emit(
(lists.index(category),
category_dict[category]))
else:
self.changeProcessbar.emit(
(lists.index(category),
divided[divided_index]))
divided_index += 1
found = False
exception = False
if self._mask_analyser.returnNumberOfContours(mask) > 0:
x, y, w, h = self._mask_analyser.returnMaxAreaRectangle(
mask)
if w + h > self._threshold:
# print(x, y, w, h)
found = True
# cv2.rectangle(frame, (x, y), (x + w, y + h), [0, 255, 0], 2)
if w + h > frame.shape[0] + frame.shape[1] - 10:
exception = True
if not self._new and self._is_upload[0] is None and (
found and not exception):
# 动作开始，上传图片
self._is_upload[0] = True # 标识识别开始
self.changeBorderToNormal.emit()
self.changeText.emit("请开始做动作")
self._gesture_rec.start_action()
if not self._new and self._is_upload[0] and (not found
or exception):
# 动作结束，预测
self._is_upload[0] = False # 标识识别结束
self._gesture_rec.end_action(is_upload=self._is_upload)
if self._new and not found:
self._new = False
self._show_opencv(mask)
self._show_frame(frame)
else:
# 初始化
self._new = True
self.flag = True
self._is_upload = [None]
self._show_picture("resource/white.jpg")
self.changeText.emit("系统停止")
self._cap_video.release()
def _processbar_generator(self, category_dict):
"""剩余进度条生成随机值
算法：
1. 构造一个N+1项的数组，第一项为0，最后一项为max
2. 在[1, L)中随机选取N-1个不重复的正整数，并排序
3. 所有的数组相邻两项的差值即为生成值
"""
total = 100 - sum(category_dict.values())
if total > 100:
return None
nums = 12 - len(category_dict)
if "非手势动作" in category_dict:
nums += 1
# 当总值少于分配个数时，直接返回
if total < nums:
res = [0] * nums
res[random.randint(0, nums - 1)] += total
return res
divided = []
stick = [0] + random.sample(range(0, total), nums - 1) + [total]
stick.sort()
for i in range((len(stick) - 1)):
divided.append((stick[i + 1] - stick[i]))
return divided
def _show_frame(self, frame):
"""帧画面显示于QT界面"""
try:
rgbImage = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1],
rgbImage.shape[0], QImage.Format_RGB888)
convertToQtFormat = QPixmap.fromImage(convertToQtFormat)
self.changePixmap.emit(convertToQtFormat)
except Exception as e:
pass
def _show_opencv(self, frame):
#try:
#rgbImage = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
convertToQtFormat = QImage(frame.data, frame.shape[1], frame.shape[0],
QImage.Format_Indexed8)
convertToQtFormat = QPixmap.fromImage(convertToQtFormat)
self.changeOpencv.emit(convertToQtFormat)
#except Exception as e:
#pass
def _show_picture(self, picture):
try:
img = cv2.imread(picture)
rgbImage = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1],
rgbImage.shape[0], QImage.Format_RGB888)
convertToQtFormat = QPixmap.fromImage(convertToQtFormat)
self.changePicture.emit(convertToQtFormat)
except Exception as e:
pass
def _get_upload_distance(self, event):
"""计算保存帧画面间距"""
# Number of frames to capture
num_frames = 120
print("系统初始化...")
print("Capturing {0} frames".format(num_frames))
# Start time
start = time.time()
# Grab a few frames
for i in range(0, num_frames):
ret, frame = self._cap_video.read()
# End time
end = time.time()
# Time elapsed
seconds = end - start
print("Time taken : {0} seconds".format(seconds))
# Calculate frames per second
fps = num_frames / seconds
distance = math.ceil(fps / 5)
print("Estimated frames per second : {0}".format(fps))
print("Save distance: %s" % distance)
self._frame_distance.append(distance)
self.changeText.emit("系统初始化完毕")
event.set()
def stop(self):
self.flag = 0
def run(self):
# 初始化阶段不执行识别逻辑
if not self._event.isSet():
self.changeText.emit("系统正在初始化，请稍等...")
self._event.wait()
# 获取识别对象
self._gesture_rec = GestureRec(
server_address=self._args.server_address,
upload_size=UPLOAD_SIZE,
frame_distance=self._frame_distance[0],
queue_draw=self._queue_draw,
pool=self._pool,
tfnet=self._tfnet[0],
queue_detect=self._queue_detect)
frame_total = 0
upload_total = 0
if not self._cap_video.isOpened():
self._cap_video = cv2.VideoCapture(self._args.device)
self.changeText.emit("系统已重启")
while self._cap_video.isOpened():
if self.flag == 1:
ret, frame = self._cap_video.read()
frame_total += 1
if ret:
self._gesture_rec.detection(frame_total, frame)
upload_total = self._gesture_rec.check_upload(
frame_total, upload_total, frame, self._is_upload)
category_dict = self._gesture_rec.check_draw_text()
if category_dict is not None:
print(category_dict)
# 计算剩余进度条值
divided = self._processbar_generator(category_dict)
divided_index = 0
# 显示非预测分类结果
if divided is None:
self.changeText.emit(list(category_dict)[0])
continue
# 显示文本
show_category = max(category_dict,
key=category_dict.get)
self.changeText.emit(show_category)
if show_category in [
"No gesture", "Doing other things"
]:
for category in lists:
if category in [
"No gesture", "Doing other things"
]:
continue
self.changeProcessbar.emit(
(lists.index(category), 0))
continue
else:
# 显示分类图框框
self.changeBorder.emit(lists.index(show_category))
# 同时更新12个进度条
for category in lists:
if category in [
"No gesture", "Doing other things"
]:
continue
if category in category_dict:
self.changeProcessbar.emit(
(lists.index(category),
category_dict[category]))
else:
self.changeProcessbar.emit(
(lists.index(category),
divided[divided_index]))
divided_index += 1
if self._is_upload[
0] is None and self._queue_detect.getAliveSum(
) >= DETECT_FRAMES_START_ACTION:
# 动作开始，上传图片
self._is_upload[0] = True # 标识识别开始
self.changeBorderToNormal.emit()
self.changeText.emit("请开始做动作")
self._gesture_rec.start_action()
#print(self._queue_detect.getAliveSum())
#print(DETECT_FRAMES_TOTAL - DETECT_FRAMES_END_ACTION)
#if self._is_upload[0] and self._queue_detect.getAliveSum() <= (DETECT_FRAMES_TOTAL - DETECT_FRAMES_END_ACTION):
print('@', self._is_upload[0])
print('@@', self._queue_detect.getAliveSum())
print('@@@',
(DETECT_FRAMES_TOTAL - DETECT_FRAMES_END_ACTION))
if self._is_upload[0] and self._queue_detect.getAliveSum(
) <= (DETECT_FRAMES_TOTAL - DETECT_FRAMES_END_ACTION):
# 动作结束，预测
self._is_upload[0] = False # 标识识别结束
self._gesture_rec.end_action(is_upload=self._is_upload)
self._show_frame(frame)
else:
# 初始化
self.flag = True
self._is_upload = [None]
self._show_picture("resource/white.jpg")
self.changeText.emit("系统停止")
self._cap_video.release()
def run(self):
# 初始化阶段不执行识别逻辑
if not self._event.isSet():
self.changeText.emit("系统正在初始化，请稍等...")
self._event.wait()
# 获取识别对象
self._gesture_rec = GestureRec(
server_address=self._args.server_address,
upload_size=UPLOAD_SIZE,
frame_distance=self._frame_distance[0],
queue_draw=self._queue_draw,
pool=self._pool)
frame_total = 0
upload_total = 0
if not self._cap_video.isOpened():
self._cap_video = cv2.VideoCapture(self._args.device)
self.changeText.emit("系统已重启")
while self._cap_video.isOpened():
if self.flag == 1:
ret, frame = self._cap_video.read()
frame_total += 1
if self._method == "knn":
mask = self._detector.returnMask(frame)
# dilated = cv2.dilate(mask, self._es, iterations=2) # 形态学膨胀
else:
mask = self._detector.returnGreyscaleMask(frame)
mask = cv2.merge([mask, mask, mask])
if ret:
upload_total = self._gesture_rec.check_upload(
frame_total, upload_total, frame, self._is_upload)
category_dict = self._gesture_rec.check_draw_text()
if category_dict is not None:
print(category_dict)
# 计算剩余进度条值
divided = self._processbar_generator(category_dict)
divided_index = 0
# 显示非预测分类结果
if divided is None:
self.changeText.emit(list(category_dict)[0])
continue
# 显示文本
show_category = max(category_dict,
key=category_dict.get)
self.changeText.emit(show_category)
if show_category == "非手势动作":
for category in lists:
if category == "非手势动作":
continue
self.changeProcessbar.emit(
(lists.index(category), 0))
continue
else:
# 显示分类图框框
self.changeBorder.emit(lists.index(show_category))
# 同时更新12个进度条
for category in lists:
if category == "非手势动作":
continue
if category in category_dict:
self.changeProcessbar.emit(
(lists.index(category),
category_dict[category]))
else:
self.changeProcessbar.emit(
(lists.index(category),
divided[divided_index]))
divided_index += 1
found = False
exception = False
if self._mask_analyser.returnNumberOfContours(mask) > 0:
x, y, w, h = self._mask_analyser.returnMaxAreaRectangle(
mask)
if w + h > self._threshold:
# print(x, y, w, h)
found = True
# cv2.rectangle(frame, (x, y), (x + w, y + h), [0, 255, 0], 2)
if w + h > frame.shape[0] + frame.shape[1] - 10:
exception = True
if not self._new and self._is_upload[0] is None and (
found and not exception):
# 动作开始，上传图片
self._is_upload[0] = True # 标识识别开始
self.changeBorderToNormal.emit()
self.changeText.emit("请开始做动作")
self._gesture_rec.start_action()
if not self._new and self._is_upload[0] and (not found
or exception):
# 动作结束，预测
self._is_upload[0] = False # 标识识别结束
self._gesture_rec.end_action(is_upload=self._is_upload)
if self._new and not found:
self._new = False
self._show_opencv(mask)
self._show_frame(frame)
else:
# 初始化
self._new = True
self.flag = True
self._is_upload = [None]
self._show_picture("resource/white.jpg")
self.changeText.emit("系统停止")
self._cap_video.release()