def __init__(self, frames=[], fps=20):
self.__callbacks = {}
self.fps = fps
self.playFps = 0
self.calculation = 0
if isinstance(frames, list): self.frames = [Image(f) for f in frames]
elif isinstance(frames, tuple) or isinstance(frames, list):
self.frames = [Image(frame) for frame in frames]
elif isinstance(frames, str):
self.load(frames)
else:
self.frames = []
def load(self, path):
cap = cv2.VideoCapture(path)
self.fps = cap.get(cv2.CAP_PROP_FPS)
self.frames = []
try:
while (cap.isOpened()):
ret, frame = cap.read()
if ret and not frame is None: self.frames.append(Image(frame))
else: break
finally:
cap.release()
return self
def play(self, loop=True, name='Video Frame'):
dataLoop = [True]
def __exit__():
dataLoop[0] = False
def __mouse__(e, x, y, f, p):
self.__call('mouse', x, y, __exit__)
cv2.namedWindow(name)
cv2.setMouseCallback(name, __mouse__)
lastException = None
while dataLoop[0]:
i = 0
while i < self.length:
frame = self.frames[i]
now = time.clock()
key = cv2.waitKey(1)
if key == 27: dataLoop[0] = False
elif key != -1:
self.__call('key#' + str(key), key, __exit__)
self.__call('keys', key, __exit__)
if not dataLoop[0]: break
rendered = frame.copy()
if 'elaborate' in self.__callbacks:
try:
tmp = self.__call('elaborate', rendered, i, __exit__)
rendered = Image(tmp) if tmp is not None else rendered
printedException = False
except Exception as ex:
exMessage = str(ex)
if exMessage != lastException:
print('video callback:', exMessage)
lastException = exMessage
rentered = frame
cv2.imshow(name, rendered.asByte().data)
self.calculation = time.clock() - now
sleepAmmount = (1.0 / self.fps -
self.calculation) if self.fps > 0 else 0
if self.fps < 240 and sleepAmmount > 0:
time.sleep(sleepAmmount)
self.playFps = 1.0 / (time.clock() - now + 0.0001)
if 'index' in self.__callbacks:
i = self.__call('index', i, __exit__)
else:
i += 1
if i < 0: i = self.length + i
dataLoop[0] = dataLoop[0] and loop
cv2.destroyWindow(name)
return self
def mode(self):
mode = np.stack(self.frames, axis=0)
mode = stats.mode(mode, axis=0)[0][0]
return Image(mode)
def min(self):
min = np.stack(self.frames, axis=0)
min = np.min(min, axis=0).astype(np.uint8)
return Image(min)
def max(self):
max = np.stack(self.frames, axis=0)
max = np.max(max, axis=0).astype(np.uint8)
return Image(max)
def mean(self):
mean = np.stack(self.frames, axis=0)
mean = np.mean(mean, axis=0).astype(np.uint8)
return Image(mean)