def read_video(self, path):
# Return: Numpy.ndarray 5-d tensor with shape (1, <No. of frames>, <height>, <width>, <channels>)
capt = FFmpegReader(filename=path)
self.fps = int(capt.inputfps)
list_of_frames = []
for index, frame in enumerate(capt.nextFrame()):
# frame -> (<height>, <width>, 3)
capture_frame = True
if self.required_fps != None:
is_valid = range(self.required_fps)
capture_frame = (index % self.fps) in is_valid
if capture_frame:
if self.target_size is not None:
temp_image = image.array_to_img(frame)
frame = image.img_to_array(
temp_image.resize(self.target_size,
Image.ANTIALIAS)).astype('uint8')
list_of_frames.append(frame)
temp_video = np.stack(list_of_frames)
capt.close()
if self.to_gray:
temp_video = rgb2gray(temp_video)
if self.max_frames is not None:
temp_video = self.process_video(video=temp_video)
return np.expand_dims(temp_video, axis=0)
def raw2gray(bayer_images):
"""RGBG -> linear RGB"""
# T H W C
lin_rgb = np.stack([
bayer_images[..., 0],
np.mean(bayer_images[..., [1, 3]], axis=3), bayer_images[..., 2]
],
axis=3)
lin_gray = rgb2gray(lin_rgb)
return lin_gray
def create_pkl(mp4path):
tgtpklpath = (mp4path.parent / (mp4path.stem + ".pkl"))
if not tgtpklpath.exists():
try:
images = rgb2gray(vread(mp4path)).astype(np.uint8).squeeze()
face_detector = FaceDetector()
faces = np.stack([
face_detector.crop_mouth(image, bounding_box_shape=(220, 150))
for image in images
], 0)
faces.dump(tgtpklpath.as_posix())
except Exception as e:
print("{}: {}".format(tgtpklpath, e))
def _read_video(self, path):
"""
Parameters:
path (str): Required
Path of the video to be read
Returns:
Numpy.ndarray
A 5-d tensor with shape (1, <No. of frames>, <height>, <width>, <channels>)
"""
cap = FFmpegReader(filename=path)
list_of_frames = []
self.fps = int(cap.inputfps) # Frame Rate
for index, frame in enumerate(cap.nextFrame()):
capture_frame = True
if self.required_fps != None:
is_valid = range(self.required_fps)
capture_frame = (index % self.fps) in is_valid
if capture_frame:
if self.target_size is not None:
temp_image = image.array_to_img(frame)
frame = image.img_to_array(
temp_image.resize(
self.target_size,
Image.ANTIALIAS)).astype('uint8')
# Shape of each frame -> (<height>, <width>, 3)
list_of_frames.append(frame)
temp_video = np.stack(list_of_frames)
cap.close()
if self.to_gray:
temp_video = rgb2gray(temp_video)
if self.max_frames is not None:
temp_video = self._process_video(video=temp_video)
return temp_video
def main(model_path, data_dir, prediction_path):
# Grab all the input videos
video_extensions = ["*.pgm", "*.mp4"]
videos = []
for extension in video_extensions:
video_paths = os.path.join(data_dir, extension)
videos += glob(video_paths)
# load Deep learning model
model = load_model(
model_path,
custom_objects={"dice_coef_multilabel": dice_coef_multilabel})
# Loop for each video
for vid in videos:
# Define video's specific parameters
vid_name = os.path.split(vid)[1] # with extension
vid_ext = os.path.splitext(vid_name)[1]
output_path = prediction_path + os.path.splitext(vid_name)[0] + ".npy"
vid_reader = skv.FFmpegReader(vid)
num_frames = vid_reader.getShape()[0]
batch_array = np.zeros((num_frames, 240, 320, 4)).astype(np.uint8)
print("Current video:", vid)
print("with num_frames = ", num_frames)
# Loop for each frame
for idx, frame in enumerate(vid_reader.nextFrame()):
# Printing progress
print("\rNow is at %d/%d" % (idx, num_frames), end="", flush=True)
# Preprocessing before DL network
if vid_ext == ".pgm" or vid_name == "4.5mA_0.0ms_180s.mp4" or "max" in vid_name:
frame = frame[:, 36:356, :]
frame_gray = rgb2gray(frame) / 255
prediction = model.predict(frame_gray)
batch_array[idx, ] = (prediction[0, ] * 255).astype(np.uint8)
print("\n")
print("Saving prediction: ", output_path)
np.save(output_path, batch_array)
def blockMotion(videodata, method='DS', mbSize=8, p=2, **plugin_args):
videodata = vshape(videodata)
# grayscale
luminancedata = rgb2gray(videodata)
numFrames, height, width, channels = luminancedata.shape
assert numFrames > 1, "Must have more than 1 frame for motion estimation!"
# luminance is 1 channel, so flatten for computation
luminancedata = luminancedata.reshape((numFrames, height, width))
motionData = np.zeros((numFrames - 1, np.int(height / mbSize), np.int(width / mbSize), 2), np.int8)
if method == "N3SS":
for i in range(numFrames - 1):
motion, comps = _N3SS(luminancedata[i + 1, :, :], luminancedata[i, :, :], mbSize, p)
motionData[i, :, :, :] = motion
else:
raise NotImplementedError
return motionData
import numpy as np
from skvideo.io import vread, vwrite
from skvideo.utils import rgb2gray
import os
if __name__ == '__main__':
orig_path = r'C:\Users\dekel\Desktop\MP4_Vids\DCM10'
orig_Dir = os.listdir(orig_path)
for folder in np.arange(len(orig_Dir)):
in_path = orig_path + '/' + orig_Dir[folder]
Dir = os.listdir(in_path)
out_path = r'C:\Users\dekel\Desktop\MP4_Vids\DCM10_cut/' + orig_Dir[
folder] + '_cut'
if not os.path.exists(out_path):
os.makedirs(out_path)
for file in np.arange(len(Dir)):
path = in_path + '/' + Dir[file]
vid = vread(path)
vid = rgb2gray(vid)
Video = vid[:, 54:354, 240:540]
Video[:, :45, :45] = 0
vwrite(out_path + '/' + Dir[file], Video)
print('Done!')
tmp = 0
for i in tqdm(range(len(train_files))):
file = train_files[i]
vid_path = path + '/' + assign[train_targets[i]] + '/' + file
ind_frames = []
frames_count = 5
while frames_count != 205:
img = cv2.imread(vid_path + '/frame-' + str(frames_count) + '.png', 1)
img = cv2.resize(img, (160, 120))
# print(vid_path+'/frame-'+str(frames_count)+'.png')
frames_count = frames_count + 5
tmp = tmp + 1
ind_frames.append(img)
ind_frames = rgb2gray(ind_frames)
train_frames.append(ind_frames)
train_frames = np.array(train_frames)
print(train_frames.shape)
# In[5]:
import numpy as np
from skvideo.utils import rgb2gray
import cv2
from tqdm import tqdm
test_frames = []
tmp = 0
for i in tqdm(range(len(test_files))):
def __call__(self, sample):
for channel in sample:
if channel['modality'] == 'video':
channel['features'] = su.rgb2gray(channel['features'])
return sample
def plot_video(self,
output_video_path,
output_record_path,
update_template=False):
# Initialise writer and recorder
self.vid_writer = skv.FFmpegWriter(output_video_path)
self.record_fh = open(output_record_path, "w")
self.record_fh.write("frame,rotation\n")
# Initialise counter
idx = 0
# Loop for each frame
for pred, pred_masked, frame in zip(self.predictions,
self.predictions_masked,
self.vid_reader.nextFrame()):
# Print progress
print("\rNow is at %d/%d" % (idx, self.vid_shape[0]),
end="",
flush=True)
# Initialize maps and frames
frame = img_as_float(frame) # frame ~ (240, 320, 3)
frame_gray = rgb2gray(frame)[0, :, :, 0] # frame_gray ~ (240, 320)
frame_rgb = np.zeros(frame.shape) # frame_rgb ~ (240, 320, 3)
frame_rgb[:, :, :] = frame_gray.reshape(frame_gray.shape[0],
frame_gray.shape[1], 1)
useful_map, (pupil_map, _, _, _) = getSegmentation_fromDL(
pred
) # useful_map is used for polar transformation and cross-correlation
_, (pupil_map_masked, iris_map_masked, glints_map_masked,
visible_map_masked) = getSegmentation_fromDL(pred_masked)
rr, _, centre, _, _, _, _, _ = fit_ellipse(pupil_map, 0.5)
# Cross-correlation
if idx == 0:
polar_pattern_template, polar_pattern_template_longer, r_template, theta_template, extra_radian = genPolar(
frame_gray,
useful_map,
center=centre,
template=True,
filter_sigma=100,
adhist_times=2)
rotated_info = (polar_pattern_template, r_template,
theta_template)
rotation = 0
elif rr is not None:
# for finding the rotation value and determine if it is needed to update
rotation, rotated_info, _ = findTorsion(
polar_pattern_template_longer,
frame_gray,
useful_map,
center=centre,
filter_sigma=100,
adhist_times=2)
if (update_template == True) and rotation == 0:
polar_pattern_template, polar_pattern_template_longer, r_template, theta_template, extra_radian = genPolar(
frame_gray,
useful_map,
center=centre,
template=True,
filter_sigma=100,
adhist_times=2)
else:
rotation, rotated_info = np.nan, None
self.rotation_results.append(rotation)
self.record_fh.write("{},{}\n".format(idx, rotation))
# Drawing the frames of visualisation video
rotation_plot_arr = self._plot_rotation_curve(idx)
segmented_frame = self._draw_segmented_area(
frame_gray, pupil_map_masked, iris_map_masked,
glints_map_masked, visible_map_masked)
polar_transformed_graph_arr = self._plot_polar_transformed_graph(
(polar_pattern_template, r_template, theta_template),
rotated_info, extra_radian)
frames_to_draw = (frame_rgb, rotation_plot_arr, segmented_frame,
polar_transformed_graph_arr)
final_output = self._build_final_output_frame(frames_to_draw)
self.vid_writer.writeFrame(final_output)
idx += 1
self.delete_handles()
del self.predictions
del self.predictions_masked
