def hockeyLoadData(shuffle=True):
path_violence = constants.PATH_HOCKEY_FRAMES_VIOLENCE
path_non_violence = constants.PATH_HOCKEY_FRAMES_NON_VIOLENCE
# if not os.path.exists(os.path.join(constants.PATH_HOCKEY_README, 'all_data_labels_numFrames.csv')):
# datasetAll, labelsAll, numFramesAll = initializeDataset.createDataset(path_violence, path_non_violence, shuffle) #shuffle
# all_data = zip(datasetAll, labelsAll, numFramesAll)
# save_csvfile_multicolumn(all_data, os.path.join(constants.PATH_HOCKEY_README, 'all_data_labels_numFrames.csv'))
# else:
# datasetAll, labelsAll, numFramesAll = read_csvfile_threecolumns(os.path.join(constants.PATH_HOCKEY_README, 'all_data_labels_numFrames.csv'))
# return datasetAll, labelsAll, numFramesAll
if not os.path.exists(
os.path.join(constants.PATH_HOCKEY_README,
'all_data_labels_numFrames.csv')):
datasetAll, labelsAll, numFramesAll = [], [], []
v_videos = os.listdir(path_violence)
v_videos = sortListByStrNumbers(v_videos)
nv_videos = os.listdir(path_non_violence)
nv_videos = sortListByStrNumbers(nv_videos)
for vd in v_videos:
d = os.path.join(path_violence, vd)
if not os.path.isdir(d):
continue
datasetAll.append(d)
labelsAll.append(1)
# imagesNoF = []
for vd in nv_videos:
d = os.path.join(path_non_violence, vd)
if not os.path.isdir(d):
continue
datasetAll.append(d)
labelsAll.append(0)
# Dataset = imagesF + imagesNoF
# Labels = list([1] * len(imagesF)) + list([0] * len(imagesNoF))
numFramesAll = [
len(glob.glob1(datasetAll[i], "*.jpg"))
for i in range(len(datasetAll))
]
if shuffle:
combined = list(zip(datasetAll, labelsAll, numFramesAll))
random.shuffle(combined)
datasetAll[:], labelsAll[:], numFramesAll[:] = zip(*combined)
all_data = zip(datasetAll, labelsAll, numFramesAll)
save_csvfile_multicolumn(
all_data,
os.path.join(constants.PATH_HOCKEY_README,
'all_data_labels_numFrames.csv'))
else:
datasetAll, labelsAll, numFramesAll = read_csvfile_threecolumns(
os.path.join(constants.PATH_HOCKEY_README,
'all_data_labels_numFrames.csv'))
return datasetAll, labelsAll, numFramesAll
def __load_video_frames__(self, video_path, as_grey=False):
_, video_name = os.path.split(video_path)
imgs_paths = os.listdir(video_path)
imgs_paths = sortListByStrNumbers(imgs_paths)
frames = []
for i, path in enumerate(imgs_paths):
frame_path = os.path.join(video_path, path)
img = cv2.imread(frame_path)
if as_grey:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
frames.append(img)
return frames, imgs_paths
def __getitem__(self, idx):
vid_name = self.videos[idx]
label = self.labels[idx]
frames_names = os.listdir(vid_name)
frames_names = sortListByStrNumbers(frames_names)
frame_path = os.path.join(vid_name, frames_names[self.frame_idx])
frame = Image.open(frame_path)
frame = frame.convert("RGB")
# frame = np.array(frame)
frame = self.spatial_transform(frame)
return vid_name, frame, label, frame_path
def load_video_segment(path, init_frame, num_frames):
frames = os.listdir(path)
frames = sortListByStrNumbers(frames)
# print('NumFRames=', len(frames))
segment = []
n = 0
for i in range(len(frames)):
if len(segment) == num_frames:
break
if i >= init_frame:
img_dir = os.path.join(path, frames[i])
img1 = Image.open(img_dir)
img1 = img1.convert("RGB")
img = np.array(img1)
segment.append(img)
central_frame = segment[int(len(segment) / 2)]
return segment, central_frame
def sample(self, video_path):
frames_dirs = os.listdir(video_path)
frames_dirs = sortListByStrNumbers(frames_dirs)
frameT0 = Image.open(os.path.join(video_path,
frames_dirs[0])).convert('L')
frameT0 = np.array(frameT0)
# self.plotFrame(frameT0)
frameT1 = 0
x_c0, y_c0 = self.grayCentroid(frameT0)
frames_centroids = [[x_c0, y_c0]]
frames_samples = [frameT0]
frames_samples_indices = [0]
d_v = 0
for i in range(1, len(frames_dirs)):
frameT1 = Image.open(os.path.join(video_path,
frames_dirs[i])).convert('L')
frameT1 = np.array(frameT1)
self.plotFrame(frameT0, i - 1, frameT1, i)
[x_c0, y_c0] = frames_centroids[len(frames_centroids) - 1]
x_c1, y_c1 = self.grayCentroid(frameT1)
#
x_c_start, y_c_start = self.averageCentroid(frames_centroids)
d = self.relativeDistance(x_c_start, y_c_start, x_c1, y_c1)
if d < self.threshold1:
frames_centroids.append([x_c1, y_c1])
if len(frames_centroids) > self.maxSampleLength:
[x_c0, y_c0] = frames_centroids[len(frames_centroids) - 1]
frames_centroids = [[x_c0, y_c0]]
else:
frames_samples.append(frameT1)
frames_samples_indices.append(i)
print(
'LenSimilarFrames({}), frame t({})-frame t+1({}) = {}, avg ({}, {}), current_centroid ({}, {})'
.format(len(frames_centroids), i - 1, i, d, x_c_start,
y_c_start, x_c1, y_c1))
frameT0 = frameT1
return frames_samples, frames_samples_indices
def load_multiple_segments(path, segments_num, segment_len):
frames = os.listdir(path)
frames = sortListByStrNumbers(frames)