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helperFunctions.py
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helperFunctions.py
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import os
import numpy as np
import cv2
import time
import h5py
def getUCF101(base_directory = ''):
# action class labels
class_file = open(base_directory + 'ucfTrainTestlist/classInd.txt','r')
lines = class_file.readlines()
lines = [line.split(' ')[1].strip() for line in lines]
class_file.close()
class_list = np.asarray(lines)
# training data
train_file = open(base_directory + 'ucfTrainTestlist/trainlist01.txt','r')
lines = train_file.readlines()
filenames = ['UCF-101/' + line.split(' ')[0] for line in lines]
y_train = [int(line.split(' ')[1].strip())-1 for line in lines]
y_train = np.asarray(y_train)
filenames = [base_directory + filename for filename in filenames]
train_file.close()
train = (np.asarray(filenames),y_train)
# testing data
test_file = open(base_directory + 'ucfTrainTestlist/testlist01.txt','r')
lines = test_file.readlines()
filenames = ['UCF-101/' + line.split(' ')[0].strip() for line in lines]
classnames = [filename.split('/')[1] for filename in filenames]
y_test = [np.where(classname == class_list)[0][0] for classname in classnames]
y_test = np.asarray(y_test)
filenames = [base_directory + filename for filename in filenames]
test_file.close()
test = (np.asarray(filenames),y_test)
return class_list, train, test
def loadFrame(args):
mean = np.asarray([0.485, 0.456, 0.406], np.float32)
std = np.asarray([0.229, 0.224, 0.225], np.float32)
curr_w = 320
curr_h = 240
height = width = 224
(filename, augment) = args
data = np.zeros((3, height, width), dtype=np.float32)
try:
### load file from HDF5
filename = filename.replace('.avi', '.hdf5')
filename = filename.replace('UCF-101', 'UCF-101-hdf5')
h = h5py.File(filename, 'r')
nFrames = len(h['video']) - 1
frame_index = np.random.randint(nFrames)
frame = h['video'][frame_index]
if (augment == True):
## RANDOM CROP - crop 70-100% of original size
## don't maintain aspect ratio
if (np.random.randint(2) == 0):
resize_factor_w = 0.3 * np.random.rand() + 0.7
resize_factor_h = 0.3 * np.random.rand() + 0.7
w1 = int(curr_w * resize_factor_w)
h1 = int(curr_h * resize_factor_h)
w = np.random.randint(curr_w - w1)
h = np.random.randint(curr_h - h1)
frame = frame[h:(h + h1), w:(w + w1)]
## FLIP
if (np.random.randint(2) == 0):
frame = cv2.flip(frame, 1)
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
## Brightness +/- 15
brightness = 30
random_add = np.random.randint(brightness + 1) - brightness / 2.0
frame += random_add
frame[frame > 255] = 255.0
frame[frame < 0] = 0.0
else:
# don't augment
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
## resnet model was trained on images with mean subtracted
frame = frame / 255.0
frame = (frame - mean) / std
frame = frame.transpose(2, 0, 1)
data[:, :, :] = frame
except:
print("Exception: " + filename)
data = np.array([])
return data
def loadSequence(args):
mean = np.asarray([0.433, 0.4045, 0.3776], np.float32)
std = np.asarray([0.1519876, 0.14855877, 0.156976], np.float32)
curr_w = 320
curr_h = 240
height = width = 224
num_of_frames = 16
(filename, augment) = args
data = np.zeros((3, num_of_frames, height, width), dtype=np.float32)
try:
### load file from HDF5
filename = filename.replace('.avi', '.hdf5')
filename = filename.replace('UCF-101', 'UCF-101-hdf5')
h = h5py.File(filename, 'r')
nFrames = len(h['video']) - 1
frame_index = np.random.randint(nFrames - num_of_frames)
video = h['video'][frame_index:(frame_index + num_of_frames)]
if (augment == True):
## RANDOM CROP - crop 70-100% of original size
## don't maintain aspect ratio
resize_factor_w = 0.3 * np.random.rand() + 0.7
resize_factor_h = 0.3 * np.random.rand() + 0.7
w1 = int(curr_w * resize_factor_w)
h1 = int(curr_h * resize_factor_h)
w = np.random.randint(curr_w - w1)
h = np.random.randint(curr_h - h1)
random_crop = np.random.randint(2)
## Random Flip
random_flip = np.random.randint(2)
## Brightness +/- 15
brightness = 30
random_add = np.random.randint(brightness + 1) - brightness / 2.0
data = []
for frame in video:
if (random_crop):
frame = frame[h:(h + h1), w:(w + w1), :]
if (random_flip):
frame = cv2.flip(frame, 1)
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
frame += random_add
frame[frame > 255] = 255.0
frame[frame < 0] = 0.0
frame = frame / 255.0
frame = (frame - mean) / std
data.append(frame)
data = np.asarray(data)
else:
# don't augment
data = []
for frame in video:
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
frame = frame / 255.0
frame = (frame - mean) / std
data.append(frame)
data = np.asarray(data)
data = data.transpose(3, 0, 1, 2)
except:
print("Exception: " + filename)
data = np.array([])
return data