class BUILDING_DATASETS:
def __init__(self, test_folder):
self.var = RootVariables()
self.dataset = None
self.imu_arr_acc, self.imu_arr_gyro, self.gaze_arr = None, None, None
self.train_last, self.test_last = None, None
self.train_new, self.test_new = None, None
temp = None
self.video_file = 'scenevideo.mp4'
self.test_folders_num, self.train_folders_num = 0, 0
self.frame_count = 0
self.capture = None
self.ret = None
self.toggle = 0
self.test_folder = test_folder
self.stack_frames = []
self.transforms = transforms.Compose([transforms.ToTensor()])
self.panda_data = {}
def populate_gaze_data(self, subDir):
# if toggle != self.toggle:
# self.folders_num = 0
# self.toggle = toggle
subDir = subDir + '/' if subDir[-1]!='/' else subDir
print(subDir)
os.chdir(self.var.root + subDir)
capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.dataset = JSON_LOADER(subDir)
self.dataset.POP_GAZE_DATA(self.frame_count)
self.gaze_arr = np.array(self.dataset.var.gaze_data).transpose()
_ = os.system('rm gaze_file.csv')
self.panda_data = {}
self.create_dataframes(subDir, 'gaze')
self.gaze_arr = np.array(self.dataset.var.gaze_data).transpose()
temp = np.zeros((self.frame_count*4-self.var.trim_frame_size*4*2, 2))
temp[:,0] = self.gaze_arr[tuple([np.arange(self.var.trim_frame_size*4, self.frame_count*4 - self.var.trim_frame_size*4), [0]])]
temp[:,1] = self.gaze_arr[tuple([np.arange(self.var.trim_frame_size*4, self.frame_count*4 - self.var.trim_frame_size*4), [1]])]
return temp
def load_unified_gaze_dataset(self): ## missing data in imu_lift_s1
self.test_folders_num, self.train_folders_num = 0, 0
for index, subDir in enumerate(tqdm(sorted(os.listdir(self.var.root)), desc="Building gaze dataset")):
if 'train_' in subDir :
self.temp = self.populate_gaze_data(subDir)
self.train_folders_num += 1
if self.train_folders_num > 1:
self.train_new = np.concatenate((self.train_last, self.temp), axis=0)
else:
self.train_new = self.temp
self.train_last = self.train_new
print(subDir, len(self.train_last.reshape(-1, 4, 2)))
if 'test_' in subDir:
print('TEST folder: ', self.test_folder)
self.temp = self.populate_gaze_data(subDir)
self.test_folders_num += 1
if self.test_folders_num > 1:
self.test_new = np.concatenate((self.test_last, self.temp), axis=0)
else:
self.test_new = self.temp
self.test_last = self.test_new
print(subDir, len(self.test_last.reshape(-1, 4, 2)))
return self.train_new, self.test_new
def load_unified_frame_dataset(self, reset_dataset=0):
## INCLUDES THE LAST FRAME
if reset_dataset == 1:
print('Deleting the old dataset .. ')
_ = os.system('rm -r ' + self.var.root + 'training_images')
_ = os.system('rm -r ' + self.var.root + 'testing_images')
_ = os.system('mkdir ' + self.var.root + 'training_images')
_ = os.system('mkdir ' + self.var.root + 'testing_images')
train_frame_index, test_frame_index = 0, 0
trainpaths, testpaths = [], []
print("Building Image dataset ..")
tqdmloader = tqdm(sorted(os.listdir(self.var.root)))
for index, subDir in enumerate(tqdmloader):
if 'train_' in subDir :
tqdmloader.set_description('Train folder: {}'.format(subDir))
# _ = os.system('rm -r ' + self.var.root + 'training_images/' + subDir)
_ = os.system('mkdir ' + self.var.root + 'training_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size - 8)
for i in range(self.frame_count - (self.var.trim_frame_size*2) + 8): ## because we need frame no. 149 to stack with frame 150, to predict for frame no. 150
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288)) # (512, 288)
w, h = 224, 224
center_x = frame.shape[1] / 2
center_y = frame.shape[0] / 2
x = center_x - w/2
y = center_y - h/2
frame = frame[int(y):int(y+h), int(x):int(x+w)]
# frame = cv2.resize(frame, (224, 224)) # (512, 288)
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
path = self.var.root + 'training_images/' + subDir + 'image_' + str(train_frame_index) + '.jpg'
cv2.imwrite(path, frame)
# self.create_clips(self.capture, train_frame_index, 'training_images')
train_frame_index += 1
trainpaths.append(path)
if 'test_' in subDir:
tqdmloader.set_description('Test folder: {}'.format(subDir))
_ = os.system('mkdir ' + self.var.root + 'testing_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
# _ = os.system('rm ' + str(self.var.frame_size) + '_framesExtracted_data_' + str(self.var.trim_frame_size) + '.npy')
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size - 8)
for i in range(self.frame_count - (self.var.trim_frame_size*2) + 8): ## because we need frame no. 149 to stack with frame 150, to predict for frame no. 150
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288)) # (398, 224)
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
w, h = 224, 224
center_x = frame.shape[1] / 2
center_y = frame.shape[0] / 2
x = center_x - w/2
y = center_y - h/2
frame = frame[int(y):int(y+h), int(x):int(x+w)]
path = self.var.root + 'testing_images/' + subDir + 'image_' + str(test_frame_index) + '.jpg'
cv2.imwrite(path, frame)
# self.create_clips(self.capture, test_frame_index, 'testing_images')
test_frame_index += 1
testpaths.append(path)
print(test_frame_index)
os.chdir(self.var.root)
dict = {'image_paths': trainpaths}
df = pd.DataFrame(dict)
df.to_csv(self.var.root + '/trainImg.csv')
dict = {'image_paths':testpaths}
df = pd.DataFrame(dict)
df.to_csv(self.var.root + '/testImg.csv')
def populate_imu_data(self, subDir):
subDir = subDir + '/' if subDir[-1]!='/' else subDir
print(subDir)
os.chdir(self.var.root + subDir)
capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.dataset = JSON_LOADER(subDir)
self.dataset.POP_IMU_DATA(self.frame_count, cut_short=True)
_ = os.system('rm imu_file.csv')
self.panda_data = {}
self.create_dataframes(subDir, dframe_type='imu')
self.imu_arr_acc = np.array(self.dataset.var.imu_data_acc).transpose()
self.imu_arr_gyro = np.array(self.dataset.var.imu_data_gyro).transpose()
temp = np.zeros((len(self.imu_arr_acc) , 6))
temp = np.zeros((self.frame_count*4-self.var.trim_frame_size*4, 6))
temp[:,0] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [0]])]
temp[:,1] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [1]])]
temp[:,2] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [2]])]
temp[:,3] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [0]])]
temp[:,4] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [1]])]
temp[:,5] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [2]])]
return temp
def load_unified_imu_dataset(self): ## missing data in imu_CoffeeVendingMachine_S2
for index, subDir in enumerate(tqdm(sorted(os.listdir(self.var.root)), desc="Building IMU dataset")):
if 'train_' in subDir :
self.temp = self.populate_imu_data(subDir)
self.train_folders_num += 1
if self.train_folders_num > 1:
self.train_new = np.concatenate((self.train_last, self.temp), axis=0)
else:
self.train_new = self.temp
self.train_last = self.train_new
if 'test_' in subDir:
print('TEST folder: ', self.test_folder)
self.temp = self.populate_imu_data(subDir)
self.test_folders_num += 1
if self.test_folders_num > 1:
self.test_new = np.concatenate((self.test_last, self.temp), axis=0)
else:
self.test_new = self.temp
self.test_last = self.test_new
return self.train_new, self.test_new
def create_dataframes(self, subDir, dframe_type, start_index=0):
if dframe_type == 'gaze':
## GAZE
for sec in range(self.frame_count):
self.panda_data[sec] = list(zip(self.dataset.var.gaze_data[0][start_index:start_index + 4], self.dataset.var.gaze_data[1][start_index:start_index+4]))
start_index += 4
self.df_gaze = pd.DataFrame({ key:pd.Series(value) for key, value in self.panda_data.items()}).T
self.df_gaze.columns =['Gaze_Pt_1', 'Gaze_Pt_2', 'Gaze_Pt_3', 'Gaze_Pt_4']
self.df_gaze.to_csv('gaze_file.csv')
elif dframe_type == 'imu':
## IMU
for sec in range(self.frame_count):
# self.panda_data[sec] = list(tuple((sec, sec+2)))
self.panda_data[sec] = list(zip(zip(self.dataset.var.imu_data_acc[0][start_index:start_index+4],
self.dataset.var.imu_data_acc[1][start_index:start_index+4],
self.dataset.var.imu_data_acc[2][start_index:start_index+4]),
zip(self.dataset.var.imu_data_gyro[0][start_index:start_index+4],
self.dataset.var.imu_data_gyro[1][start_index:start_index+4],
self.dataset.var.imu_data_gyro[2][start_index:start_index+4])))
start_index += 4
self.df_imu = pd.DataFrame({ key:pd.Series(value) for key, value in self.panda_data.items()}).T
self.df_imu.columns =['IMU_Acc/Gyro_Pt_1', 'IMU_Acc/Gyro_Pt_2', 'IMU_Acc/Gyro_Pt_3', 'IMU_Acc/Gyro_Pt_4']
self.df_imu.to_csv('imu_file.csv')
return dummy
datasets = IMU_GAZE_FRAME_DATASET(var.root, frame_size, trim_size)
uni_imu_dataset = datasets.imu_datasets
n_uni_imu_dataset = normalization(uni_imu_dataset)
s_uni_imu_dataset = standarization(uni_imu_dataset)
start_index, end_index = 0, 0
for index, subDir in enumerate(sorted(os.listdir(var.root))):
if 'imu_Book' in subDir:
subDir = subDir + '/' if subDir[-1] != '/' else subDir
os.chdir(var.root + subDir)
capture = cv2.VideoCapture('scenevideo.mp4')
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
dataset = JSON_LOADER(subDir)
imu = dataset.POP_IMU_DATA(frame_count, return_val=True)
imu_arr_acc = np.array(dataset.var.imu_data_acc).transpose()
imu_arr_gyro = np.array(dataset.var.imu_data_gyro).transpose()
dataset = JSON_LOADER(subDir)
new_imu = dataset.POP_IMU_DATA(frame_count,
cut_short=False,
return_val=True)
acc_copy = np.array(dataset.var.imu_data_acc).transpose()
gyro_copy = np.array(dataset.var.imu_data_gyro).transpose()
end_index = start_index + len(acc_copy)
print(subDir, start_index, end_index)
n_acc = n_uni_imu_dataset[start_index:end_index, :3]
n_gyro = n_uni_imu_dataset[start_index:end_index, 3:]
s_acc = s_uni_imu_dataset[start_index:end_index, :3]
s_gyro = s_uni_imu_dataset[start_index:end_index, 3:]
start_index = end_index
class BUILDING_DATASETS:
def __init__(self, test_folder):
self.var = RootVariables()
self.dataset = None
self.imu_arr_acc, self.imu_arr_gyro, self.gaze_arr = None, None, None
self.train_new, self.test_new = None, None
temp = None
self.video_file = 'scenevideo.mp4'
self.test_folders_num, self.train_folders_num = 0, 0
self.frame_count = 0
self.capture = None
self.ret = None
self.toggle = 0
self.test_folder = test_folder
self.stack_frames = []
self.transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
self.panda_data = {}
self.df = None
def populate_gaze_data(self, subDir):
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.dataset = JSON_LOADER(subDir)
self.dataset.POP_GAZE_DATA(self.frame_count)
self.gaze_arr = np.array(self.dataset.var.gaze_data).transpose()
_ = os.system('rm gaze_file.csv')
self.panda_data = {}
self.create_dataframes(subDir, 'gaze')
self.gaze_arr = np.array(self.dataset.var.gaze_data).transpose()
temp = np.zeros((self.frame_count*4-self.var.trim_frame_size*4*2, 2))
temp[:,0] = self.gaze_arr[tuple([np.arange(self.var.trim_frame_size*4, self.frame_count*4 - self.var.trim_frame_size*4), [0]])]
temp[:,1] = self.gaze_arr[tuple([np.arange(self.var.trim_frame_size*4, self.frame_count*4 - self.var.trim_frame_size*4), [1]])]
return temp
def load_unified_gaze_dataset(self): ## missing data in imu_lift_s1
self.test_folders_num, self.train_folders_num = 0, 0
print('Building gaze dataset ..')
tqdmloader = tqdm(sorted(os.listdir(self.var.root)))
for index, subDir in enumerate(tqdmloader):
if 'train_' in subDir:
tqdmloader.set_description('Train folder: {}'.format(subDir))
self.temp = self.populate_gaze_data(subDir)
self.train_folders_num += 1
if self.train_folders_num > 1:
self.train_new = np.concatenate((self.train_new, self.temp), axis=0)
else:
self.train_new = self.temp
if 'test_' in subDir:
tqdmloader.set_description('Test folder: {}'.format(subDir))
self.temp = self.populate_gaze_data(subDir)
self.test_folders_num += 1
if self.test_folders_num > 1:
self.test_new = np.concatenate((self.test_new, self.temp), axis=0)
else:
self.test_new = self.temp
print(subDir, len(self.test_new.reshape(-1, 4, 2)))
return self.train_new, self.test_new
def populate_imu_data(self, subDir):
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.dataset = JSON_LOADER(subDir)
self.dataset.POP_IMU_DATA(self.frame_count, cut_short=True)
_ = os.system('rm imu_file.csv')
self.panda_data = {}
self.create_dataframes(subDir, dframe_type='imu')
self.imu_arr_acc = np.array(self.dataset.var.imu_data_acc).transpose()
self.imu_arr_gyro = np.array(self.dataset.var.imu_data_gyro).transpose()
temp = np.zeros((len(self.imu_arr_acc) , 6))
temp = np.zeros((self.frame_count*4-self.var.trim_frame_size*4, 6))
temp[:,0] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [0]])]
temp[:,1] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [1]])]
temp[:,2] = self.imu_arr_acc[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [2]])]
temp[:,3] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [0]])]
temp[:,4] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [1]])]
temp[:,5] = self.imu_arr_gyro[tuple([np.arange(self.var.trim_frame_size*2, self.frame_count*4 - self.var.trim_frame_size*2), [2]])]
return temp
def load_unified_imu_dataset(self): ## missing data in imu_CoffeeVendingMachine_S2
print('Building IMU dataset ..')
tqdmloader = tqdm(sorted(os.listdir(self.var.root)))
for index, subDir in enumerate(tqdmloader):
if 'train_' in subDir :
tqdmloader.set_description('Train folder: {}'.format(subDir))
self.temp = self.populate_imu_data(subDir)
self.train_folders_num += 1
if self.train_folders_num > 1:
self.train_new = np.concatenate((self.train_new, self.temp), axis=0)
else:
self.train_new = self.temp
if 'test_' in subDir:
tqdmloader.set_description('Test folder: {}'.format(subDir))
self.temp = self.populate_imu_data(subDir)
self.test_folders_num += 1
if self.test_folders_num > 1:
self.test_new = np.concatenate((self.test_new, self.temp), axis=0)
else:
self.test_new = self.temp
print(len(self.test_new.reshape(-1, 4, 6)))
return self.train_new, self.test_new
def create_dataframes(self, subDir, dframe_type, start_index=0):
if dframe_type == 'gaze':
## GAZE
for sec in range(self.frame_count):
self.panda_data[sec] = list(zip(self.dataset.var.gaze_data[0][start_index:start_index + 4], self.dataset.var.gaze_data[1][start_index:start_index+4]))
start_index += 4
self.df_gaze = pd.DataFrame({ key:pd.Series(value) for key, value in self.panda_data.items()}).T
self.df_gaze.columns =['Gaze_Pt_1', 'Gaze_Pt_2', 'Gaze_Pt_3', 'Gaze_Pt_4']
self.df_gaze.to_csv('gaze_file.csv')
elif dframe_type == 'imu':
## IMU
for sec in range(self.frame_count):
# self.panda_data[sec] = list(tuple((sec, sec+2)))
self.panda_data[sec] = list(zip(zip(self.dataset.var.imu_data_acc[0][start_index:start_index+4],
self.dataset.var.imu_data_acc[1][start_index:start_index+4],
self.dataset.var.imu_data_acc[2][start_index:start_index+4]),
zip(self.dataset.var.imu_data_gyro[0][start_index:start_index+4],
self.dataset.var.imu_data_gyro[1][start_index:start_index+4],
self.dataset.var.imu_data_gyro[2][start_index:start_index+4])))
start_index += 4
self.df_imu = pd.DataFrame({ key:pd.Series(value) for key, value in self.panda_data.items()}).T
self.df_imu.columns =['IMU_Acc/Gyro_Pt_1', 'IMU_Acc/Gyro_Pt_2', 'IMU_Acc/Gyro_Pt_3', 'IMU_Acc/Gyro_Pt_4']
self.df_imu.to_csv('imu_file.csv')
def create_circular_mask(self, h, w, center=None, radius=None):
if center is None: # use the middle of the image
center = (int(w/2), int(h/2))
if radius is None: # use the smallest distance between the center and image walls
radius = 30 #min(center[0], center[1], w-center[0], h-center[1])
Y, X = np.ogrid[:h, :w]
dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2)
mask = dist_from_center <= radius
return mask
def load_heatmap(self, joints, n_joints, trim_size, index):
joints = cv2.cvtColor(joints, cv2.COLOR_BGR2RGB)[:,:,0]
h, w = joints.shape
y1 = np.zeros((h, w, n_joints))
padding = 40
mask = None
# x, y, = int(coordinates[0]*512), int(coordinates[1]*288)
for j in range(5):
y2 = np.copy(y1)
try:
gpts = list(map(literal_eval, self.df[trim_size-4+index+j, 1:]))
except Exception as e:
print(e)
telem = 4
for item in gpts:
if (item[0] + item[1]) == 0:
telem -= 1
if telem > 0:
coordinates = [sum(y) / telem for y in zip(*gpts)]
center = (int(coordinates[0]*512), int(coordinates[1]*288))
if j > 0 and coordinates[0] != 0.0:
mask += self.create_circular_mask(h, w, center)
else:
mask = self.create_circular_mask(h, w, center)
if mask is None:
mask = self.create_circular_mask(h, w, radius=0)
heatmap = np.zeros(joints.shape)
heatmap[mask] = 1.0
if heatmap.sum() > 0 :
y2[:, :, 0] = self.decay_heatmap(heatmap, sigma2=30)
y1 += y2
return y1
def decay_heatmap(self, heatmap, sigma2=10):
"""
Args
heatmap :
WxH matrix to decay
sigma2 :
(Default value = 1)
Returns
Heatmap obtained by gaussian-blurring the input
"""
heatmap = cv2.GaussianBlur(heatmap, (0, 0), sigma2)
# heatmap /= np.sum(heatmap)
# heatmap /= np.max(heatmap) # keep the max to 1
return heatmap
def load_heatmap_dataset(self, reset_dataset=0):
## INCLUDES THE LAST FRAME
if reset_dataset == 1:
print('Deleting the old dataset .. ')
_ = os.system('rm ' + os.path.dirname(os.path.realpath(__file__)) + '/' + 'heatmap_trainImg.csv')
_ = os.system('rm ' + os.path.dirname(os.path.realpath(__file__)) + '/' + 'heatmap_testImg.csv')
_ = os.system('rm -r ' + self.var.root + 'heatmap_training_images')
_ = os.system('rm -r ' + self.var.root + 'heatmap_testing_images')
_ = os.system('mkdir ' + self.var.root + 'heatmap_training_images')
_ = os.system('mkdir ' + self.var.root + 'heatmap_testing_images')
train_frame_index, test_frame_index = 0, 0
trainpaths, testpaths = [], []
print("Building heatmap dataset ..")
tqdmloader = tqdm(sorted(os.listdir(self.var.root)))
for index, subDir in enumerate(tqdmloader):
if 'train_' in subDir :
tqdmloader.set_description('Train folder: {}'.format(subDir))
_ = os.system('mkdir ' + self.var.root + 'heatmap_training_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.df = pd.read_csv('gaze_file.csv').to_numpy()
for i in range(len(self.df)):
try:
_ = (list(map(literal_eval, self.df[i, 1:])))
except:
indexes = []
for j in range(1, len(self.df[i])):
if 'nan' in self.df[i][j]:
self.df[i][j] = '(0.0, 0.0)'
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size)
for i in range(self.frame_count - 100):
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288))
heatmapshow = None
try:
x = self.load_heatmap(frame, 1, self.var.trim_frame_size, i-4)
heatmapshow = cv2.normalize(x, heatmapshow, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
heatmapshow = cv2.applyColorMap(heatmapshow, cv2.COLORMAP_JET)
# frame = cv2.addWeighted(heatmapshow, 0.5, frame, 0.7, 0)
# cv2.imshow('image', x)
# # cv2.waitKey(0)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
except Exception as e:
print(e)
path = self.var.root + 'heatmap_training_images/' + subDir + 'image_' + str(train_frame_index) + '.jpg'
cv2.imwrite(path, heatmapshow)
# self.create_clips(self.capture, train_frame_index, 'training_images')
train_frame_index += 1
trainpaths.append(path)
if 'test_' in subDir:
tqdmloader.set_description('Test folder: {}'.format(subDir))
_ = os.system('mkdir ' + self.var.root + 'heatmap_testing_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
# _ = os.system('rm ' + str(self.var.frame_size) + '_framesExtracted_data_' + str(self.var.trim_frame_size) + '.npy')
self.df = pd.read_csv('gaze_file.csv').to_numpy()
for i in range(len(self.df)):
try:
_ = (list(map(literal_eval, self.df[i, 1:])))
except:
indexes = []
for j in range(1, len(self.df[i])):
if 'nan' in self.df[i][j]:
self.df[i][j] = '(0.0, 0.0)'
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size)
for i in range(self.frame_count - 100): ## because we need frame no. 149 to stack with frame 150, to predict for frame no. 150
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288)) # (398, 224)
heatmapshow = None
try:
x = self.load_heatmap(frame, 1, self.var.trim_frame_size, i-4)
heatmapshow = cv2.normalize(x, heatmapshow, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
heatmapshow = cv2.applyColorMap(heatmapshow, cv2.COLORMAP_JET)
# frame = cv2.addWeighted(heatmapshow, 0.5, frame, 0.7, 0)
except Exception as e:
print(e)
path = self.var.root + 'heatmap_testing_images/' + subDir + 'image_' + str(test_frame_index) + '.jpg'
cv2.imwrite(path, heatmapshow)
# self.create_clips(self.capture, test_frame_index, 'testing_images')
test_frame_index += 1
testpaths.append(path)
print(test_frame_index)
dict = {'image_paths': trainpaths}
df = pd.DataFrame(dict)
# print(os.path.dirname(os.path.realpath(__file__)))
df.to_csv(self.var.root + 'heatmap_trainImg.csv')
dict = {'image_paths':testpaths}
df = pd.DataFrame(dict)
df.to_csv(self.var.root + 'heatmap_testImg.csv')
def create_clips(self, cap, index, type):
fourcc = cv2.VideoWriter_fourcc(*'MPEG')
out = cv2.VideoWriter('/Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/' + type + '/output_' + str(index) + '.avi', fourcc, 1.0, (224,224))
chunks = None
for i in range(5):
_, frame = cap.read()
frame = cv2.resize(frame, (224, 224))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = self.transforms(frame)
frame = frame.unsqueeze(dim=3)
chunks = torch.cat((chunks, frame), axis=3) if i > 0 else frame
out.write(frame)
cap.set(cv2.CAP_PROP_POS_FRAMES,50+index-4)
out.release()
def load_unified_frame_dataset(self, reset_dataset=0):
## INCLUDES THE LAST FRAME
if reset_dataset == 1:
# print('Deleting the old dataset .. ')
# _ = os.system('rm -r ' + self.var.root + 'training_images')
# _ = os.system('rm -r ' + self.var.root + 'testing_images')
#
# _ = os.system('mkdir ' + self.var.root + 'training_images')
# _ = os.system('mkdir ' + self.var.root + 'testing_images')
train_frame_index, test_frame_index = 0, 0
trainpaths, testpaths = [], []
print("Building Image dataset ..")
tqdmloader = tqdm(sorted(os.listdir(self.var.root)))
for index, subDir in enumerate(tqdmloader):
if 'washands' in subDir :
tqdmloader.set_description('Train folder: {}'.format(subDir))
_ = os.system('rm -r ' + self.var.root + 'training_images/' + subDir)
_ = os.system('mkdir ' + self.var.root + 'training_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size - 1)
for i in range(self.frame_count - 100 + 4): ## because we need frame no. 149 to stack with frame 150, to predict for frame no. 150
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288))
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
path = self.var.root + 'training_images/' + subDir + 'image_' + str(train_frame_index) + '.jpg'
cv2.imwrite(path, frame)
# self.create_clips(self.capture, train_frame_index, 'training_images')
train_frame_index += 1
trainpaths.append(path)
if 'test_' in subDir:
tqdmloader.set_description('Test folder: {}'.format(subDir))
_ = os.system('mkdir ' + self.var.root + 'testing_images/' + subDir)
total_frames = 0
subDir = subDir + '/' if subDir[-1]!='/' else subDir
os.chdir(self.var.root + subDir)
self.capture = cv2.VideoCapture(self.video_file)
self.frame_count = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
# _ = os.system('rm ' + str(self.var.frame_size) + '_framesExtracted_data_' + str(self.var.trim_frame_size) + '.npy')
self.capture.set(cv2.CAP_PROP_POS_FRAMES,self.var.trim_frame_size - 1)
for i in range(self.frame_count - 100 + 4): ## because we need frame no. 149 to stack with frame 150, to predict for frame no. 150
_, frame = self.capture.read()
frame = cv2.resize(frame, (512, 288)) # (398, 224)
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
path = self.var.root + 'testing_images/' + subDir + 'image_' + str(test_frame_index) + '.jpg'
cv2.imwrite(path, frame)
# self.create_clips(self.capture, test_frame_index, 'testing_images')
test_frame_index += 1
testpaths.append(path)
print(test_frame_index)
os.chdir(self.var.root)
dict = {'image_paths': trainpaths}
df = pd.DataFrame(dict)
df.to_csv(self.var.root + '/trainImg.csv')
dict = {'image_paths':testpaths}
df = pd.DataFrame(dict)
df.to_csv(self.var.root + '/testImg.csv')