def __getitem__(self, idx):
epiInd = 0 # calculate the epiInd
while idx >= self.episodeNum[epiInd]:
# print self.episodeNum[epiInd],
epiInd += 1
if epiInd > 0:
idx -= self.episodeNum[epiInd - 1]
# random fliping
flipping = False
if self.aug and random.random() > 0.5:
flipping = True
# print epiInd, idx
imgseq = []
for k in range(self.batch):
img = cv2.imread(self.imgnamelist[epiInd][idx + k])
if self.aug:
img = im_hsv_augmentation(img)
img = im_crop(img)
outimg = im_scale_norm_pad(img,
outsize=self.imgsize,
mean=self.mean,
std=self.std,
down_reso=True,
flip=flipping)
imgseq.append(outimg)
return np.array(imgseq)
def augment_image(self, img, flipping):
# augment image to make "new" data
if self.aug:
img = utils.im_hsv_augmentation(img)
img = utils.im_crop(img, maxscale=self.maxscale)
out_img = utils.im_scale_norm_pad(
img,
self.mean,
self.std,
out_size=self.img_size,
# down_reso=True,
flip=flipping)
return out_img
def __getitem__(self, idx):
img = cv2.imread(self.imgnamelist[idx]) # in bgr
label = np.array(self.labellist[idx], dtype=np.float32)
# random fliping
flipping = False
if self.aug and random.random() > 0.5:
flipping = True
label[1] = -label[1]
if self.aug:
img = im_hsv_augmentation(img)
img = im_crop(img, maxscale=self.maxscale)
outimg = im_scale_norm_pad(img,
outsize=self.imgsize,
mean=self.mean,
std=self.std,
down_reso=True,
flip=flipping)
return {'img': outimg, 'label': label}
def __getitem__(self, idx):
epiInd = 0 # calculate the epiInd
while idx >= self.episodeNum[epiInd]:
# print self.episodeNum[epiInd],
epiInd += 1
if epiInd > 0:
idx -= self.episodeNum[epiInd - 1]
# random fliping
flipping = False
if self.aug and random.random() > 0.5:
flipping = True
# print epiInd, idx
imgseq = []
labelseq = []
for k in range(self.batch):
img = cv2.imread(self.imgnamelist[epiInd][idx + k][0])
angle = self.imgnamelist[epiInd][idx + k][1]
direction_angle_cos = np.cos(float(angle))
direction_angle_sin = np.sin(float(angle))
label = np.array([direction_angle_sin, direction_angle_cos],
dtype=np.float32)
if self.aug:
img = im_hsv_augmentation(img)
img = im_crop(img)
if flipping:
label[1] = -label[1]
outimg = im_scale_norm_pad(img,
outsize=self.imgsize,
mean=self.mean,
std=self.std,
down_reso=True,
flip=flipping)
imgseq.append(outimg)
labelseq.append(label)
return {'imgseq': np.array(imgseq), 'labelseq': np.array(labelseq)}
def __getitem__(self, idx):
if self.filename[-3:] == 'csv':
point_info = self.items.iloc[idx]
else:
point_info = self.items[idx]
#print(point_info)
img_name = point_info['path']
direction_angle = point_info['direction_angle']
direction_angle_cos = np.cos(float(direction_angle))
direction_angle_sin = np.sin(float(direction_angle))
label = np.array([direction_angle_sin, direction_angle_cos],
dtype=np.float32)
img = cv2.imread(img_name)
# random fliping
flipping = False
if self.aug and random.random() > 0.5:
flipping = True
label[1] = -label[1]
if img is None:
print 'error image:', img_name
return
if self.aug:
img = im_hsv_augmentation(img, Hscale=10, Sscale=60, Vscale=60)
img = im_crop(img, maxscale=self.maxscale)
outimg = im_scale_norm_pad(img,
outsize=self.imgsize,
mean=self.mean,
std=self.std,
down_reso=True,
flip=flipping)
return {'img': outimg, 'label': label}
iy = F.ix
facesy = F.bbox
# for all enrolled images
for i in range(n):
X = extract_rows(D, ix, i) # descriptors of subject i
# computation of scores between enrolled faces and session images
scr, scr_best, ind_best, face_detected = vector_distances(
Y, X.T, sc_method, theta, print_scr)
F.scores[i][j] = scr_best
# construction of output image with the recognized faces per subject in each session
if show_img == 1:
if face_detected == 1:
ii = ind_best[0]
jj = iy[ind_best[0]].item()
image = im_crop(img_path_session + img_names_session[jj],
facesy[ii], 0)
else:
image = []
image_session = im_concatenate(image_session, image, img_size, 0)
if show_img == 1:
image_final = im_concatenate(image_final, image_session, img_size_f, 1)
image_session = []
# assistance report
if show_img == 1:
imshow(image_final)
F.reportAssistance()