def __call__(self, img_list, label_list):
img_list = [img.resize(self.size, Image.BILINEAR) for img in img_list]
label_list = [
fl.resize_flow(label, self.size[0], self.size[1], self.method)
for label in label_list
]
return img_list, label_list
def __call__(self, img_list, label_list):
temp_scale = self.scale[0] + (self.scale[1] -
self.scale[0]) * random.random()
new_crop_h = int(self.crop_h * temp_scale)
new_crop_w = int(self.crop_w * temp_scale)
w, h = img_list[0].size
h_off = random.randint(0, h - new_crop_h)
w_off = random.randint(0, w - new_crop_w)
img_list = [
img.crop((w_off, h_off, w_off + new_crop_w, h_off + new_crop_h))
for img in img_list
]
label_list = [
label[h_off:h_off + new_crop_h, w_off:w_off + new_crop_w, :]
for label in label_list
]
img_list = [
img.resize((self.crop_w, self.crop_h), Image.BILINEAR)
for img in img_list
]
label_list = [
fl.resize_flow(label, self.crop_w, self.crop_h, self.method)
for label in label_list
]
return img_list, label_list
def __call__(self, img_list, label_list):
temp_scale = self.scale[0] + (self.scale[1] -
self.scale[0]) * random.random()
temp_aspect_ratio = 1.0
if self.aspect_ratio is not None:
temp_aspect_ratio = self.aspect_ratio[0] + (
self.aspect_ratio[1] - self.aspect_ratio[0]) * random.random()
temp_aspect_ratio = math.sqrt(temp_aspect_ratio)
scale_factor_w = temp_scale * temp_aspect_ratio
scale_factor_h = temp_scale / temp_aspect_ratio
w, h = img_list[0].size
new_w = int(w * scale_factor_w)
new_h = int(h * scale_factor_h)
img_list = [
img.resize((new_w, new_h), Image.BILINEAR) for img in img_list
]
label_list = [
fl.resize_flow(label, new_w, new_h, self.method)
for label in label_list
]
return img_list, label_list