def call_image(self, img):
        return torch_transforms.ColorJitter(self.brightness, self.contrast, self.saturation, self.hue)(img) 

def preprocessImage(img, use_color_jitter, image_size_dict, img_norm_info, use_caffe_pretrained_model):
		# calculate target_size and scale_factor, target_size's format is (h, w)
		w_ori, h_ori = img.width, img.height
		if w_ori > h_ori:
			target_size = (image_size_dict.get('SHORT_SIDE'), image_size_dict.get('LONG_SIDE'))
		else:
			target_size = (image_size_dict.get('LONG_SIDE'), image_size_dict.get('SHORT_SIDE'))
		h_t, w_t = target_size
		scale_factor = min(w_t/w_ori, h_t/h_ori)
		target_size = (round(scale_factor*h_ori), round(scale_factor*w_ori))
		# define and do transform
		if use_caffe_pretrained_model:
			means_norm = img_norm_info['caffe'].get('mean_rgb')
			stds_norm = img_norm_info['caffe'].get('std_rgb')
			if use_color_jitter:
				transform = transforms.Compose([transforms.Resize(target_size),
												transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
												transforms.ToTensor(),
												transforms.Normalize(mean=means_norm, std=stds_norm)])
			else:
				transform = transforms.Compose([transforms.Resize(target_size),
												transforms.ToTensor(),
												transforms.Normalize(mean=means_norm, std=stds_norm)])
			img = transform(img) * 255
			img = img[(2, 1, 0), :, :]
		else:
			means_norm = img_norm_info['pytorch'].get('mean_rgb')
			stds_norm = img_norm_info['pytorch'].get('std_rgb')
			if use_color_jitter:
				transform = transforms.Compose([transforms.Resize(target_size),
												transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
												transforms.ToTensor(),
												transforms.Normalize(mean=means_norm, std=stds_norm)])
			else:
				transform = transforms.Compose([transforms.Resize(target_size),
												transforms.ToTensor(),
												transforms.Normalize(mean=means_norm, std=stds_norm)])
			img = transform(img)
		# return necessary data
		return img, scale_factor, target_size