def load_demo_images(paths): paths = [os.path.join(paths, x) for x in os.listdir(paths)] img_h = cfg.CONST.IMG_H img_w = cfg.CONST.IMG_W model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True) """ imgs = [] for path in paths: img = Image.open(path) #img = segment(path) #img = transforms.ToPILImage()(img) img = img.resize((img_h, img_w), Image.ANTIALIAS) img = preprocess_img(img, train=False) imgs.append([np.array(img).transpose( \ (2, 0, 1)).astype(np.float32)]) """ imgs = segment(paths, model) result = [] for img in imgs: img = transforms.ToPILImage()(img) img = img.resize((img_h, img_w), Image.ANTIALIAS) img = preprocess_img(img, train=False) result.append([np.array(img).transpose( \ (2, 0, 1)).astype(np.float32)]) ims_np = np.array(result).astype(np.float32) return torch.from_numpy(ims_np)
def load_demo_images(): ims = [] for i in range(3): im = Image.open('imgs/%02d.png' % i) im = preprocess_img(im, train=False)[0] ims.append([np.array(im).transpose((2, 0, 1)).astype(np.float32)]) return np.array(ims)
def load_demo_images(): ims = [] for i in range(3): im = preprocess_img(Image.open('imgs/%d.jpg' % i).resize((127, 127)), train=False) ims.append([np.array(im).transpose((2, 0, 1)).astype(np.float32)]) plt.imshow(im) plt.show() return np.array(ims)
def load_demo_images(imgs='./imgs/', maxrange=3): global demo_imgs ims = [] # print("maxrange", maxrange) for i in range(maxrange): im = preprocess_img( # Image.open(imgs + '%d.jpg' % #进来的时候是127*127*3 Image.open(imgs + '0%d.png' % #进来的时候是127*127*3 i).resize((127, 127)), train=False) ims.append([np.array(im).transpose((2, 0, 1)).astype(np.float32)]) # return np.array(ims) demo_imgs = np.array(ims)
def load_demo_images(): img_h = cfg.CONST.IMG_H img_w = cfg.CONST.IMG_W imgs = [] for i in range(3): img = Image.open('imgs/%d.png' % i) img = img.resize((img_h, img_w), Image.ANTIALIAS) img = preprocess_img(img, train=False) imgs.append([np.array(img).transpose( \ (2, 0, 1)).astype(np.float32)]) ims_np = np.array(imgs).astype(np.float32) return torch.from_numpy(ims_np)
def load_imgs(taken_time): assert (os.path.isdir(taken_time)) img_h = cfg.CONST.IMG_H img_w = cfg.CONST.IMG_W imgs = [] for file in os.listdir(taken_time): if file[-4:] in [".JPG", ".png"]: img = Image.open(os.path.join(taken_time, file)) img = img.resize((img_h, img_w), Image.ANTIALIAS) img = preprocess_img(img, train=False) imgs.append([np.array(img).transpose( \ (2, 0, 1)).astype(np.float32)]) plt.imshow(img) plt.show() return np.array(imgs)
def load_img(self, category, model_id, image_id): image_fn = get_rendering_file(category, model_id, image_id) im = Image.open(image_fn) t_im = preprocess_img(im, self.train) return t_im
def load_img(category, model_id, img_id, train=False): img_fn = get_rendering_file(category, model_id, img_id) im = Image.open(img_fn) t_im = preprocess_img(im, train=train) return t_im