def __getitem__(self, index): sample = self.imgs[index] if self.phase == 'train': splits = sample.split('/', 7)[-1] #分割6次,可变 label = int(splits.split('_')[0]) img_path = sample data = Image.open(img_path).convert('RGB') # data = amaugimg(data) data = self.transforms(data) return data, label else: #对于测试 data = Image.open(sample) data = data.convert('RGB') img1 = data #原图 img2 = data.transpose(Image.FLIP_LEFT_RIGHT) #水平反转图 data = data.convert('L') #灰度图 img3 = cv2.cvtColor(np.asarray(data), cv2.COLOR_RGB2BGR) img3 = Image.fromarray(cv2.cvtColor(img3, cv2.COLOR_BGR2RGB)) img4 = img3.transpose(Image.FLIP_LEFT_RIGHT) #灰度反转 img1 = self.transforms(img1) img2 = self.transforms(img2) img3 = self.transforms(img3) img4 = self.transforms(img4) return img1, img2
def __getitem__(self, index): #print(self.dict_labels) #print(lst) img_path = self.lst[index] #dog->1,cat->0 label = self.lst[index][1] data = imageio.imread(self.root + '/images/' + img_path[0] + '.jpg') try: data = self.augmentations.augment_image(data) imageio.imwrite( r'F:/5.datasets/test/aug_images/' + img_path[0] + '-aug_pipline.jpg', data) data = Image.open(r'F:/5.datasets/test/aug_images/' + img_path[0] + '-aug_pipline.jpg') except Exception as e: print(e) finally: data = Image.open(self.root + '/images/' + img_path[0] + '.jpg') #data = data.convert('RGB') #array = np.asarray(pil_img) #data = t.from_numpy(array) #plt.imshow(data) #plt.imshow(data) #data = Image.open(r'F:/5.datasets/test/aug_images/'+img_path[0] + '-aug_pipline.jpg') data = data.convert('RGB') if self.transforms: #data = self.transforms(TF.to_tensor(data)) data = self.transforms(data) #data = TF.to_tensor(self.augmentations.augment_image(data)) return data, label
def __getitem__(self, index): data = Image.open(os.path.join(self.file_path, self.ims[index])) bic_im = self.trans_bic(data) data = data.convert("YCbCr") data_y, cb, cr = data.split() data = self.trans(data_y) # batch must contain tensors, numbers, dicts or lists; # data_dict = {'bic':bic_im,'name':self.ims[index]} return data, bic_im, self.ims[index]
def __getitem__(self, index): sample = self.imgs[index] splits = sample.split() img_path = splits[0] data = Image.open(img_path) data = data.convert('L') data = self.transforms(data) label = np.int32(splits[1]) return data.float(), label
def __getitem__(self, index): sample = self.imgs[index] splits = sample.split() img_path = os.path.join(self.root, splits[0]) data = Image.open(img_path) data = data.convert('L') data = self.transforms(data) image1 = data img_path = os.path.join(self.root, splits[1]) data = Image.open(img_path) data = data.convert('L') data = self.transforms(data) image2 = data img_path = os.path.join(self.pd_root, splits[0]) pdimg = Image.open(img_path) pdimg = pdimg.convert('L') pdimg1 = self.pd_transforms(pdimg) img_path = os.path.join(self.pd_root, splits[1]) pdimg = Image.open(img_path) pdimg = pdimg.convert('L') pdimg2 = self.pd_transforms(pdimg) pdimg1 = pdimg1[:, 1::4, 1::4] pdimg2 = pdimg2[:, 1::4, 1::4] pdimg1[pdimg1 > 165 / 256] = pdimg1[pdimg1 > 165 / 256] - 1 pdimg1[pdimg1 > 90 / 256] = 180 / 256 pdimg1 = torch.cat((torch.cos(pdimg1 * 256 / 180 * math.pi), torch.sin(pdimg1 * 256 / 180 * math.pi)), 0) pdimg2[pdimg2 > 165 / 256] = pdimg2[pdimg2 > 165 / 256] - 1 pdimg2[pdimg2 > 90 / 256] = 180 / 256 pdimg2 = torch.cat((torch.cos(pdimg2 * 256 / 180 * math.pi), torch.sin(pdimg2 * 256 / 180 * math.pi)), 0) dx = np.int32(splits[2]) dy = np.int32(splits[3]) da = np.int32(splits[4]) label = torch.FloatTensor([dx/25.0, dy/25.0, da/20]) return image1.float(), image2.float(), (pdimg1.float(), pdimg2.float()), label
def __getitem__(self, index): data = self.index_to_data[index] img_path, label = data[self.img_colname], data[self.label_colname] if self.relative_path: img_path = os.path.join(self.root_path, img_path) data = Image.open(img_path) data = data.convert(self.img_to) data = self.transforms(data) return data, label
def __getitem__(self, index): img_path = self.imgs[index] label = int(img_path.strip().split('/')[10]) print(img_path, label) #data = Image.open(img_path) data = io.imread(img_path) data = Image.fromarray(data) if data.getbands()[0] == 'L': data = data.convert('RGB') data = self.transforms(data) return data, label
def __getitem__(self, index): """ 一次返回一张图片的数据 """ img_path = self.root + str(self.imgs[index]) + '.jpg' data = Image.open(img_path) data = data.convert('RGB') data = self.transforms(data) label = self.labels[index] return data, label
def __getitem__(self, index): ImageFile.LOAD_TRUNCATED_IMAGES = True img_path = self.imgs[index] if self.test: label = img_path.split('\\')[-1] else: label = int(img_path.split('\\')[-2]) - 1 with Image.open(img_path) as data: data = data.convert('RGB') data = self.transforms(data) return data, label
def __getitem__(self, index): """ 一次返回一张图片的数据 """ img_path = self.imgs[index] label = self.labels[index] # img = cv2.imread(img_path) # img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) data = Image.open(img_path) data = data.convert("RGB") # 如果有4通道图片转化为3通道 data = self.transforms(data) return data, label, img_path, opt.cate_classes[label] # 返回数据级标签图片路径
def __getitem__(self, index): sample = self.imgs[index] splits = sample.split() img_path = os.path.join(self.root, splits[0]) data = Image.open(img_path) data = data.convert('L') data = self.transforms(data) image1 = data img_path = os.path.join(self.root, splits[1]) data = Image.open(img_path) data = data.convert('L') data = self.transforms(data) image2 = data dx = np.int32(splits[2]) dy = np.int32(splits[3]) da = np.int32(splits[4]) label = torch.FloatTensor([dx/50.0, dy/50.0, da/40]) return image1.float(), image2.float(), label
def __getitem__(self, index): sample = self.imgs[index] if self.phase == 'train': splits = sample.split('/')[-1] label = int(splits.split('_')[0]) img_path = sample data = Image.open(img_path).convert('RGB') data = self.transforms(data) return data, label else: data = Image.open(sample) data = data.convert('RGB') data = self.transforms(data) return data
def __getitem__(self, index): #print(self.dict_labels) #print(lst) img_path = self.lst[index] #dog->1,cat->0 label = self.lst[index][1] data = Image.open(self.root + '/images/' + img_path[0] + '.jpg') data = data.convert('RGB') #array = np.asarray(pil_img) #data = t.from_numpy(array) if self.transforms: data = self.transforms(data) return data, label
def __getitem__(self, index): ''' return the data of one image ''' data_path = self.images_root + 'testimg/' + self.images_path[index] data = Image.open(data_path) data = data.convert('RGB') Data = self.transforms_data(data) # *Img.getcolors() # Label = self.transforms_label(label) return Data, str(self.images_path[index])
def __getitem__(self, index): sample = self.imgs[index] if self.phase == 'train': splits = sample.split('/', 6)[-1] #分割6次,可变 label = self.dic[splits] img_path = sample data = Image.open(img_path) # data = data.convert('L') data = self.transforms(data) return data.float(), label else: data = Image.open(sample) data = data.convert('RGB') #<----------------------- data = self.transforms(data) return data
def read_throw(self, index): splits = self.imgs[index] bb = self.bbs[index] img_path = splits[0] data = Image.open(img_path) data.load() data = data.convert('RGB') data = data.crop(self.refine_bb(bb, data.size)) if self.transforms is not None: data = self.transforms(data) label = int(splits[-1]) return data, label
def __getitem__(self, index): sample = self.imgs[index] splits = sample.split() img_path = splits[0] data = Image.open(img_path) data = data.convert('RGB') data = self.rand_augment(data) data = self.transforms(data) label = np.int32(splits[1]) if label > (self.NUM_CLASSES - 1): label = (self.NUM_CLASSES - 1) levels = [1] * label + [0] * (self.NUM_CLASSES - 1 - label) levels = torch.tensor(levels, dtype=torch.float32) return data, label, levels
def __getitem__(self, index): sample_path = self.imgs[index] # 当前图片的路径 sample_dir = os.path.dirname(sample_path) # 当前图片的上级目录的路径 # 标签必须是从0~classNums的连续整数 class_name = sample_dir.split(self.path_spilt)[-1] # 获取类名(当前图片的文件夹名字) label = self.classes.index(class_name) # 根据类名从列表里找到它的索引, 把索引号当作标签 data = Image.open(sample_path) # Image打开返回的是RGB (H , W , C) if self.input_shape[0] is 1: # 输入是1维, 则转成灰度图 data = data.convert( 'L') # 转为灰度图像(1维), 公式L = R*0.299 + G*0.587+ B*0.114 data = self.transforms(data) return data.float(), label
def __getitem__(self, index): """ 一次返回一张图片的数据 """ img_path = self.imgs[index] if self.test: # label = self.imgs[index].split('.')[-2].split('/')[-1] label = img_path.split('/')[-1] else: label = self.labels[index] data = Image.open(img_path) if opt.gray == True: dataRGB = data.convert('RGB') dataRGB = self.transforms(dataRGB) return dataRGB, label data = self.transforms(data) return data, label
def __getitem__(self, index): sample = self.imgs[index] splits = sample.split() img_path = splits[0] data = Image.open(img_path) if self.input_shape[0] == 1: # channel = 1 data = data.convert('L') data = self.transforms(data) # data_view = data.cpu().numpy().transpose(1, 2, 0) # plt.imshow(data_view) # # plt.axis('off') # plt.savefig(f'datasets/test/test_m{index}.jpg', bbox_inches='tight') # plt.show() if self.phase == 'test': return data.float() else: label = np.int32(splits[1]) return data.float(), label
def __getitem__(self, index): img_path = self.imgs[index] label = self.labels[index] # 提取特征 if self.extract_feature: label = self.imgs[index].rsplit('/', 1)[-1] try: data = Image.open(img_path) except Exception as e: print(e) else: if len(data.split())!=3: data = data.convert('RGB') #print(len(data.split())) if self.transforms: data = self.transforms(data) return data, label return torch.zeros(3, 224, 224), 0