### 进行错误诊断测试
def test(img_path):
    crop_size = 128
    resize_size = 128
    img = Image.open(img_path)
    # print(img_path)
    # 统一图片大小
    img = img.resize((resize_size, resize_size), Image.ANTIALIAS)
    # 随机水平翻转
    # r1 = random.random()
    # if r1 > 0.5:
    #     img = img.transpose(Image.FLIP_LEFT_RIGHT)
    # # 随机垂直翻转
    # r2 = random.random()
    # if r2 > 0.5:
    #     img = img.transpose(Image.FLIP_TOP_BOTTOM)
    # # 随机角度翻转
    # r3 = random.randint(-3, 3)
    # img = img.rotate(r3, expand=False)
    # # # 随机裁剪
    # # r4 = random.randint(0, int(resize_size - crop_size))
    # # r5 = random.randint(0, int(resize_size - crop_size))
    # # box = (r4, r5, r4 + crop_size, r5 + crop_size)
    # # img = img.crop(box)
    # # 把图片转换成numpy值
    # img = np.array(img).astype(np.float32)
    # # 转换成CHW
    # img = img.transpose((2, 0, 1))
    # # 转换成BGR
    # img = img[(2, 1, 0), :, :] / 255.0
    return img

def load_image(file):
        #打开图片
        im = Image.open(file)
        #将图片调整为跟训练数据一样的大小  32*32，                   设定ANTIALIAS，即抗锯齿.resize是缩放
        im = im.resize((32, 32), Image.ANTIALIAS)
        #建立图片矩阵 类型为float32
        im = np.array(im.convert("RGB")).astype(np.float32)
        #矩阵转置 
        im = im.transpose((2, 0, 1))                               
        #将像素值从【0-255】转换为【0-1】
        im = im / 255.0
        #print(im)       
        im = np.expand_dims(im, axis=0)
        # 保持和之前输入image维度一致
        print('im_shape的维度：',im.shape)
        return im

bb = r'cat_12_train/tO6cKGH8uPEayzmeZJ51Fdr2Tx3fBYSn.jpg'
aa = r'cat_12_train/hwQDH3VBabeFXISfjlWEmYicoyr6qK1p.jpg'
load_image(aa)
img = Image.open(bb)
plt.imshow(img)   
plt.show()
load_image(bb)

# if not os.path.exists(bb):
#     print('不存在')
# else:
#     img = Image.open(bb)
#     plt.imshow(img)   
#     plt.show()
#     img = test(bb)
#     plt.imshow(img)   
#     plt.show()
#     load_image(bb)
    
def test2(sample):
    img = sample
    try:
        img = paddle.dataset.image.load_image(file=img, is_color=True)
        plt.imshow(img)   
        plt.show()
        img = paddle.dataset.image.simple_transform(im=img, resize_size=resize_size, crop_size=crop_size, is_color=True, is_train=True)
        img = img.flatten().astype('float32') / 255.0
        return img
    except Exception as err:
        print(sample)
        print(err)

bb = r'cat_12_train/tO6cKGH8uPEayzmeZJ51Fdr2Tx3fBYSn.jpg'
test2(bb);





### 预测测试
# test_infer_path = []
# test_infer_path.append(r'cat_12_train/hwQDH3VBabeFXISfjlWEmYicoyr6qK1p.jpg')
# test_infer_path.append(r'cat_12_train/u9RFI6LdD7xi8YrKTtfNBE2qX0ZUsheg.jpg')
# test_infer_path.append(r'cat_12_train/bEkVJ6cdRWyXjl842umngODsBfhTIUte.jpg')
# test_infer_path.append(r'cat_12_train/BjWieGnrq0zpFaAmNyTvLfc47hOKRgSM.jpg')
# test_infer_path.append(r'cat_12_train/Q8mkFIpAR9qMzJEaijPlOvcNGWBf425o.jpg')
# test_infer_path.append(r'cat_12_train/N5p3cFKAMiseqtZDUayRPkX60YuHdoT9.jpg')
# test_infer_path.append(r'cat_12_train/2xvtgnXlHws7r3LQZ9PoNi6qJ51OW0ed.jpg')
# test_infer_path.append(r'cat_12_train/3ITPOakWJShC2UtZeLNoGn8fbdRxDHjw.jpg')
# test_infer_path.append(r'cat_12_train/qNy8KsJC9GiO6rLmbgpneAE2tjcHVhfZ.jpg')
# test_infer_path.append(r'cat_12_train/0iYHbBF5vXlG7u3DC6p9mc2j1xItRWZS.jpg')
# test_infer_path.append(r'cat_12_train/kBQV7YS3rgW2oJEesimD5Pnx0hpuZRH9.jpg')
# test_infer_path.append(r'cat_12_train/yvQ8O27RFKHrcZu93TgYEJA4hGqbenSX.jpg')
test_infer_path = []
label_infer = []
with open('','r') as f:
    for line in f.readlines():
        img_path,label = line.split('\t')
        test_infer_path.append(img_path)
        label_infer.append(label)


with fluid.scope_guard(inference_scope):
    #从指定目录中加载 推理model(inference model)
    [inference_program, # 预测用的program
     feed_target_names, # 是一个str列表，它包含需要在推理 Program 中提供数据的变量的名称。 
     fetch_targets] = fluid.io.load_inference_model(model_save_dir,#fetch_targets：是一个 Variable 列表，从中我们可以得到推断结果。
                                                    infer_exe)     #infer_exe: 运行 inference model的 executor
    count = 0
    for infer_path in test_infer_path:
        img = load_image(infer_path)
        results = infer_exe.run(inference_program,                 #运行预测程序
                                        feed={feed_target_names[0]: img},  #喂入要预测的img
                                        fetch_list=fetch_targets)          #得到推测结果
        


data_imgs = os.listdir('cat_12_test')
# print(data_imgs)
img_label = {}
with open('test.csv','r') as f:
    lines =  f.readlines()
    for line in lines:
        img , label = line.split(',')
        img_label[img] = label
print(len(img_label))
key = img_label.keys()
for i in data_imgs:
    if i not in key:
        print(i)
with open('testout.csv','w') as f:
    for i in data_imgs:
        f.write(i+','+img_label[i])
print('修改完成')