这段代码报错的原因是什么，请做出修改
import os
import numpy as np
from PIL import Image
import json


def load_image(self, img_path, bg_color=(1,1,1), alpha=None, return_type='np'):
    # not using cv2 as may load in uint16 format
    # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
    # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
    # pil always returns uint8
    img = np.array(Image.open(img_path).resize(self.img_wh))
    img = img.astype(np.float32) / 255. # [0, 1]
    assert img.shape[-1] == 3 or img.shape[-1] == 4 # RGB or RGBA

    if alpha is None and img.shape[-1] == 4:
        alpha = img[:, :, 3:]
        img = img[:, :, :3]

    if alpha.shape[-1] != 1:
        alpha = alpha[:, :, None]

    img = img[...,:3] * alpha + bg_color * (1 - alpha)

    if return_type == "np":
        pass
    elif return_type == "pt":
        img = torch.from_numpy(img)
    else:
        raise NotImplementedError

    return img


def has_high_percentage_of_colors(img,
                                  colors,
                                  threshold_percentage,
                                  tolerance=3):
    """
    检查图像中指定颜色的像素数量是否超过阈值百分比。

    Args:
        img (numpy.ndarray): 图像数组，形状为 (height, width, channels)，数据类型为 uint8，像素值范围 [0, 255]。
        colors (list of list): 需要检查的像素值列表，每个颜色是一个长度为 3 的列表。
        threshold_percentage (float): 阈值百分比，例如 25 表示 25%。

    Returns:
        bool: 如果指定颜色的像素数量超过阈值百分比，返回 True；否则返回 False。
    """
    # 展开图像为二维数组，每行一个像素
    pixels = img.reshape(-1, img.shape[-1])  # 形状为 (像素数, 3)
    pixels = pixels.astype(np.int16)

    # 总像素数
    total_pixels = pixels.shape[0]
    matched_pixels = 0

    # 将颜色列表转换为 numpy 数组
    colors = np.array(colors, dtype=np.int16)

    # 对于每个颜色，计算匹配的像素数量
    for color in colors:
        # 计算颜色的容差范围，确保不超出 [0, 255]
        lower_bound = np.maximum(color - tolerance, 0)
        upper_bound = np.minimum(color + tolerance, 255)
        for pixel in pixels :
            print(pixel,"  ")
        # 生成逻辑掩码，判断每个像素在各通道上是否在容差范围内
        channel_matches = np.logical_and(pixels >= lower_bound, pixels
                                         <= upper_bound)  # 形状为 (像素数, 3)

        # 找到在所有通道上都匹配的像素
        matches = np.all(channel_matches, axis=1)  # 形状为 (像素数,)

        # 统计匹配的像素数量
        matched_pixels += np.sum(matches)

    # 计算匹配的像素占比
    percentage = (matched_pixels / total_pixels) * 100
    print(f"匹配的像素占比: {percentage:.2f}%")

    # 判断是否超过阈值
    return percentage >= threshold_percentage

# 定义视角类型和其他参数
view_types = ['front', 'front_right', 'right', 'back', 'left', 'front_left']
set_idx = 0
suffix = 'png'
img_wh = (256, 256)  # 图像宽高
bg_color = (1, 1, 1)  # 背景颜色（此处未使用）
alpha = None  # 透明度（此处未使用）

# 定义需要检查的颜色
allowed_colors = [
    [133, 133, 133,0],  # 第一个像素值
    [1, 1, 1,255]     # 第二个像素值
]

# 定义阈值百分比
threshold_percentage = 95  # 25%

# 读取 UID 和路径数据
uid_file = "/data/fl/dataset/objaverse/objaverse-list/half-part-objaverse-list/lvis-annotations-filtered-part-half.json"  # 44798
paths_file = "/data/fl/dataset/objaverse/object-paths.json"
uid_data = json.load(open(uid_file))
paths_data = json.load(open(paths_file))

uids_to_save = []  # 用于保存符合条件的 UID
uid="b8736912c99e47f2ae635c9609a803d7"
# for uid in uid_data:
path = paths_data.get(uid)
    # if not path:
    #     print(f"未找到 uid 的路径: {uid}")
    #     continue
object_dir = os.path.join("/data/fl/dataset/objaverse/lvis-subset/out_renderings/", path[5:-4])

    # 检查对象目录是否存在
    # if not os.path.exists(object_dir):
    #     print(f"未找到 uid 的对象目录: {uid}")
    #     continue

found = False  # 标志位，表示是否找到超过阈值的图像

for cond_view in view_types:
    # 构建图像路径
    img_name = "rgb_%03d_%s.%s" % (set_idx, cond_view, suffix)
    img_path = os.path.join(object_dir, img_name)
    if not os.path.exists(img_path):
        print(f"未找到图像: {img_path}")
        continue
    # 加载图像
    try:
        img = load_image(img_path,
                              bg_color,
                              alpha,
                              return_type='np')
    except Exception as e:
        print(f"加载图像时出错 {img_path}: {e}")
        continue
    # 检查图像中指定颜色的像素数量是否超过阈值
    if has_high_percentage_of_colors(img, allowed_colors, threshold_percentage):
        uids_to_save.append(uid)
        found = True
        print(f"UID {uid}: 图像 {img_name} 中指定颜色的像素占比超过 {threshold_percentage}%")
        break  # 不需要检查该 uid 的其他图像
if not found:
    print(f"UID {uid} 没有符合条件的图像")

# 将结果保存到 JSON 文件
output_file = "uids_with_high_percentage_of_colors.json"
with open(output_file, "w") as f:
    json.dump(uids_to_save, f)

print(f"符合条件的 UID 已保存到 '{output_file}'")

加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_front.png: 'tuple' object has no attribute 'read'
加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_front_right.png: 'tuple' object has no attribute 'read'
加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_right.png: 'tuple' object has no attribute 'read'
加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_back.png: 'tuple' object has no attribute 'read'
加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_left.png: 'tuple' object has no attribute 'read'
加载图像时出错 /data/fl/dataset/objaverse/lvis-subset/out_renderings/000-082/b8736912c99e47f2ae635c9609a803d7/rgb_000_front_left.png: 'tuple' object has no attribute 'read'
UID b8736912c99e47f2ae635c9609a803d7 没有符合条件的图像
符合条件的 UID 已保存到 'uids_with_high_percentage_of_colors.json'