Update pixelation.py

pixelation.py

@@ -1,128 +1,128 @@
-from PIL import Image
-import numpy as np
-from collections import Counter
-
-def pixelate_image(image, pixel_size, interpolation="Nearest"):
-    """
-    对图像进行像素化。
-
-    参数:
-    - image: 输入的 PIL 图像对象
-    - pixel_size: 像素块大小
-    - interpolation: 插值方法 ("Nearest", "Bilinear", "Bicubic", "Lanczos")
-
-    返回:
-    - 像素化后的 PIL 图像对象
-    """
-    # 将输入图像转为 RGB 模式
-    img = image.convert("RGB")
-
-    # 获取原图像的尺寸
-    width, height = img.size
-    pixel_size = max(1, round(min(width, height) / 1024) * pixel_size)
-
-    # 选择插值方式
-    if interpolation == "Nearest":
-        resample_method = Image.NEAREST
-    elif interpolation == "Bilinear":
-        resample_method = Image.BILINEAR
-    elif interpolation == "Bicubic":
-        resample_method = Image.BICUBIC
-    elif interpolation == "Lanczos":
-        resample_method = Image.LANCZOS
-    else:
-        raise ValueError(f"未知的插值方法: {interpolation}")
-
-    # 第一步：缩小图像，使用邻近插值保持像素块的正方形效果
-    small_img = img.resize(
-        (width // pixel_size, height // pixel_size),
-        resample=resample_method
-    )
-
-    # 第二步：放大图像，使用用户选择的插值方法
-    pixelated_img = small_img.resize(
-        (width, height),
-        resample=resample_method
-    )
-
-    return pixelated_img
-
-
-def mosaic_pixelation(image, pixel_size):
-    """
-    使用马赛克方法对图像进行像素化。
-
-    参数:
-    - image: 输入的 PIL 图像对象
-    - pixel_size: 像素块大小
-
-    返回:
-    - 马赛克效果的 PIL 图像对象
-    """
-    img = image.convert("RGB")
-    img_np = np.array(img)
-    h, w, _ = img_np.shape
-    pixel_size = round(min(w, h) / 1024) * pixel_size
-
-    for y in range(0, h, pixel_size):
-        for x in range(0, w, pixel_size):
-            block = img_np[y:y + pixel_size, x:x + pixel_size]
-            mean_color = block.mean(axis=(0, 1)).astype(int)
-            img_np[y:y + pixel_size, x:x + pixel_size] = mean_color
-
-    return Image.fromarray(img_np)
-
-
-def oil_paint_pixelation(image, pixel_size):
-    """
-    使用油画滤镜方法对图像进行像素化。
-
-    参数:
-    - image: 输入的 PIL 图像对象
-    - pixel_size: 像素块大小
-
-    返回:
-    - 油画滤镜效果的 PIL 图像对象
-    """
-    img = image.convert("RGB")
-    img_np = np.array(img)
-    h, w, _ = img_np.shape
-    pixel_size = round(min(w, h) / 1024) * pixel_size
-    for y in range(0, h, pixel_size):
-        for x in range(0, w, pixel_size):
-            block = img_np[y:y + pixel_size, x:x + pixel_size]
-            block_colors = [tuple(color) for color in block.reshape(-1, 3)]
-            most_common_color = Counter(block_colors).most_common(1)[0][0]
-            img_np[y:y + pixel_size, x:x + pixel_size] = most_common_color
-
-    return Image.fromarray(img_np)
-
-
-def hierarchical_pixelation(image, min_pixel_size, max_pixel_size):
-    """
-    使用层次像素化方法对图像进行像素化。
-
-    参数:
-    - image: 输入的 PIL 图像对象
-    - min_pixel_size: 最小像素块大小
-    - max_pixel_size: 最大像素块大小
-
-    返回:
-    - 层次像素化效果的 PIL 图像对象
-    """
-    img = image.convert("RGB")
-    img_np = np.array(img)
-    h, w, _ = img_np.shape
-    min_pixel_size = round(min(w, h) / 1024) * min_pixel_size
-    max_pixel_size = round(min(w, h) / 1024) * max_pixel_size
-
-    step = max((max_pixel_size - min_pixel_size) // (w // min_pixel_size), 1)
-
-    for pixel_size in range(min_pixel_size, max_pixel_size + 1, step):
-        for y in range(0, h, pixel_size):
-            for x in range(0, w, pixel_size):
-                block = img_np[y:y + pixel_size, x:x + pixel_size]
-                mean_color = block.mean(axis=(0, 1)).astype(int)
-                img_np[y:y + pixel_size, x:x + pixel_size] = mean_color
-
+from PIL import Image
+import numpy as np
+from collections import Counter
+
+def pixelate_image(image, pixel_size, interpolation="Nearest"):
+    """
+    对图像进行像素化。
+
+    参数:
+    - image: 输入的 PIL 图像对象
+    - pixel_size: 像素块大小
+    - interpolation: 插值方法 ("Nearest", "Bilinear", "Bicubic", "Lanczos")
+
+    返回:
+    - 像素化后的 PIL 图像对象
+    """
+    # 将输入图像转为 RGB 模式
+    img = image.convert("RGB")
+
+    # 获取原图像的尺寸
+    width, height = img.size
+    # pixel_size = max(1, round(min(width, height) / 1024) * pixel_size)
+
+    # 选择插值方式
+    if interpolation == "Nearest":
+        resample_method = Image.NEAREST
+    elif interpolation == "Bilinear":
+        resample_method = Image.BILINEAR
+    elif interpolation == "Bicubic":
+        resample_method = Image.BICUBIC
+    elif interpolation == "Lanczos":
+        resample_method = Image.LANCZOS
+    else:
+        raise ValueError(f"未知的插值方法: {interpolation}")
+
+    # 第一步：缩小图像，使用邻近插值保持像素块的正方形效果
+    small_img = img.resize(
+        (width // pixel_size, height // pixel_size),
+        resample=resample_method
+    )
+
+    # 第二步：放大图像，使用用户选择的插值方法
+    pixelated_img = small_img.resize(
+        (width, height),
+        resample=resample_method
+    )
+
+    return pixelated_img
+
+
+def mosaic_pixelation(image, pixel_size):
+    """
+    使用马赛克方法对图像进行像素化。
+
+    参数:
+    - image: 输入的 PIL 图像对象
+    - pixel_size: 像素块大小
+
+    返回:
+    - 马赛克效果的 PIL 图像对象
+    """
+    img = image.convert("RGB")
+    img_np = np.array(img)
+    h, w, _ = img_np.shape
+    pixel_size = round(min(w, h) / 1024) * pixel_size
+
+    for y in range(0, h, pixel_size):
+        for x in range(0, w, pixel_size):
+            block = img_np[y:y + pixel_size, x:x + pixel_size]
+            mean_color = block.mean(axis=(0, 1)).astype(int)
+            img_np[y:y + pixel_size, x:x + pixel_size] = mean_color
+
+    return Image.fromarray(img_np)
+
+
+def oil_paint_pixelation(image, pixel_size):
+    """
+    使用油画滤镜方法对图像进行像素化。
+
+    参数:
+    - image: 输入的 PIL 图像对象
+    - pixel_size: 像素块大小
+
+    返回:
+    - 油画滤镜效果的 PIL 图像对象
+    """
+    img = image.convert("RGB")
+    img_np = np.array(img)
+    h, w, _ = img_np.shape
+    pixel_size = round(min(w, h) / 1024) * pixel_size
+    for y in range(0, h, pixel_size):
+        for x in range(0, w, pixel_size):
+            block = img_np[y:y + pixel_size, x:x + pixel_size]
+            block_colors = [tuple(color) for color in block.reshape(-1, 3)]
+            most_common_color = Counter(block_colors).most_common(1)[0][0]
+            img_np[y:y + pixel_size, x:x + pixel_size] = most_common_color
+
+    return Image.fromarray(img_np)
+
+
+def hierarchical_pixelation(image, min_pixel_size, max_pixel_size):
+    """
+    使用层次像素化方法对图像进行像素化。
+
+    参数:
+    - image: 输入的 PIL 图像对象
+    - min_pixel_size: 最小像素块大小
+    - max_pixel_size: 最大像素块大小
+
+    返回:
+    - 层次像素化效果的 PIL 图像对象
+    """
+    img = image.convert("RGB")
+    img_np = np.array(img)
+    h, w, _ = img_np.shape
+    min_pixel_size = round(min(w, h) / 1024) * min_pixel_size
+    max_pixel_size = round(min(w, h) / 1024) * max_pixel_size
+
+    step = max((max_pixel_size - min_pixel_size) // (w // min_pixel_size), 1)
+
+    for pixel_size in range(min_pixel_size, max_pixel_size + 1, step):
+        for y in range(0, h, pixel_size):
+            for x in range(0, w, pixel_size):
+                block = img_np[y:y + pixel_size, x:x + pixel_size]
+                mean_color = block.mean(axis=(0, 1)).astype(int)
+                img_np[y:y + pixel_size, x:x + pixel_size] = mean_color
+
     return Image.fromarray(img_np)