Уварова Елизавета Сергеевна

.gitignore

@@ -0,0 +1 @@
+__pycache__/*

filter.py

@@ -1,28 +1,161 @@
-from PIL import Image
+from argparse import ArgumentParser, Namespace
+from typing import Tuple
+
 import numpy as np
-img = Image.open("img2.jpg")
-arr = np.array(img)
-a = len(arr)
-a1 = len(arr[1])
-i = 0
-while i < a - 11:
-    j = 0
-    while j < a1 - 11:
-        s = 0
-        for n in range(i, i + 10):
-            for n1 in range(j, j + 10):
-                n1 = arr[n][n1][0]
-                n2 = arr[n][n1][1]
-                n3 = arr[n][n1][2]
-                M = n1 + n2 + n3
-                s += M
-        s = int(s // 100)
-        for n in range(i, i + 10):
-            for n1 in range(j, j + 10):
-                arr[n][n1][0] = int(s // 50) * 50
-                arr[n][n1][1] = int(s // 50) * 50
-                arr[n][n1][2] = int(s // 50) * 50
-        j = j + 10
-    i = i + 10
-res = Image.fromarray(arr)
-res.save('res.jpg')
+from PIL import Image
+
+
+class ValidationError(Exception):
+    pass
+
+
+def _save_image(image_array: np.ndarray, output_image_path: str) -> None:
+    """
+    Сохранить изображение по указанному пути
+    """
+    res = Image.fromarray(image_array)
+    res.save(output_image_path)
+
+
+def _load_image(input_image_path: str) -> np.ndarray:
+    """
+    Загрузить изображение по указанному пути
+    """
+    img = Image.open(input_image_path)
+    return np.array(img, dtype=np.uint8)
+
+
+def _get_and_validate_size(
+    pixel_matrix: np.ndarray, segment_size: int
+) -> Tuple[int, int]:
+    """
+    Получение и валидация размеров матрицы пикселей
+    """
+    rows_count = len(pixel_matrix)
+
+    if not rows_count:
+        raise ValidationError("Изображение пустое")
+
+    columns_count = len(pixel_matrix[0])
+
+    if rows_count % segment_size != 0 or columns_count % segment_size != 0:
+        raise ValidationError(
+            f"Невозможно разделить изображение размером: ({rows_count}, "
+            "{columns_count}) на сегменты размером: {segment_size}"
+        )
+
+    return rows_count, columns_count
+
+
+def _calculate_mean_by_sector(
+    pixel_matrix: np.ndarray, begin_corrdinates: Tuple[int, int], segment_size: int
+) -> int:
+    min_row_number, min_column_number = begin_corrdinates
+    return int(
+        pixel_matrix[
+            min_row_number : min_row_number + segment_size,
+            min_column_number : min_column_number + segment_size,
+        ].mean()
+    )
+
+
+def _calculate_grayscale_gradation(
+    mean_mosaic_segment_value: int, grayscale_gradation: int
+) -> int:
+    if mean_mosaic_segment_value < grayscale_gradation:
+        return 0
+
+    return (mean_mosaic_segment_value // grayscale_gradation) * grayscale_gradation
+
+
+def _insert_grayscale_segment(
+    pixel_matrix: np.ndarray,
+    begin_corrdinates: Tuple[int, int],
+    segment_size: int,
+    grayscale_gradation_value: int,
+) -> None:
+    min_row_number, min_column_number = begin_corrdinates
+    pixel_matrix[
+        min_row_number : min_row_number + segment_size,
+        min_column_number : min_column_number + segment_size,
+    ][...].fill(grayscale_gradation_value)
+
+
+def _create_pixel_art(
+    input_image_path: str,
+    output_image_path: str,
+    segment_size: int = 10,
+    grayscale_gradation: int = 50,
+) -> None:
+    pixel_matrix = _load_image(input_image_path)
+
+    rows_count, columns_count = _get_and_validate_size(pixel_matrix, segment_size)
+
+    min_row_number = 0
+
+    while min_row_number < rows_count:
+        min_column_number = 0
+
+        while min_column_number < columns_count:
+            mean_mosaic_segment_value = _calculate_mean_by_sector(
+                pixel_matrix, (min_row_number, min_column_number), segment_size
+            )
+            grayscale_gradation_value = _calculate_grayscale_gradation(
+                mean_mosaic_segment_value, grayscale_gradation
+            )
+
+            _insert_grayscale_segment(
+                pixel_matrix,
+                (min_row_number, min_column_number),
+                segment_size,
+                grayscale_gradation_value,
+            )
+
+            min_column_number = min_column_number + segment_size
+        min_row_number = min_row_number + segment_size
+
+    _save_image(pixel_matrix, output_image_path)
+
+
+def _parser() -> Namespace:
+    parser = ArgumentParser(description="Преобразование изображения в пиксель арт")
+    parser.add_argument(
+        "-i",
+        "--input-image",
+        type=str,
+        default="./docs/examples/img2.jpg",
+        help="Путь до ихображения которое вы хотите преобразовать",
+    )
+    parser.add_argument(
+        "-o",
+        "--output-image",
+        type=str,
+        default="./docs/examples/res.jpg",
+        help="Путь, куда вы хотите сохранить изображение",
+    )
+    parser.add_argument(
+        "-s",
+        "--segment-size",
+        type=int,
+        default=10,
+        help="Размер сегмента мозаики",
+    )
+    parser.add_argument(
+        "-g",
+        "--grayscale-gradation",
+        type=int,
+        default=50,
+        help="Градация серого цвета",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = _parser()
+    _create_pixel_art(
+        args.input_image, args.output_image, args.segment_size, args.grayscale_gradation
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.dev.txt

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+# formatters
+isort==5.10.1
+black==21.10b0
+
+# linters
+pylint==2.11.1
+mypy==0.910

requirements.txt

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+numpy==1.21.4
+Pillow==8.4.0