test-10 1.0.1

Sprite detection package

Sprite Detection

Features

• Find the Most Common Color in an Image.
• Find Sprites in an Image.
• Draw Sprite Label Bounding Boxes.

Why this project is useful?

• Used reasonable library for process Image.
• Can be used on a big image.
• Easy to understand.

Usage

• Find the Most Common Color in an Image

>>> from PIL import Image
# JPEG image
>>> image = Image.open('first_image.jpg')
>>> image.mode
'RGB'
>>> find_most_common_color(image)
(0, 221, 204)
# PNG image
>>> image = Image.open('second_image.png')
>>> image.mode
'RGBA'
>>> find_most_common_color(image)
(0, 0, 0, 0)
# Grayscale image
>>> image = image.convert('L')
>>> image.mode
'L'
>>> find_most_common_color(image)
0

• Find Sprites in an Image.

>>> from PIL import Image
>>> image = Image.open('metal_slug_single_sprite.png')
>>> sprites, label_map = find_sprites(image, background_color=(255, 255, 255))
>>> len(sprites)
1
>>> for label, sprite in sprites.items():
...     print(f"Sprite ({label}): [{sprite.top_left}, {sprite.bottom_right}] {sprite.width}x{sprite.height}")
Sprite (1): [(0, 0), (29, 37)] 30x38
>>> import pprint
>>> pprint.pprint(label_map, width=120)
[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0],
 [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0],
 [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],
 [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,0],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0],
 [0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0],
 [0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [0,0,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
 [0,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0],
 [1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0]]

Other example with the following image:

>>> from PIL import Image
>>> image = Image.open('optimized_sprite_sheet.png')
>>> sprites, label_map = find_sprites(image)
>>> len(sprites)
22
>>> for label, sprite in sprites.items():
...     print(f"Sprite ({label}): [{sprite.top_left}, {sprite.bottom_right}] {sprite.width}x{sprite.height}")
Sprite (25): [(383, 1), (455, 102)] 73x102
Sprite (43): [(9, 2), (97, 122)] 89x121
Sprite (26): [(110, 4), (195, 123)] 86x120
Sprite (46): [(207, 4), (291, 123)] 85x120
Sprite (16): [(305, 8), (379, 123)] 75x116
Sprite (53): [(349, 125), (431, 229)] 83x105
Sprite (61): [(285, 126), (330, 181)] 46x56
Sprite (100): [(1, 129), (101, 237)] 101x109
Sprite (106): [(106, 129), (193, 249)] 88x121
Sprite (93): [(183, 137), (278, 241)] 96x105
Sprite (95): [(268, 173), (355, 261)] 88x89
Sprite (178): [(6, 244), (101, 348)] 96x105
Sprite (185): [(145, 247), (245, 355)] 101x109
Sprite (141): [(343, 257), (417, 372)] 75x116
Sprite (169): [(102, 262), (142, 303)] 41x42
Sprite (188): [(249, 267), (344, 373)] 96x107
Sprite (192): [(412, 337), (448, 372)] 37x36
Sprite (256): [(89, 353), (184, 459)] 96x107
Sprite (234): [(11, 356), (104, 461)] 94x106
Sprite (207): [(188, 358), (281, 463)] 94x106
Sprite (229): [(384, 374), (456, 475)] 73x102
Sprite (248): [(286, 378), (368, 482)] 83x105

• Draw Sprite Label Bounding Boxes.

>>> from PIL import Image
>>> image = Image.open('optimized_sprite_sheet.png')
>>> sprites, label_map = find_sprites(image)
>>> # Draw sprite masks and bounding boxes with the default white background color.
>>> sprite_label_image = create_sprite_labels_image(sprites, label_map)
>>> sprite_label_image.save('optimized_sprite_sheet_bounding_box_white_background.png')
>>> # Draw sprite masks and bounding boxes with a transparent background color.
>>> sprite_label_image = create_sprite_labels_image(sprites, label_map, background_color=(0, 0, 0, 0))
>>> sprite_label_image.save('optimized_sprite_sheet_bounding_box_transparent_background.png')

Sprite Masks with White Background	Sprite Masks with Transparent Background

Built with

• Pillow
• Numpy

Pull requests welcome!

Spotted an error? Something doesn't make sense? Send me a pull request!

Support

Ask your question here: https://www.google.com/

Everyone can Maintains && Contributing

Just follow steps:

1. Fork it (https://github.com/intek-training-jsc/sprite-detection-masternhat.git)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE.md file for details