Synced repo using 'sync_with_huggingface' Github Action
Browse files- addbg.py +71 -0
- gradio_imager.py +106 -0
- image_processor.py +278 -0
- imager.py +47 -0
- requirements.txt +2 -0
addbg.py
ADDED
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import sys
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import argparse
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from PIL import Image, ImageStat, ImageEnhance
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def adjust_background_brightness(bg_image, mode):
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"""
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Adjusts the brightness of the background image based on the specified mode.
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Args:
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- bg_image (Image): The PIL Image object of the background.
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- mode (str): The mode for background adjustment ('lighter', 'darker').
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Returns:
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- Image: The adjusted PIL Image object.
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"""
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enhancer = ImageEnhance.Brightness(bg_image)
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if mode == 'lighter':
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return enhancer.enhance(1.5) # Increase brightness
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elif mode == 'darker':
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return enhancer.enhance(0.5) # Decrease brightness
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return bg_image # Return unmodified if mode is not recognized
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def generate_complementary_background(image_path, guidance):
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"""
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Generates a complementary background image based on guidance.
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Args:
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- image_path (str): Path to the input image.
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- guidance (str): Guidance for background generation ('lighter', 'darker', 'color').
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Returns:
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- Image: A PIL Image object of the generated background.
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"""
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with Image.open(image_path) as img:
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# Assuming a simplistic approach to generate a solid color background
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background = Image.new(
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'RGB', img.size, (255, 255, 255) if guidance == 'lighter' else (0, 0, 0))
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# Adjust the background based on guidance
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if guidance in ['lighter', 'darker']:
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background = adjust_background_brightness(background, guidance)
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# For color or other guidance, additional logic can be implemented
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return background
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def blend_images(foreground_path, output_path, guidance):
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"""
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Blends an input image with a dynamically generated background image based on guidance.
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Args:
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- foreground_path (str): Path to the input image with a transparent background.
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- output_path (str): Path where the blended image will be saved.
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- guidance (str): Guidance for background generation.
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"""
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background = generate_complementary_background(foreground_path, guidance)
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with Image.open(foreground_path).convert("RGBA") as foreground:
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background = background.convert("RGBA")
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blended_image = Image.alpha_composite(background, foreground)
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blended_image.convert("RGB").save(output_path, "PNG")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Blends an input image with a dynamically generated background image based on guidance.")
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parser.add_argument(
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"foreground", help="Path to the input image with a transparent background.")
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parser.add_argument("output", nargs='?', default="out.png",
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help="Path where the blended image will be saved. Defaults to 'out.png'.")
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parser.add_argument("--guidance", choices=['lighter', 'darker', 'color'], default='darker',
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help="Guidance for the type of background to generate ('lighter', 'darker', 'color'). Defaults to 'darker'.")
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args = parser.parse_args()
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blend_images(args.foreground, args.output, args.guidance)
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gradio_imager.py
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@@ -0,0 +1,106 @@
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import gradio as gr
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from PIL import Image
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import tempfile
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import os
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from image_processor import process_image
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def apply_standard_settings(setting):
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"""Returns the parameters for the selected standard setting."""
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settings_dict = {
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"S light": (True, True, "240x240", 48, "whitesmoke"),
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"M light": (True, True, "480x480", 96, "whitesmoke"),
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"L light": (True, True, "960x960", 128, "whitesmoke"),
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"S dark": (True, True, "240x240", 48, "#2A373D"),
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"M dark": (True, True, "480x480", 96, "#2A373D"),
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"L dark": (True, True, "960x960", 128, "#2A373D"),
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}
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# Default to no special settings
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return settings_dict.get(setting, (None, None, None, None, None))
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def settings_description(crop, remove_bg, resize, padding, background):
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"""Generate an HTML text description of the current settings in a smaller font and list format."""
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description = f"""
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<ul style="font-size:small;">
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<li>Crop: {crop}</li>
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<li>Remove Background: {remove_bg}</li>
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<li>Resize: {resize if resize else 'No resize'}</li>
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<li>Padding: {padding}</li>
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<li>Background: {background}</li>
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</ul>
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"""
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return description
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def gradio_interface(image, standard_settings, crop=False, remove_bg=False, resize=None, padding=0, background="white"):
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# Apply standard settings if selected and not "None"
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if image is None:
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# Load the standard image from the specified path if no image is uploaded
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standard_image_path = './data/examples/supermario.png'
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image = Image.open(standard_image_path)
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if standard_settings and standard_settings != "None":
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crop, remove_bg, resize, padding, background = apply_standard_settings(
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standard_settings)
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# Generate settings description
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applied_settings = settings_description(
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crop, remove_bg, resize, padding, background)
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# Convert resize string to tuple (if provided)
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resize_dimensions = None
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if resize:
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try:
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width, height = map(int, resize.split('x'))
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resize_dimensions = (width, height)
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except ValueError:
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return "Invalid format for resize dimensions. Please use 'WxH'.", "original", applied_settings
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# Process the image directly
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processed_image = process_image(
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image, crop, remove_bg, resize_dimensions, padding, background)
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# Generate settings description
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applied_settings = settings_description(
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crop, remove_bg, resize, padding, background)
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return processed_image, applied_settings
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example_images = [
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[os.path.join("data", "examples", "supermario.png"),
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"S light", True, True, "480x420", 10, "whitesmoke"],
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[os.path.join("data", "examples",
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"depositphotos_520707962-stock-photo-fujifilm-s10-body-black-fujifilm.jpg"), "None", True, True, "480x320", 48, "blue"],
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[os.path.join("data", "examples", "batman_b_c_320x280_bg.png"),
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"None", True, True, "360x360", 48, "yellow"],
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]
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# Define the Gradio interface
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interface = gr.Interface(fn=gradio_interface,
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inputs=[
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gr.components.Image(
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type="pil", label="Input Image"),
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gr.components.Radio(choices=[
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"None", "S light", "M light", "L light", "S dark", "M dark", "L dark"], label="Settings"),
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gr.components.Checkbox(label="Crop"),
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gr.components.Checkbox(label="Remove Background"),
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gr.components.Textbox(
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label="Resize (WxH)", placeholder="Example: 100x100"),
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gr.components.Slider(
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minimum=0, maximum=200, label="Padding"),
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gr.components.Textbox(
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label="Background", placeholder="Color name or hex code")
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],
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outputs=[
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gr.components.Image(type="pil"),
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gr.components.HTML(label="Applied Settings")
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],
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examples=example_images,
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title="IMAGER ___ Image Processor",
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description="Upload an image and select processing options or choose a standard setting. Supports crop, autoremove background, resize, add padding, and set the background color.",)
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if __name__ == "__main__":
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interface.launch()
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image_processor.py
ADDED
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1 |
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import argparse
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2 |
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import os
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3 |
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import shutil
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from rembg import remove
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from PIL import Image
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import io
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7 |
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8 |
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9 |
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def add_background(image, background, default_color="#FFFFFF"):
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"""
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11 |
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Adds a background to an image, with a fallback to a default color if the specified background is not available.
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Args:
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13 |
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- image (PIL.Image.Image): Image with a transparent background.
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14 |
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- background (str or PIL.Image.Image): Background color (as a hex code) or a PIL Image to be used as background.
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15 |
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- default_color (str): Fallback color if the specified background is not valid. Defaults to white.
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16 |
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Returns:
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17 |
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- PIL.Image.Image: The image with the new background.
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18 |
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"""
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19 |
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foreground = image.convert("RGBA")
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20 |
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21 |
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if isinstance(background, str) and (background.startswith("#") or background.isalpha()):
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# Background is a color
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23 |
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try:
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Image.new("RGBA", (1, 1), background) # Test if valid color
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background_layer = Image.new("RGBA", foreground.size, background)
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26 |
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except ValueError:
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27 |
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print(
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28 |
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f"Invalid color '{background}'. Using default color '{default_color}'.")
|
29 |
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background_layer = Image.new(
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30 |
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"RGBA", foreground.size, default_color)
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31 |
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elif isinstance(background, Image.Image):
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32 |
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# Background is an image
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33 |
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bg_img = background.convert("RGBA")
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34 |
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background_layer = bg_img.resize(foreground.size)
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35 |
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else:
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36 |
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# Fallback to default color
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37 |
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background_layer = Image.new("RGBA", foreground.size, default_color)
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38 |
+
|
39 |
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final_img = Image.alpha_composite(
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40 |
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background_layer, foreground).convert("RGB")
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41 |
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42 |
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return final_img
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43 |
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44 |
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45 |
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def autocrop_image(image):
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46 |
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"""
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47 |
+
Autocrops an image, focusing on the non-transparent pixels.
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48 |
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Args:
|
49 |
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- image (PIL.Image.Image): Image to be autocropped.
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50 |
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Returns:
|
51 |
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- PIL.Image.Image: The autocropped image.
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52 |
+
"""
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53 |
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bbox = image.getbbox()
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54 |
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if bbox:
|
55 |
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return image.crop(bbox)
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56 |
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return image
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57 |
+
|
58 |
+
|
59 |
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def remove_bg_func(image):
|
60 |
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"""
|
61 |
+
Removes the background from an image using the rembg library.
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62 |
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Args:
|
63 |
+
- image (PIL.Image.Image): Image object from which to remove the background.
|
64 |
+
Returns:
|
65 |
+
- PIL.Image.Image: New image object with the background removed.
|
66 |
+
"""
|
67 |
+
# Convert the PIL Image to bytes
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68 |
+
img_byte_arr = io.BytesIO()
|
69 |
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image.save(img_byte_arr, format='PNG')
|
70 |
+
img_byte_arr = img_byte_arr.getvalue()
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71 |
+
|
72 |
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# Use rembg to remove the background
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73 |
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result_bytes = remove(img_byte_arr)
|
74 |
+
|
75 |
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# Convert the result bytes back to a PIL Image
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76 |
+
result_image = Image.open(io.BytesIO(result_bytes))
|
77 |
+
|
78 |
+
return result_image
|
79 |
+
|
80 |
+
|
81 |
+
def process_image(image_data, crop=False, remove_bg=False, resize=None, padding=0, background=None):
|
82 |
+
"""
|
83 |
+
Processes a single image based on the provided options.
|
84 |
+
Args:
|
85 |
+
- image_data (PIL.Image.Image): The input image.
|
86 |
+
- crop (bool): Whether to autocrop the image.
|
87 |
+
- remove_bg (bool): Whether to remove the background of the image.
|
88 |
+
- resize (tuple): Optional dimensions (width, height) to resize the image.
|
89 |
+
- padding (int): Number of padding pixels to add around the image.
|
90 |
+
- background (str): Optional background color (hex code or name) or path to an image file to set as the background.
|
91 |
+
Returns:
|
92 |
+
- PIL.Image.Image: The processed image.
|
93 |
+
"""
|
94 |
+
# Assume image_data is a PIL.Image.Image object
|
95 |
+
|
96 |
+
if remove_bg:
|
97 |
+
# Assuming remove_bg function returns a PIL image
|
98 |
+
image_data = remove_bg_func(image_data)
|
99 |
+
|
100 |
+
if crop:
|
101 |
+
# Assuming autocrop_image function modifies the image in place or returns a new PIL image
|
102 |
+
image_data = autocrop_image(image_data)
|
103 |
+
|
104 |
+
if resize:
|
105 |
+
# Assuming resize_and_pad_image function modifies the image in place or returns a new PIL image
|
106 |
+
image_data = resize_and_pad_image(image_data, resize, padding)
|
107 |
+
|
108 |
+
if background:
|
109 |
+
# Assuming add_background function modifies the image in place or returns a new PIL image
|
110 |
+
image_data = add_background(image_data, background)
|
111 |
+
|
112 |
+
return image_data
|
113 |
+
|
114 |
+
|
115 |
+
def resize_and_pad_image(image, dimensions, padding=0):
|
116 |
+
"""
|
117 |
+
Resizes an image to fit the specified dimensions and adds padding.
|
118 |
+
Args:
|
119 |
+
- image (PIL.Image.Image): Image object to be resized and padded.
|
120 |
+
- dimensions (tuple): Target dimensions (width, height).
|
121 |
+
- padding (int): Padding to add around the resized image.
|
122 |
+
Returns:
|
123 |
+
- PIL.Image.Image: Resized and padded image object.
|
124 |
+
"""
|
125 |
+
target_width, target_height = dimensions
|
126 |
+
content_width, content_height = target_width - \
|
127 |
+
2*padding, target_height - 2*padding
|
128 |
+
|
129 |
+
# Determine new size, preserving aspect ratio
|
130 |
+
img_ratio = image.width / image.height
|
131 |
+
target_ratio = content_width / content_height
|
132 |
+
|
133 |
+
if target_ratio > img_ratio:
|
134 |
+
new_height = content_height
|
135 |
+
new_width = int(new_height * img_ratio)
|
136 |
+
else:
|
137 |
+
new_width = content_width
|
138 |
+
new_height = int(new_width / img_ratio)
|
139 |
+
|
140 |
+
# Resize the image
|
141 |
+
resized_img = image.resize(
|
142 |
+
(new_width, new_height), Image.Resampling.LANCZOS)
|
143 |
+
|
144 |
+
# Create a new image with the target dimensions and a transparent background
|
145 |
+
new_img = Image.new(
|
146 |
+
"RGBA", (target_width, target_height), (255, 255, 255, 0))
|
147 |
+
|
148 |
+
# Calculate the position to paste the resized image to center it
|
149 |
+
paste_position = ((target_width - new_width) // 2,
|
150 |
+
(target_height - new_height) // 2)
|
151 |
+
|
152 |
+
# Paste the resized image onto the new image, centered
|
153 |
+
new_img.paste(resized_img, paste_position,
|
154 |
+
resized_img if resized_img.mode == 'RGBA' else None)
|
155 |
+
|
156 |
+
return new_img
|
157 |
+
|
158 |
+
|
159 |
+
def generate_output_filename(input_path, remove_bg=False, crop=False, resize=None, background=None):
|
160 |
+
"""
|
161 |
+
Generates an output filename based on the input path and processing options applied.
|
162 |
+
Appends specific suffixes based on the operations: '_b' for background removal, '_c' for crop,
|
163 |
+
and '_bg' if a background is added. It ensures the file extension is '.png'.
|
164 |
+
Args:
|
165 |
+
- input_path (str): Path to the input image.
|
166 |
+
- remove_bg (bool): Indicates if background removal was applied.
|
167 |
+
- crop (bool): Indicates if autocrop was applied.
|
168 |
+
- resize (tuple): Optional dimensions (width, height) for resizing the image.
|
169 |
+
- background (str): Indicates if a background was added (None if not used).
|
170 |
+
Returns:
|
171 |
+
- (str): Modified filename with appropriate suffix and '.png' extension.
|
172 |
+
"""
|
173 |
+
base, _ = os.path.splitext(os.path.basename(input_path))
|
174 |
+
suffix = ""
|
175 |
+
|
176 |
+
if remove_bg:
|
177 |
+
suffix += "_b"
|
178 |
+
if crop:
|
179 |
+
suffix += "_c"
|
180 |
+
if resize:
|
181 |
+
width, height = resize
|
182 |
+
suffix += f"_{width}x{height}"
|
183 |
+
if background:
|
184 |
+
suffix += "_bg" # Append "_bg" if the background option was used
|
185 |
+
|
186 |
+
# Ensure the file saves as PNG, accommodating for transparency or added backgrounds
|
187 |
+
return f"{base}{suffix}.png"
|
188 |
+
|
189 |
+
|
190 |
+
# The main and process_images functions remain the same, but ensure to update them to handle the new PNG output correctly.
|
191 |
+
|
192 |
+
# Update the process_images and main functions to include the new autocrop functionality
|
193 |
+
# Ensure to pass the crop argument to process_image and adjust the output filename generation accordingly
|
194 |
+
|
195 |
+
|
196 |
+
def process_images2(input_dir="./input", output_dir="./output", crop=False, remove_bg=False, resize=None, padding=0, background=None):
|
197 |
+
"""
|
198 |
+
Processes images in the specified directory based on the provided options.
|
199 |
+
Args:
|
200 |
+
- input_dir (str): Directory containing the images to be processed.
|
201 |
+
- output_dir (str): Directory where processed images will be saved.
|
202 |
+
- crop (bool): Whether to crop the images.
|
203 |
+
- remove_bg (bool): Whether to remove the background of the images.
|
204 |
+
- resize (tuple): Optional dimensions (width, height) to resize the image.
|
205 |
+
- padding (int): Number of padding pixels to add around the image.
|
206 |
+
- background (str): Optional background color (hex code or name) or path to an image file to set as the background.
|
207 |
+
"""
|
208 |
+
processed_input_dir = os.path.join(input_dir, "processed")
|
209 |
+
os.makedirs(processed_input_dir, exist_ok=True)
|
210 |
+
os.makedirs(output_dir, exist_ok=True)
|
211 |
+
|
212 |
+
inputs = [os.path.join(input_dir, f) for f in os.listdir(
|
213 |
+
input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
214 |
+
|
215 |
+
# if images are not in the input directory, print a message and return
|
216 |
+
if not inputs:
|
217 |
+
print("No images found in the input directory.")
|
218 |
+
return
|
219 |
+
|
220 |
+
for i, input_path in enumerate(inputs, start=1):
|
221 |
+
filename = os.path.basename(input_path)
|
222 |
+
output_filename = generate_output_filename(
|
223 |
+
input_path, remove_bg=remove_bg, crop=crop, resize=resize, background=background)
|
224 |
+
output_path = os.path.join(output_dir, output_filename)
|
225 |
+
print(f"Processing image {i}/{len(inputs)}...{filename}")
|
226 |
+
|
227 |
+
# Update the call to process_image with all parameters including background
|
228 |
+
process_image(input_path, output_path, crop=crop, remove_bg=remove_bg,
|
229 |
+
resize=resize, padding=padding, background=background)
|
230 |
+
|
231 |
+
shutil.move(input_path, os.path.join(processed_input_dir, filename))
|
232 |
+
|
233 |
+
print("All images have been processed.")
|
234 |
+
|
235 |
+
|
236 |
+
def process_images(input_dir="./input", output_dir="./output", crop=False, remove_bg=False, resize=None, padding=0, background=None):
|
237 |
+
"""
|
238 |
+
Processes images in the specified directory based on the provided options.
|
239 |
+
"""
|
240 |
+
processed_input_dir = os.path.join(input_dir, "processed")
|
241 |
+
os.makedirs(processed_input_dir, exist_ok=True)
|
242 |
+
os.makedirs(output_dir, exist_ok=True)
|
243 |
+
|
244 |
+
inputs = [os.path.join(input_dir, f) for f in os.listdir(
|
245 |
+
input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
246 |
+
|
247 |
+
if not inputs:
|
248 |
+
print("No images found in the input directory.")
|
249 |
+
return
|
250 |
+
|
251 |
+
for i, input_path in enumerate(inputs, start=1):
|
252 |
+
try:
|
253 |
+
with Image.open(input_path) as img:
|
254 |
+
# Define filename here, before it's used
|
255 |
+
filename = os.path.basename(input_path)
|
256 |
+
|
257 |
+
# Process the image
|
258 |
+
processed_img = process_image(
|
259 |
+
img, crop=crop, remove_bg=remove_bg, resize=resize, padding=padding, background=background)
|
260 |
+
|
261 |
+
# Generate output filename based on processing parameters
|
262 |
+
output_filename = generate_output_filename(
|
263 |
+
filename, remove_bg=remove_bg, crop=crop, resize=resize, background=background)
|
264 |
+
output_path = os.path.join(output_dir, output_filename)
|
265 |
+
|
266 |
+
# Save the processed image to the output directory
|
267 |
+
processed_img.save(output_path)
|
268 |
+
|
269 |
+
print(
|
270 |
+
f"Processed image {i}/{len(inputs)}: {filename} -> {output_filename}")
|
271 |
+
|
272 |
+
# Optionally move the processed input image to a "processed" subdirectory
|
273 |
+
shutil.move(input_path, os.path.join(
|
274 |
+
processed_input_dir, filename))
|
275 |
+
except Exception as e:
|
276 |
+
print(f"Error processing image {input_path}: {e}")
|
277 |
+
|
278 |
+
print("All images have been processed.")
|
imager.py
ADDED
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
# Ensure this is the function intended for directory processing
|
3 |
+
from image_processor import process_images
|
4 |
+
|
5 |
+
|
6 |
+
def main():
|
7 |
+
parser = argparse.ArgumentParser(
|
8 |
+
description="Process multiple images from a directory with options for cropping, background removal, resizing, and adding a background.")
|
9 |
+
parser.add_argument("-i", "--input_dir", type=str, required=True,
|
10 |
+
help="Directory containing the images to be processed.")
|
11 |
+
parser.add_argument("-o", "--output_dir", type=str, required=True,
|
12 |
+
help="Directory where processed images will be saved.")
|
13 |
+
parser.add_argument("-c", "--crop", action="store_true",
|
14 |
+
help="Crop the images.")
|
15 |
+
parser.add_argument("-b", "--background_removal", action="store_true",
|
16 |
+
help="Remove the background from the images.")
|
17 |
+
parser.add_argument("-r", "--resize", type=str,
|
18 |
+
help="Resize the image to fit within AxB pixels while maintaining aspect ratio. Format: 'AxB'")
|
19 |
+
parser.add_argument("-p", "--padding", type=int, default=0,
|
20 |
+
help="Number of padding pixels to add around the image.")
|
21 |
+
parser.add_argument("-bg", "--background", type=str, default=None,
|
22 |
+
help="Add a background to the image. Accepts color names, hex codes, or paths to image files.")
|
23 |
+
|
24 |
+
args = parser.parse_args()
|
25 |
+
|
26 |
+
resize_dimensions = None
|
27 |
+
if args.resize:
|
28 |
+
try:
|
29 |
+
width, height = map(int, args.resize.split('x'))
|
30 |
+
resize_dimensions = (width, height)
|
31 |
+
except ValueError:
|
32 |
+
print("Invalid format for resize dimensions. Please use 'AxB'.")
|
33 |
+
return
|
34 |
+
|
35 |
+
process_images(
|
36 |
+
input_dir=args.input_dir,
|
37 |
+
output_dir=args.output_dir,
|
38 |
+
crop=args.crop,
|
39 |
+
remove_bg=args.background_removal,
|
40 |
+
resize=resize_dimensions,
|
41 |
+
padding=args.padding,
|
42 |
+
background=args.background
|
43 |
+
)
|
44 |
+
|
45 |
+
|
46 |
+
if __name__ == "__main__":
|
47 |
+
main()
|
requirements.txt
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
rembg
|
2 |
+
pillow
|