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# utils/image_utils.py | |
import os | |
from io import BytesIO | |
import cairosvg | |
import base64 | |
import numpy as np | |
#from decimal import ROUND_CEILING | |
from PIL import Image, ImageChops, ImageDraw, ImageEnhance, ImageFilter, ImageDraw, ImageOps, ImageMath | |
from typing import List, Union, is_typeddict | |
#import numpy as np | |
#import math | |
from pathlib import Path | |
from utils.constants import default_lut_example_img, PRE_RENDERED_MAPS_JSON_LEVELS | |
from utils.color_utils import ( | |
detect_color_format, | |
update_color_opacity | |
) | |
from utils.file_utils import rename_file_to_lowercase_extension | |
def save_image_to_temp_png(image_source, user_dir: str = None, file_name: str = None): | |
""" | |
Opens an image from a file path, URL, or DataURL and saves it as a PNG in the user's temporary directory. | |
Parameters: | |
image_source (str, dict or PIL.Image.Image): The source of the image to open. | |
Returns: | |
str: The file path of the saved PNG image in the temporary directory. | |
""" | |
import tempfile | |
import uuid | |
# Open the image using the existing utility function | |
img = open_image(image_source) | |
# Ensure the image is in a format that supports PNG (convert if necessary) | |
if img.mode not in ("RGB", "RGBA"): | |
img = img.convert("RGBA") | |
# Generate a unique filename in the system temporary directory | |
if user_dir is None: | |
user_dir = tempfile.gettempdir() | |
if file_name is None: | |
file_name = f"{uuid.uuid4()}.png" | |
temp_filepath = os.path.join(user_dir, file_name.lower()) | |
os.makedirs(user_dir, exist_ok=True) | |
# Save the image as PNG | |
img.save(temp_filepath, format="PNG") | |
return temp_filepath | |
def get_image_from_dict(image_path): | |
if isinstance(image_path, dict) : | |
if 'composite' in image_path: | |
image_path = image_path.get('composite') | |
elif 'image' in image_path: | |
image_path = image_path.get('image') | |
else: | |
print("\n Unknown image dictionary.\n") | |
raise UserWarning("Unknown image dictionary.") | |
return image_path, True | |
else: | |
return image_path, False | |
def open_image(image_path): | |
""" | |
Opens an image from a file path or URL, or decodes a DataURL string into an image. | |
Supports SVG and ICO by converting them to PNG. | |
Parameters: | |
image_path (str): The file path, URL, or DataURL string of the image to open. | |
Returns: | |
Image: A PIL Image object of the opened image. | |
Raises: | |
Exception: If there is an error opening the image. | |
""" | |
if isinstance(image_path, Image.Image): | |
return image_path | |
elif isinstance(image_path, dict): | |
image_path, is_dict = get_image_from_dict(image_path) | |
image_path = rename_file_to_lowercase_extension(image_path) | |
import requests | |
try: | |
# Strip leading and trailing double quotation marks, if present | |
image_path = image_path.strip('"') | |
if image_path.startswith('http'): | |
response = requests.get(image_path) | |
if image_path.lower().endswith('.svg'): | |
png_data = cairosvg.svg2png(bytestring=response.content) | |
img = Image.open(BytesIO(png_data)) | |
elif image_path.lower().endswith('.ico'): | |
img = Image.open(BytesIO(response.content)).convert('RGBA') | |
else: | |
img = Image.open(BytesIO(response.content)) | |
elif image_path.startswith('data'): | |
encoded_data = image_path.split(',')[1] | |
decoded_data = base64.b64decode(encoded_data) | |
if image_path.lower().endswith('.svg'): | |
png_data = cairosvg.svg2png(bytestring=decoded_data) | |
img = Image.open(BytesIO(png_data)) | |
elif image_path.lower().endswith('.ico'): | |
img = Image.open(BytesIO(decoded_data)).convert('RGBA') | |
else: | |
img = Image.open(BytesIO(decoded_data)) | |
else: | |
if image_path.lower().endswith('.svg'): | |
png_data = cairosvg.svg2png(url=image_path) | |
img = Image.open(BytesIO(png_data)) | |
elif image_path.lower().endswith('.ico'): | |
img = Image.open(image_path).convert('RGBA') | |
else: | |
img = Image.open(image_path) | |
except Exception as e: | |
raise Exception(f'Error opening image: {e}') | |
return img | |
def build_prerendered_images(images_list): | |
""" | |
Opens a list of images from file paths, URLs, or DataURL strings. | |
Parameters: | |
images_list (list): A list of file paths, URLs, or DataURL strings of the images to open. | |
Returns: | |
list: A list of PIL Image objects of the opened images. | |
""" | |
return [open_image(image) for image in images_list] | |
# Example usage | |
# filtered_maps = get_maps_with_quality_less_than(3) | |
# print(filtered_maps) | |
def build_prerendered_images_by_quality(quality_limit, key='file'): | |
""" | |
Retrieve and sort file paths from PRE_RENDERED_MAPS_JSON_LEVELS where quality is <= quality_limit. | |
Sorts by quality and case-insensitive alphanumeric key. | |
Args: | |
quality_limit (int): Maximum quality threshold | |
key (str): Key to extract file path from map info (default: 'file') | |
Returns: | |
tuple: (sorted file paths list, list of corresponding map names) | |
""" | |
# Pre-compute lowercase alphanumeric key once per item | |
def get_sort_key(item): | |
name, info = item | |
return (info['quality'], ''.join(c for c in name.lower() if c.isalnum())) | |
# Single pass: sort and filter | |
filtered_maps = [ | |
(info[key].replace("\\", "/"), name) | |
for name, info in sorted(PRE_RENDERED_MAPS_JSON_LEVELS.items(), key=get_sort_key) | |
if info['quality'] <= quality_limit | |
] | |
# Split into separate lists efficiently | |
if filtered_maps: | |
#file_paths, map_names = zip(*filtered_maps) | |
#return (build_prerendered_images(file_paths), list(map_names)) | |
return [(open_image(file_path), map_name) for file_path, map_name in filtered_maps] | |
return (None,"") | |
def build_encoded_images(images_list): | |
""" | |
Encodes a list of images to base64 strings. | |
Parameters: | |
images_list (list): A list of file paths, URLs, DataURL strings, or PIL Image objects of the images to encode. | |
Returns: | |
list: A list of base64-encoded strings of the images. | |
""" | |
return [image_to_base64(image) for image in images_list] | |
def image_to_base64(image): | |
""" | |
Encodes an image to a base64 string. | |
Supports ICO files by converting them to PNG with RGBA channels. | |
Parameters: | |
image (str or PIL.Image.Image): The file path, URL, DataURL string, or PIL Image object of the image to encode. | |
Returns: | |
str: A base64-encoded string of the image. | |
""" | |
buffered = BytesIO() | |
if isinstance(image, str): | |
image = open_image(image) | |
image.save(buffered, format="PNG") | |
return "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode() | |
def change_color(image, color, opacity=0.75): | |
""" | |
Changes the color of an image by overlaying it with a specified color and opacity. | |
Parameters: | |
image (str or PIL.Image.Image): The file path, URL, DataURL string, or PIL Image object of the image to change. | |
color (str or tuple): The color to overlay on the image. | |
opacity (float): The opacity of the overlay color (0.0 to 1.0). | |
Returns: | |
PIL.Image.Image: The image with the color changed. | |
""" | |
if type(image) is str: | |
image = open_image(image) | |
try: | |
# Convert the color to RGBA format | |
rgba_color = detect_color_format(color) | |
rgba_color = update_color_opacity(rgba_color, opacity) | |
# Convert the image to RGBA mode | |
image = image.convert("RGBA") | |
# Create a new image with the same size and mode | |
new_image = Image.new("RGBA", image.size, rgba_color) | |
# Composite the new image with the original image | |
result = Image.alpha_composite(image, new_image) | |
except Exception as e: | |
print(f"Error changing color: {e}") | |
return image | |
return result | |
def convert_str_to_int_or_zero(value): | |
""" | |
Converts a string to an integer, or returns zero if the conversion fails. | |
Parameters: | |
value (str): The string to convert. | |
Returns: | |
int: The converted integer, or zero if the conversion fails. | |
""" | |
try: | |
return int(value) | |
except ValueError: | |
return 0 | |
def upscale_image(image, scale_factor): | |
""" | |
Upscales an image by a given scale factor using the LANCZOS filter. | |
Parameters: | |
image (PIL.Image.Image): The input image to be upscaled. | |
scale_factor (float): The factor by which to upscale the image. | |
Returns: | |
PIL.Image.Image: The upscaled image. | |
""" | |
# Calculate the new size | |
new_width = int(image.width * scale_factor) | |
new_height = int(image.height * scale_factor) | |
# Upscale the image using the LANCZOS filter | |
upscaled_image = image.resize((new_width, new_height), Image.LANCZOS) | |
return upscaled_image | |
def crop_and_resize_image(image, width, height): | |
""" | |
Crops the image to a centered square and resizes it to the specified width and height. | |
Parameters: | |
image (PIL.Image.Image): The input image to be cropped and resized. | |
width (int): The desired width of the output image. | |
height (int): The desired height of the output image. | |
Returns: | |
PIL.Image.Image: The cropped and resized image. | |
""" | |
# Get original dimensions | |
original_width, original_height = image.size | |
# Determine the smaller dimension to make a square crop | |
min_dim = min(original_width, original_height) | |
# Calculate coordinates for cropping to a centered square | |
left = (original_width - min_dim) // 2 | |
top = (original_height - min_dim) // 2 | |
right = left + min_dim | |
bottom = top + min_dim | |
# Crop the image | |
cropped_image = image.crop((left, top, right, bottom)) | |
# Resize the image to the desired dimensions | |
resized_image = cropped_image.resize((width, height), Image.LANCZOS) | |
return resized_image | |
def resize_image_with_aspect_ratio(image, target_width, target_height): | |
""" | |
Resizes the image to fit within the target dimensions while maintaining aspect ratio. | |
If the aspect ratio does not match, the image will be padded with black pixels. | |
Parameters: | |
image (PIL.Image.Image): The input image to be resized. | |
target_width (int): The target width. | |
target_height (int): The target height. | |
Returns: | |
PIL.Image.Image: The resized image. | |
""" | |
# Calculate aspect ratios | |
original_width, original_height = image.size | |
target_aspect = target_width / target_height | |
original_aspect = original_width / original_height | |
#print(f"Original size: {image.size}\ntarget_aspect: {target_aspect}\noriginal_aspect: {original_aspect}\n") | |
# Decide whether to fit width or height | |
if original_aspect > target_aspect: | |
# Image is wider than target aspect ratio | |
new_width = target_width | |
new_height = int(target_width / original_aspect) | |
else: | |
# Image is taller than target aspect ratio | |
new_height = target_height | |
new_width = int(target_height * original_aspect) | |
# Resize the image | |
resized_image = image.resize((new_width, new_height), Image.LANCZOS) | |
#print(f"Resized size: {resized_image.size}\n") | |
# Create a new image with target dimensions and black background | |
new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0)) | |
# Paste the resized image onto the center of the new image | |
paste_x = (target_width - new_width) // 2 | |
paste_y = (target_height - new_height) // 2 | |
new_image.paste(resized_image, (paste_x, paste_y)) | |
return new_image | |
def lerp_imagemath(img1, img2, alpha_percent: int = 50): | |
""" | |
Performs linear interpolation (LERP) between two images based on the given alpha value. | |
Parameters: | |
img1 (str or PIL.Image.Image): The first image or its file path. | |
img2 (str or PIL.Image.Image): The second image or its file path. | |
alpha (int): The interpolation factor (0 to 100). | |
Returns: | |
PIL.Image.Image: The interpolated image. | |
""" | |
if isinstance(img1, str): | |
img1 = open_image(img1) | |
if isinstance(img2, str): | |
img2 = open_image(img2) | |
# Ensure both images are in the same mode (e.g., RGBA) | |
img1 = img1.convert('RGBA') | |
img2 = img2.convert('RGBA') | |
# Convert images to NumPy arrays | |
arr1 = np.array(img1, dtype=np.float32) | |
arr2 = np.array(img2, dtype=np.float32) | |
# Perform linear interpolation | |
alpha = alpha_percent / 100.0 | |
result_arr = (arr1 * (1 - alpha)) + (arr2 * alpha) | |
# Convert the result back to a PIL image | |
result_img = Image.fromarray(np.uint8(result_arr)) | |
#result_img.show() | |
return result_img | |
def shrink_and_paste_on_blank(current_image, mask_width, mask_height, blank_color:tuple[int, int, int, int] = (0,0,0,0)): | |
""" | |
Decreases size of current_image by mask_width pixels from each side, | |
then adds a mask_width width transparent frame, | |
so that the image the function returns is the same size as the input. | |
Parameters: | |
current_image (PIL.Image.Image): The input image to transform. | |
mask_width (int): Width in pixels to shrink from each side. | |
mask_height (int): Height in pixels to shrink from each side. | |
blank_color (tuple): The color of the blank frame (default is transparent). | |
Returns: | |
PIL.Image.Image: The transformed image. | |
""" | |
# calculate new dimensions | |
width, height = current_image.size | |
new_width = width - (2 * mask_width) | |
new_height = height - (2 * mask_height) | |
# resize and paste onto blank image | |
prev_image = current_image.resize((new_width, new_height)) | |
blank_image = Image.new("RGBA", (width, height), blank_color) | |
blank_image.paste(prev_image, (mask_width, mask_height)) | |
return blank_image | |
def multiply_and_blend_images(base_image, image2, alpha_percent=50): | |
""" | |
Multiplies two images and blends the result with the original image. | |
Parameters: | |
image1 (PIL.Image.Image): The first input image. | |
image2 (PIL.Image.Image): The second input image. | |
alpha (float): The blend factor (0.0 to 100.0) for blending the multiplied result with the original image. | |
Returns: | |
PIL.Image.Image: The blended image. | |
""" | |
alpha = alpha_percent / 100.0 | |
if isinstance(base_image, str): | |
base_image = open_image(base_image) | |
if isinstance(image2, str): | |
image2 = open_image(image2) | |
# Ensure both images are in the same mode and size | |
base_image = base_image.convert('RGBA') | |
image2 = image2.convert('RGBA') | |
image2 = image2.resize(base_image.size) | |
# Multiply the images | |
multiplied_image = ImageChops.multiply(base_image, image2) | |
# Blend the multiplied result with the original | |
blended_image = Image.blend(base_image, multiplied_image, alpha) | |
return blended_image | |
def alpha_composite_with_control(base_image, image_with_alpha, alpha_percent=100): | |
""" | |
Overlays image_with_alpha onto base_image with controlled alpha transparency. | |
Parameters: | |
base_image (PIL.Image.Image): The base image. | |
image_with_alpha (PIL.Image.Image): The image to overlay with an alpha channel. | |
alpha_percent (float): The multiplier for the alpha channel (0.0 to 100.0). | |
Returns: | |
PIL.Image.Image: The resulting image after alpha compositing. | |
""" | |
image_with_alpha, isdict = get_image_from_dict(image_with_alpha) | |
alpha_multiplier = alpha_percent / 100.0 | |
if isinstance(base_image, str): | |
base_image = open_image(base_image) | |
if isinstance(image_with_alpha, str): | |
image_with_alpha = open_image(image_with_alpha) | |
# Ensure both images are in RGBA mode | |
base_image = base_image.convert('RGBA') | |
image_with_alpha = image_with_alpha.convert('RGBA') | |
# Extract the alpha channel and multiply by alpha_multiplier | |
alpha_channel = image_with_alpha.split()[3] | |
alpha_channel = alpha_channel.point(lambda p: p * alpha_multiplier) | |
# Apply the modified alpha channel back to the image | |
image_with_alpha.putalpha(alpha_channel) | |
# Composite the images | |
result = Image.alpha_composite(base_image, image_with_alpha) | |
return result | |
def apply_alpha_mask(image, mask_image, invert = False): | |
""" | |
Applies a mask image as the alpha channel of the input image. | |
Parameters: | |
image (PIL.Image.Image): The image to apply the mask to. | |
mask_image (PIL.Image.Image): The alpha mask to apply. | |
invert (bool): Whether to invert the mask (default is False). | |
Returns: | |
PIL.Image.Image: The image with the applied alpha mask. | |
""" | |
# Resize the mask to match the current image size | |
mask_image = resize_and_crop_image(mask_image, image.width, image.height).convert('L') # convert to grayscale | |
if invert: | |
mask_image = ImageOps.invert(mask_image) | |
# Apply the mask as the alpha layer of the current image | |
result_image = image.copy() | |
result_image.putalpha(mask_image) | |
return result_image | |
def resize_and_crop_image(image: Image, new_width: int = 512, new_height: int = 512) -> Image: | |
""" | |
Resizes and crops an image to a specified width and height. This ensures that the entire new_width and new_height | |
dimensions are filled by the image, and the aspect ratio is maintained. | |
Parameters: | |
image (PIL.Image.Image): The image to be resized and cropped. | |
new_width (int): The desired width of the new image (default is 512). | |
new_height (int): The desired height of the new image (default is 512). | |
Returns: | |
PIL.Image.Image: The resized and cropped image. | |
""" | |
# Get the dimensions of the original image | |
orig_width, orig_height = image.size | |
# Calculate the aspect ratios of the original and new images | |
orig_aspect_ratio = orig_width / float(orig_height) | |
new_aspect_ratio = new_width / float(new_height) | |
# Calculate the new size of the image while maintaining aspect ratio | |
if orig_aspect_ratio > new_aspect_ratio: | |
# The original image is wider than the new image, so we need to crop the sides | |
resized_width = int(new_height * orig_aspect_ratio) | |
resized_height = new_height | |
left_offset = (resized_width - new_width) // 2 | |
top_offset = 0 | |
else: | |
# The original image is taller than the new image, so we need to crop the top and bottom | |
resized_width = new_width | |
resized_height = int(new_width / orig_aspect_ratio) | |
left_offset = 0 | |
top_offset = (resized_height - new_height) // 2 | |
# Resize the image with Lanczos resampling filter | |
resized_image = image.resize((resized_width, resized_height), resample=Image.Resampling.LANCZOS) | |
# Crop the image to fill the entire height and width of the new image | |
cropped_image = resized_image.crop((left_offset, top_offset, left_offset + new_width, top_offset + new_height)) | |
return cropped_image | |
##################################################### LUTs ############################################################ | |
def is_3dlut_row(row: List[str]) -> bool: | |
""" | |
Check if one line in the file has exactly 3 numeric values. | |
Parameters: | |
row (list): A list of strings representing the values in a row. | |
Returns: | |
bool: True if the row has exactly 3 numeric values, False otherwise. | |
""" | |
try: | |
row_values = [float(val) for val in row] | |
return len(row_values) == 3 | |
except ValueError: | |
return False | |
def get_lut_type(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> str: | |
with open(path_lut) as f: | |
lines = f.read().splitlines() | |
lut_type = "3D" # Initially assume 3D LUT | |
size = None | |
table = [] | |
# Parse the file | |
for line in lines: | |
line = line.strip() | |
if line.startswith("#") or not line: | |
continue # Skip comments and empty lines | |
parts = line.split() | |
if parts[0] == "LUT_3D_SIZE": | |
size = int(parts[1]) | |
lut_type = "3D" | |
elif parts[0] == "LUT_1D_SIZE": | |
size = int(parts[1]) | |
lut_type = "1D" | |
elif is_3dlut_row(parts): | |
table.append(tuple(float(val) for val in parts)) | |
return lut_type | |
def read_3d_lut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> ImageFilter.Color3DLUT: | |
""" | |
Read LUT from a raw file. | |
Each line in the file is considered part of the LUT table. The function | |
reads the file, parses the rows, and constructs a Color3DLUT object. | |
Args: | |
path_lut: A string or os.PathLike object representing the path to the LUT file. | |
num_channels: An integer specifying the number of color channels in the LUT (default is 3). | |
Returns: | |
An instance of ImageFilter.Color3DLUT representing the LUT. | |
Raises: | |
FileNotFoundError: If the LUT file specified by path_lut does not exist. | |
""" | |
with open(path_lut) as f: | |
lut_raw = f.read().splitlines() | |
size = round(len(lut_raw) ** (1 / 3)) | |
row2val = lambda row: tuple([float(val) for val in row.split(" ")]) | |
lut_table = [row2val(row) for row in lut_raw if is_3dlut_row(row.split(" "))] | |
return ImageFilter.Color3DLUT(size, lut_table, num_channels) | |
def apply_1d_lut(image, lut_file): | |
# Read the 1D LUT | |
with open(lut_file) as f: | |
lines = f.read().splitlines() | |
table = [] | |
for line in lines: | |
if not line.startswith(("#", "LUT", "TITLE", "DOMAIN")) and line.strip(): | |
values = [float(v) for v in line.split()] | |
table.append(tuple(values)) | |
# Convert image to grayscale | |
if image.mode != 'L': | |
image = image.convert('L') | |
img_array = np.array(image) / 255.0 # Normalize to [0, 1] | |
# Map grayscale values to colors | |
lut_size = len(table) | |
indices = (img_array * (lut_size - 1)).astype(int) | |
colors = np.array(table)[indices] | |
# Create RGB image | |
rgb_image = Image.fromarray((colors * 255).astype(np.uint8), mode='RGB') | |
return rgb_image | |
def apply_3d_lut(img: Image, lut_path: str = "", lut: ImageFilter.Color3DLUT = None) -> Image: | |
""" | |
Apply a LUT to an image and return a PIL Image with the LUT applied. | |
The function applies the LUT to the input image using the filter() method of the PIL Image class. | |
Args: | |
img: A PIL Image object to which the LUT should be applied. | |
lut_path: A string representing the path to the LUT file (optional if lut argument is provided). | |
lut: An instance of ImageFilter.Color3DLUT representing the LUT (optional if lut_path is provided). | |
Returns: | |
A PIL Image object with the LUT applied. | |
Raises: | |
ValueError: If both lut_path and lut arguments are not provided. | |
""" | |
if lut is None: | |
if lut_path == "": | |
raise ValueError("Either lut_path or lut argument must be provided.") | |
lut = read_3d_lut(lut_path) | |
return img.filter(lut) | |
def apply_lut(image, lut_filename: str) -> Image: | |
""" | |
Apply a LUT to an image and return the result. | |
Args: | |
image (str or PIL.Image.Image): The image to apply the LUT to. | |
lut_filename (str): The path to the LUT file. | |
Returns: | |
PIL.Image.Image: The image with the LUT applied. | |
""" | |
if isinstance(image, str): | |
image = open_image(image) | |
if lut_filename is not None: | |
if (get_lut_type(lut_filename) == "3D"): | |
lut = read_3d_lut(lut_filename) | |
image = apply_3d_lut(image, lut=lut) | |
else: | |
image = apply_1d_lut(image, lut_filename) | |
return image | |
def show_lut(lut_filename: str, lut_example_image: Image = default_lut_example_img) -> Image: | |
if lut_filename is not None: | |
try: | |
lut_example_image = apply_lut(lut_example_image, lut_filename) | |
except Exception as e: | |
print(f"BAD LUT: Error applying LUT {str(e)}.") | |
else: | |
lut_example_image = open_image(default_lut_example_img) | |
return lut_example_image | |
def apply_1d_lut(image, lut_file): | |
# Read the 1D LUT | |
with open(lut_file) as f: | |
lines = f.read().splitlines() | |
table = [] | |
for line in lines: | |
if not line.startswith(("#", "LUT", "TITLE", "DOMAIN")) and line.strip(): | |
values = [float(v) for v in line.split()] | |
table.append(tuple(values)) | |
# Convert image to grayscale | |
if image.mode != 'L': | |
image = image.convert('L') | |
img_array = np.array(image) / 255.0 # Normalize to [0, 1] | |
# Map grayscale values to colors | |
lut_size = len(table) | |
indices = (img_array * (lut_size - 1)).astype(int) | |
colors = np.array(table)[indices] | |
# Create RGB image | |
rgb_image = Image.fromarray((colors * 255).astype(np.uint8), mode='RGB') | |
return rgb_image | |
def apply_lut_to_image_path(lut_filename: str, image_path: str) -> tuple[Image, str]: | |
""" | |
Apply a LUT to an image and return the result. | |
Supports ICO files by converting them to PNG with RGBA channels. | |
Args: | |
lut_filename: A string representing the path to the LUT file. | |
image_path: A string representing the path to the input image. | |
Returns: | |
tuple: A tuple containing the PIL Image object with the LUT applied and the new image path as a string. | |
""" | |
import gradio as gr | |
img_lut = None | |
if image_path is None: | |
raise UserWarning("No image provided.") | |
return None, None | |
# Split the path into directory and filename | |
directory, file_name = os.path.split(image_path) | |
lut_directory, lut_file_name = os.path.split(lut_filename) | |
# Split the filename into name and extension | |
name, ext = os.path.splitext(file_name) | |
lut_name, lut_ext = os.path.splitext(lut_file_name) | |
# Convert the extension to lowercase | |
new_ext = ext.lower() | |
path = Path(image_path) | |
img = open_image(image_path) | |
if not ((path.suffix.lower() == '.png' and img.mode == 'RGBA')): | |
if image_path.lower().endswith(('.jpg', '.jpeg')): | |
img, new_image_path = convert_jpg_to_rgba(path) | |
elif image_path.lower().endswith('.ico'): | |
img, new_image_path = convert_to_rgba_png(image_path) | |
elif image_path.lower().endswith(('.gif', '.webp')): | |
img, new_image_path = convert_to_rgba_png(image_path) | |
else: | |
img, new_image_path = convert_to_rgba_png(image_path) | |
if image_path != new_image_path: | |
delete_image(image_path) | |
else: | |
# ensure the file extension is lower_case, otherwise leave as is | |
new_filename = name + new_ext | |
new_image_path = os.path.join(directory, new_filename) | |
# Apply the LUT to the image | |
if (lut_filename is not None and img is not None): | |
try: | |
img_lut = apply_lut(img, lut_filename) | |
except Exception as e: | |
print(f"BAD LUT: Error applying LUT {str(e)}.") | |
if img_lut is not None: | |
new_filename = name + "_"+ lut_name + new_ext | |
new_image_path = os.path.join(directory, new_filename) | |
delete_image(image_path) | |
img = img_lut | |
img.save(new_image_path, format='PNG') | |
print(f"Image with LUT saved as {new_image_path}") | |
return img, gr.update(value=str(new_image_path)) | |
############################################# RGBA ########################################################### | |
def convert_rgb_to_rgba_safe(image: Image) -> Image: | |
""" | |
Converts an RGB image to RGBA by adding an alpha channel. | |
Ensures that the original image remains unaltered. | |
Parameters: | |
image (PIL.Image.Image): The RGB image to convert. | |
Returns: | |
PIL.Image.Image: The converted RGBA image. | |
""" | |
if image.mode != 'RGB': | |
if image.mode == 'RGBA': | |
return image | |
elif image.mode == 'P': | |
# Convert palette image to RGBA | |
image = image.convert('RGB') | |
else: | |
raise ValueError("Unsupported image mode for conversion to RGBA.") | |
# Create a copy of the image to avoid modifying the original | |
rgba_image = image.copy() | |
# Optionally, set a default alpha value (e.g., fully opaque) | |
alpha = Image.new('L', rgba_image.size, 255) # 255 for full opacity | |
rgba_image.putalpha(alpha) | |
return rgba_image | |
# Example usage | |
# convert_jpg_to_rgba('input.jpg', 'output.png') | |
def convert_jpg_to_rgba(input_path) -> tuple[Image, str]: | |
""" | |
Convert a JPG image to RGBA format and save it as a PNG. | |
Args: | |
input_path (str or Path): Path to the input JPG image file. | |
Raises: | |
FileNotFoundError: If the input file does not exist. | |
ValueError: If the input file is not a JPG. | |
OSError: If there's an error reading or writing the file. | |
Returns: | |
tuple: A tuple containing the RGBA image and the output path as a string. | |
""" | |
try: | |
# Convert input_path to Path object if it's a string | |
input_path = Path(input_path) | |
output_path = input_path.with_suffix('.png') | |
# Check if the input file exists | |
if not input_path.exists(): | |
#if file was renamed to lower case, update the input path | |
input_path = output_path | |
if not input_path.exists(): | |
raise FileNotFoundError(f"The file {input_path} does not exist.") | |
# Check file extension first to skip unnecessary processing | |
if input_path.suffix.lower() not in ('.jpg', '.jpeg'): | |
print(f"Skipping conversion: {input_path} is not a JPG or JPEG file.") | |
return None, None | |
print(f"Converting to PNG: {input_path} is a JPG or JPEG file.") | |
# Open the image file | |
with Image.open(input_path) as img: | |
# Convert the image to RGBA mode | |
rgba_img = img.convert('RGBA') | |
# Ensure the directory exists for the output file | |
output_path.parent.mkdir(parents=True, exist_ok=True) | |
# Save the image with RGBA mode as PNG | |
rgba_img.save(output_path) | |
except FileNotFoundError as e: | |
print(f"Error: {e}") | |
except ValueError as e: | |
print(f"Error: {e}") | |
except OSError as e: | |
print(f"Error: An OS error occurred while processing the image - {e}") | |
except Exception as e: | |
print(f"An unexpected error occurred: {e}") | |
return rgba_img, str(output_path) | |
def convert_to_rgba_png(file_path: str) -> tuple[Image, str]: | |
""" | |
Converts an image to RGBA PNG format and saves it with the same base name and a .png extension. | |
Supports ICO files. | |
Args: | |
file_path (str): The path to the input image file. | |
Returns: | |
tuple: A tuple containing the RGBA image and the new file path as a string. | |
""" | |
new_file_path = None | |
rgba_img = None | |
img = None | |
if file_path is None: | |
raise UserWarning("No image provided.") | |
return None, None | |
try: | |
file_path, is_dict = get_image_from_dict(file_path) | |
img = open_image(file_path) | |
print(f"Opened image: {file_path}\n") | |
# Handle ICO files | |
if file_path.lower().endswith(('.ico','.webp','.gif')): | |
rgba_img = img.convert('RGBA') | |
new_file_path = Path(file_path).with_suffix('.png') | |
rgba_img.save(new_file_path, format='PNG') | |
print(f"Converted ICO to PNG: {new_file_path}") | |
else: | |
rgba_img, new_file_path = convert_jpg_to_rgba(file_path) | |
if rgba_img is None: | |
rgba_img = convert_rgb_to_rgba_safe(img) | |
new_file_path = Path(file_path).with_suffix('.png') | |
rgba_img.save(new_file_path, format='PNG') | |
print(f"Image saved as {new_file_path}") | |
except ValueError as ve: | |
print(f"ValueError: {ve}") | |
except Exception as e: | |
print(f"Error converting image: {e}") | |
return rgba_img if rgba_img else img, str(new_file_path) | |
def delete_image(file_path: str) -> None: | |
""" | |
Deletes the specified image file. | |
Parameters: | |
file_path (str): The path to the image file to delete. | |
Raises: | |
FileNotFoundError: If the file does not exist. | |
Exception: If there is an error deleting the file. | |
""" | |
try: | |
path = Path(file_path) | |
path.unlink() | |
print(f"Deleted original image: {file_path}") | |
except FileNotFoundError: | |
print(f"File not found: {file_path}") | |
except Exception as e: | |
print(f"Error deleting image: {e}") | |
def resize_all_images_in_folder(target_width: int, output_folder: str = "resized", file_prefix: str = "resized_") -> tuple[int, int]: | |
""" | |
Resizes all images in the current folder to a specified width while maintaining aspect ratio. | |
Creates a new folder for the resized images. | |
Parameters: | |
target_width (int): The desired width for all images | |
output_folder (str): Name of the folder to store resized images (default: "resized") | |
file_prefix (str): Prefix for resized files (default: "resized_") | |
Returns: | |
tuple[int, int]: (number of successfully resized images, number of failed attempts) | |
Example Usage: | |
successful_count, failed_count = resize_all_images_in_folder(target_width=800, output_folder="th", file_prefix="th_") | |
""" | |
# Supported image extensions | |
valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff') | |
# Create output folder if it doesn't exist | |
output_path = Path(output_folder) | |
output_path.mkdir(exist_ok=True) | |
successful = 0 | |
failed = 0 | |
# Get current directory | |
current_dir = Path.cwd() | |
# Iterate through all files in current directory | |
for file_path in current_dir.iterdir(): | |
if file_path.is_file() and file_path.suffix.lower() in valid_extensions: | |
try: | |
# Open the image | |
with Image.open(file_path) as img: | |
# Convert to RGB if needed (handles RGBA, CMYK, etc.) | |
if img.mode != 'RGB': | |
img = img.convert('RGB') | |
# Calculate target height maintaining aspect ratio | |
original_width, original_height = img.size | |
aspect_ratio = original_height / original_width | |
target_height = int(target_width * aspect_ratio) | |
# Resize using the reference function | |
resized_img = resize_image_with_aspect_ratio(img, target_width, target_height) | |
# Create output filename | |
output_filename = output_path / f"{file_prefix}{file_path.name.lower()}" | |
# Save the resized image | |
resized_img.save(output_filename, quality=95) | |
successful += 1 | |
print(f"Successfully resized: {file_path.name.lower()}") | |
except Exception as e: | |
failed += 1 | |
print(f"Failed to resize {file_path.name.lower()}: {str(e)}") | |
print(f"\nResizing complete. Successfully processed: {successful}, Failed: {failed}") | |
return successful, failed | |
def get_image_quality(file_path): | |
"""Determine quality based on image width.""" | |
try: | |
with Image.open(file_path) as img: | |
width, _ = img.size | |
if width < 1025: | |
return 0 | |
elif width < 1537: | |
return 1 | |
elif width < 2680: | |
return 2 | |
else: # width >= 2680 | |
return 3 | |
except Exception as e: | |
print(f"Error opening {file_path}: {e}") | |
return 0 # Default to 0 if there's an error | |
def update_quality(): | |
"""Update quality for each file in PRE_RENDERED_MAPS_JSON_LEVELS.""" | |
possible_paths = ["./", "./images/prerendered/"] | |
for key, value in PRE_RENDERED_MAPS_JSON_LEVELS.items(): | |
file_path = value['file'] | |
found = False | |
# Check both possible locations | |
for base_path in possible_paths: | |
full_path = os.path.join(base_path, os.path.basename(file_path)) | |
if os.path.exists(full_path): | |
quality = get_image_quality(full_path) | |
PRE_RENDERED_MAPS_JSON_LEVELS[key]['quality'] = quality | |
print(f"Updated {key}: Quality set to {quality} (Width checked at {full_path})") | |
found = True | |
break | |
if not found: | |
print(f"Warning: File not found for {key} at any location. Keeping quality as {value['quality']}") | |
def print_json(): | |
"""Print the updated PRE_RENDERED_MAPS_JSON_LEVELS in a formatted way.""" | |
print("\nUpdated PRE_RENDERED_MAPS_JSON_LEVELS = {") | |
for key, value in PRE_RENDERED_MAPS_JSON_LEVELS.items(): | |
print(f" '{key}': {{'file': '{value['file']}', 'thumbnail': '{value['thumbnail']}', 'quality': {value['quality']}}},") | |
print("}") | |
def calculate_optimal_fill_dimensions(image: Image.Image): | |
# Extract the original dimensions | |
original_width, original_height = image.size | |
# Set constants | |
MIN_ASPECT_RATIO = 9 / 16 | |
MAX_ASPECT_RATIO = 16 / 9 | |
FIXED_DIMENSION = 1024 | |
# Calculate the aspect ratio of the original image | |
original_aspect_ratio = original_width / original_height | |
# Determine which dimension to fix | |
if original_aspect_ratio > 1: # Wider than tall | |
width = FIXED_DIMENSION | |
height = round(FIXED_DIMENSION / original_aspect_ratio) | |
else: # Taller than wide | |
height = FIXED_DIMENSION | |
width = round(FIXED_DIMENSION * original_aspect_ratio) | |
# Ensure dimensions are multiples of 8 | |
width = (width // 8) * 8 | |
height = (height // 8) * 8 | |
# Enforce aspect ratio limits | |
calculated_aspect_ratio = width / height | |
if calculated_aspect_ratio > MAX_ASPECT_RATIO: | |
width = (height * MAX_ASPECT_RATIO // 8) * 8 | |
elif calculated_aspect_ratio < MIN_ASPECT_RATIO: | |
height = (width / MIN_ASPECT_RATIO // 8) * 8 | |
# Ensure width and height remain above the minimum dimensions | |
width = max(width, 576) if width == FIXED_DIMENSION else width | |
height = max(height, 576) if height == FIXED_DIMENSION else height | |
return width, height |