119 lines
4.1 KiB
Python
119 lines
4.1 KiB
Python
import os
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from PIL import Image, ImageChops, ImageStat
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def get_gimp_export_path():
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# Path to GIMP's configuration file. This example assumes GIMP version 2.10.
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# You need to adjust the path according to the GIMP version and user's file system.
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gimp_config_file = os.path.expanduser("~/.config/GIMP/2.10/gimprc")
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try:
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# Open and read the configuration file
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with open(gimp_config_file, 'r') as file:
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for line in file:
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# Search for the default export path setting
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if "default-export-path" in line:
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# Extract the current path from the line (assuming it's enclosed in quotes)
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current_path = line.split('"')[1]
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# Compare the current path with the expected path
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return current_path
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except FileNotFoundError:
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# Handle the case where the configuration file is not found
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print("GIMP configuration file not found")
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return False
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def check_file_exists(directory, filename):
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file_path = os.path.join(directory, filename)
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return 1 if os.path.isfile(file_path) else 0
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def increase_saturation(image1_path: str, image2_path: str) -> float:
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def calculate_saturation(image):
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# convert the image to HSV mode
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hsv_image = image.convert("HSV")
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saturation_channel = hsv_image.split()[1]
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# calculate the mean saturation level
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stat = ImageStat.Stat(saturation_channel)
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mean_saturation = stat.mean[0]
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return mean_saturation
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image1 = Image.open(image1_path)
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image2 = Image.open(image2_path)
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# calculate the saturation level of each image
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saturation1 = calculate_saturation(image1)
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saturation2 = calculate_saturation(image2)
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return 1 if saturation1 < saturation2 else 0
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def decrease_brightness(image1_path: str, image2_path: str) -> float:
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def calculate_brightness(image):
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# Convert the image to grayscale mode
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grayscale_image = image.convert("L")
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# Get the image data
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pixels = list(grayscale_image.getdata())
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brightness = sum(pixels) / len(pixels)
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return brightness
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image1 = Image.open(image1_path)
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image2 = Image.open(image2_path)
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brightness1 = calculate_brightness(image1)
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brightness2 = calculate_brightness(image2)
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return 1 if brightness1 > brightness2 else 0
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import cv2
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import numpy as np
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def find_yellow_triangle(image):
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# Convert the image to RGBA
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rgba = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA)
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# define range of yellow color in HSV
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lower_yellow = np.array([0, 0, 0], dtype=np.uint8)
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upper_yellow = np.array([255, 255, 255], dtype=np.uint8)
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# expand the dimensions of lower and upper yellow to match the image dimensions
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lower_yellow = np.reshape(lower_yellow, (1, 1, 3))
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upper_yellow = np.reshape(upper_yellow, (1, 1, 3))
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# build a mask for the yellow color
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mask = cv2.inRange(rgba, lower_yellow, upper_yellow)
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# search for contours in the mask
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# choose the largest contour
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max_contour = max(contours, key=cv2.contourArea)
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# calculate the center of the contour
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M = cv2.moments(max_contour)
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cx = int(M['m10'] / M['m00'])
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cy = int(M['m01'] / M['m00'])
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return cx, cy
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def compare_triangle_positions(image1, image2):
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image1 = cv2.imread(image1, cv2.IMREAD_COLOR)
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image2 = cv2.imread(image2, cv2.IMREAD_COLOR)
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# find the center of the yellow triangle in each image
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cx1, cy1 = find_yellow_triangle(image1)
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cx2, cy2 = find_yellow_triangle(image2)
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# calculate the distance between the center of the triangle and the center of the image
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center_distance1 = np.sqrt((cx1 - image1.shape[1] // 2)**2 + (cy1 - image1.shape[0] // 2)**2)
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center_distance2 = np.sqrt((cx2 - image2.shape[1] // 2)**2 + (cy2 - image2.shape[0] // 2)**2)
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return 1 if center_distance1 > center_distance2 else 0
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if __name__ == "__main__":
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image1_path = "../Downloads/1.png"
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image2_path = "../Downloads/edited_darker.png"
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decrease_brightness(image1_path, image2_path)
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increase_saturation(image1_path, image2_path)
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