322 lines
10 KiB
Python
322 lines
10 KiB
Python
import os
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from PIL import Image, ImageStat
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from skimage.metrics import structural_similarity as ssim
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import numpy as np
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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(
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(cx1 - image1.shape[1] // 2) ** 2 + (cy1 - image1.shape[0] // 2) ** 2)
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center_distance2 = np.sqrt(
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(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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# Functions for the GIMP evaluator
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def calculate_brightness(image):
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"""Calculate the average brightness of an image"""
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grayscale = image.convert('L')
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stat = ImageStat.Stat(grayscale)
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return stat.mean[0]
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def normalize_brightness(image, target_brightness):
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"""Normalize the brightness of an image to a target brightness in [0, 1]"""
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current_brightness = calculate_brightness(image)
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factor = target_brightness / current_brightness
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# Apply a point transform to each pixel
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def point_transform(x):
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return min(255, max(0, int(x * factor)))
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return image.point(point_transform)
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def measure_saturation(hsv_image):
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"""Measure the average saturation of an image"""
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# Split into H, S, V channels
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_, s, _ = hsv_image.split()
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# Convert the saturation channel to a numpy array
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s_array = np.array(s)
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# Calculate the average saturation
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avg_saturation = np.mean(s_array)
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return avg_saturation
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def calculate_contrast(image):
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"""Calculate the contrast of an image as the standard deviation of the pixel
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values."""
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pixels = np.asarray(image, dtype=np.float32)
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return np.std(pixels)
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def structure_check_by_mse(img1, img2, threshold=0.03):
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"""Check if two images are approximately the same by MSE"""
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mse = np.mean(
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(np.array(img1, dtype=np.float32) / 255
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- np.array(img2, dtype=np.float32) / 255) ** 2)
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structure_same = True if mse < threshold else False
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return structure_same
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def structure_check_by_ssim(img1, img2, threshold=0.9):
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"""Check if two images are approximately the same by SSIM"""
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similarity = ssim(np.array(img1), np.array(img2), multichannel=True)
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return similarity >= threshold
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def check_brightness_decrease_and_structure_sim(src_path, tgt_path):
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"""
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Compare the brightness and structure of two images
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gimp:7a4deb26-d57d-4ea9-9a73-630f66a7b568
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"""
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img_src = Image.open(src_path)
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img_tgt = Image.open(tgt_path)
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# Brightness comparison
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brightness_src = calculate_brightness(img_src)
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brightness_tgt = calculate_brightness(img_tgt)
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brightness_reduced = brightness_tgt < brightness_src
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# Normalize and compare images
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target_brightness = 128
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img_src_normalized = normalize_brightness(img_src, target_brightness)
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img_tgt_normalized = normalize_brightness(img_tgt, target_brightness)
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structure_same = structure_check_by_mse(img_src_normalized, img_tgt_normalized)
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return brightness_reduced and structure_same
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def check_saturation_increase_and_structure_sim(src_path, tgt_path):
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"""
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Compare the saturation of two images
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gimp:554785e9-4523-4e7a-b8e1-8016f565f56a
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"""
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img_src = Image.open(src_path)
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hsv_img_src = img_src.convert('HSV')
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img_tgt = Image.open(tgt_path)
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hsv_img_tgt = img_tgt.convert('HSV')
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# Saturation comparison
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src_saturation = measure_saturation(hsv_img_src)
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tgt_saturation = measure_saturation(hsv_img_tgt)
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saturation_increased = tgt_saturation > src_saturation
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# Structure comparison
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h1, s1, v1 = hsv_img_src.split()
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h2, s2, v2 = hsv_img_tgt.split()
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h_same = structure_check_by_ssim(h1, h2)
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v_same = structure_check_by_ssim(v1, v2)
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if h_same and v_same:
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structure_same = True
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else:
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structure_same = False
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return saturation_increased and structure_same
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def check_file_exists_and_structure_sim(src_path):
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"""
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Check if the image has been exported to the desktop
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gimp:77b8ab4d-994f-43ac-8930-8ca087d7c4b4
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"""
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desktop_directory = os.path.expanduser("~/Desktop")
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target_image = os.path.join(desktop_directory, "export.jpg")
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# Check if the target image exists
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file_exists = os.path.isfile(target_image)
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# Check whether the target image is the same as the source image
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img_src = Image.open(src_path)
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img_tgt = Image.open(target_image)
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structure_same = structure_check_by_ssim(img_src, img_tgt)
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return file_exists and structure_same
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def check_triangle_position(tgt_path):
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"""
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Check if the triangle is in the middle of the image.
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gimp:f4aec372-4fb0-4df5-a52b-79e0e2a5d6ce
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"""
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# Load the image
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img = Image.open(tgt_path)
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img_array = np.array(img)
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# We will determine if the triangle is in the middle of the picture by checking the centroid
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# We assume the triangle is a different color from the background
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# Find the unique colors
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unique_colors = np.unique(img_array.reshape(-1, img_array.shape[2]), axis=0)
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# Assuming the background is the most common color and the triangle is a different color,
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# we identify the triangle's color as the least common one
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triangle_color = unique_colors[-1]
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# Create a mask where the triangle pixels are True
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triangle_mask = np.all(img_array == triangle_color, axis=2)
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# Get the coordinates of the triangle pixels
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triangle_coords = np.argwhere(triangle_mask)
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# Calculate the centroid of the triangle
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centroid = triangle_coords.mean(axis=0)
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# Check if the centroid is approximately in the middle of the image
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image_center = np.array(img_array.shape[:2]) / 2
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# We will consider the triangle to be in the middle if the centroid is within 5% of the image's center
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tolerance = 0.05 * np.array(img_array.shape[:2])
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middle = np.all(np.abs(centroid - image_center) < tolerance)
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return middle
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def check_structure_sim(src_path, tgt_path):
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"""
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Check if the structure of the two images are similar
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gimp:2a729ded-3296-423d-aec4-7dd55ed5fbb3
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"""
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img_src = Image.open(src_path)
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img_tgt = Image.open(tgt_path)
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structure_same = structure_check_by_ssim(img_src, img_tgt)
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return structure_same
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def check_contrast_increase_and_structure_sim(src_path, tgt_path):
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"""
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Check if the contrast of the image has increased
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gimp:f723c744-e62c-4ae6-98d1-750d3cd7d79d
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"""
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# Load images
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source_image = Image.open(src_path)
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target_image = Image.open(tgt_path)
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# Calculate contrast
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source_contrast = calculate_contrast(source_image)
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target_contrast = calculate_contrast(target_image)
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higher_contrast = target_contrast > source_contrast
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# Check structure
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structure_same = structure_check_by_ssim(source_image, target_image, threshold=0.8)
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return higher_contrast and structure_same
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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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