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extract.py
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import cv2
from skimage import io
from barvocuc import ImageAnalyzer
import os
import pandas as pd
import numpy as np
import utils
from pathlib import Path
import utils.directory_utils
import utils.yaml_utils
class LuminanceExtraction:
def __init__(self, config):
self.config = utils.yaml_utils.read_config(config)
# results = {}
def automatic_color_segmentation(self):
folders_to_process, save_folder = utils.directory_utils.search_existing_directories(self.config,
"auto_segment_results",
"background_segmented")
for folder in folders_to_process:
subfolder = os.path.join(save_folder, os.path.basename(folder))
os.makedirs(subfolder)
overall_luminance = []
light_values = []
dark_values = []
light_amount = []
dark_amount = []
for file in os.listdir(folder):
analysis = ImageAnalyzer(Path(os.path.join(folder, file)))
overall_lum = np.mean(analysis.arrays["l"]) / np.max(analysis.arrays["l"])
rel_mel_lum = np.mean(analysis.arrays["l"][np.where(analysis.arrays["black"])]) / np.max(
analysis.arrays["l"])
rel_non_mel_lum = np.mean(analysis.arrays["l"][np.where(~analysis.arrays["black"])]) / np.max(
analysis.arrays["l"])
non_mel_area = analysis.results["colorful"] + analysis.results["white"] + analysis.results["gray"]
mel_area = analysis.results["black"]
light_values.append(rel_non_mel_lum)
dark_values.append(rel_mel_lum)
light_amount.append(non_mel_area)
dark_amount.append(mel_area)
overall_luminance.append(overall_lum)
luma_values = pd.DataFrame(list(zip(overall_luminance, light_values, dark_values, light_amount, dark_amount)),
columns=["Overall Lum", "Light lum", "Dark lum", "Light prop", "Dark prop"])
luma_values.to_csv(Path(os.path.join(subfolder, "luma_values.csv")), sep=',')
print("Finished! results saved to: " + subfolder + "luma_values.csv")
# TODO: Masked image is based off sklearn, which segments image with recolorization
# to correct, get unique values from masked array (image with 4 clusters should have 4 unique colors).
# color to mask, in this case melanistic color, will have lowest value
def extract_manual_segmentations(self):
folders_to_process, save_folder = utils.directory_utils.search_existing_directories(self.config,
"manual_segment_results",
"manually_segmented")
modified_images_folder = Path(self.config["project_path"]) / "modified"
def extract(original, masked):
original_mask = np.array(original[:, :, 0], copy=True, dtype=bool).astype(float)
masked_mask = np.array(masked[:, :, 0], copy=True, dtype=bool).astype(float)
masked_mask[original_mask == 0] = np.nan
'''might switch to luminance calculation here, need to see what other packages do/recommend'''
# def lum_convert(arr):
# red = 0.2126
# green = 0.7152
# blue = 0.0722
# red_ch = np.multiply(arr[:, :, 0], red)
# green_ch = np.multiply(arr[:, :, 1], green)
# blue_ch = np.multiply(arr[:, :, 2], blue)
# lum_arr = np.add(red_ch, green_ch, blue_ch)
# return lum_arr
img_yuv = cv2.cvtColor(original[:, :, [2, 1, 0]], cv2.COLOR_BGR2YUV)
luma, u, v = cv2.split(img_yuv)
# calc_luma = lum_convert(original)
light = luma[np.where(masked_mask == 1)]
dark = luma[np.where(masked_mask == 0)]
light_luma = np.mean(light) / np.max(luma)
dark_luma = np.mean(dark) / np.max(luma)
image_size = len(original_mask[original_mask == 1])
light_proportion = len(light) / image_size
dark_proportion = len(dark) / image_size
return light_luma, dark_luma, light_proportion, dark_proportion
for folder in folders_to_process:
subfolder = os.path.join(save_folder, os.path.basename(folder))
os.makedirs(subfolder)
modified_subdirectory = modified_images_folder / os.path.basename(folder)
modified_images = os.listdir(modified_subdirectory)
light_values = []
dark_values = []
light_amount = []
dark_amount = []
for file in os.listdir(folder):
mod_image_path = [i for i in modified_images if i == file]
if len(mod_image_path) == 1:
mod_image = io.imread(Path(os.path.join(modified_images_folder, mod_image_path[0]))).astype(np.uint8)
masked_image = io.imread(Path(os.path.join(folder, file))).astype(np.uint8)
light_luma, dark_luma, light_proportion, dark_proportion = extract(mod_image, masked_image)
light_values.append(light_luma)
dark_values.append(dark_luma)
light_amount.append(light_proportion)
dark_amount.append(dark_proportion)
elif len(mod_image_path) > 1:
print("multiple files found to match")
elif len(mod_image_path) < 1:
print("No matching images found")
luma_values = pd.DataFrame(list(zip(light_values, dark_values, light_amount, dark_amount)),
columns=["Light lum", "Dark lum", "Light prop", "Dark prop"])
if len(luma_values) > 0:
luma_values.to_csv(Path(os.path.join(subfolder, "luma_values.csv")), sep=',')
print("Finished! luma_values.csv results saved to: {}".format(save_folder))
else:
print("Error: No values calculated for folder {}, output file not saved".format(folder))