trash_pickup.py

import numpy as np
import matplotlib.pyplot as plt
import cv2
from PIL import Image
from skimage.filters import threshold_otsu
from skimage import measure
from scipy import ndimage, signal
import heapq
import color_constancy as cc
import os
import time
import pandas as pd



width = 600
height = 450
preserve_size = 600
paths = [r'C:\Users\ptrkm\OneDrive\Dokumenter\Bachelor deep learning\Data ISIC\ISIC_2019_Training_Input\\']
return_folder = r'C:\Users\ptrkm\OneDrive\Dokumenter\TestFolder\return\\'
# paths = [r'C:\Users\Bruger\OneDrive\DTU - General engineering\6. Semester\Bachelor\ISBI2016_ISIC_Part2B_Training_Data\TestRunImages\\']
# return_folder = r'C:\Users\Bruger\OneDrive\DTU - General engineering\6. Semester\Bachelor\ISBI2016_ISIC_Part2B_Training_Data\TestRunImagesOutput\\'
standard_size = np.asarray([height, width])
preserve_ratio = True
margin = 0.1
crop_black = True
k = 200
threshold = 0.7
resize = True
use_color_constancy = True
write_to_png = False
write = True
ind = 1
all_heights = 0
all_width = 0
use_cropping = False
errors = []
area_threshold = 0.80

full_data = os.listdir(paths[0])
cropped_data = os.listdir(return_folder)

unused_data = list(set(full_data)-set(cropped_data))






for i,images in enumerate(cropped_data):
    try:
        image = cv2.imread(return_folder+images)

        if image.shape[0] < 50 or image.shape[1] < 50:
            image = cv2.imread(paths[0]+images)

            if resize:
                if preserve_ratio:
                    if image.shape[0] > image.shape[1]:
                        image = np.moveaxis(image, [0, 1, 2], [1, 0, 2])

                    if image.shape[1] != preserve_size:
                        ratio = preserve_size / image.shape[1]
                        try:
                            image = cv2.resize(image, dsize=(round(image.shape[0] * ratio), preserve_size))
                        except:
                            print("resize problem on image" + images)
                            errors.append(images)
                            continue
            R, G, B, new_image = cc.general_color_constancy(image, 0, 6, 0)
            new_image = np.uint8(new_image)

            im = Image.fromarray(new_image.astype('uint8')).convert('RGB')

            im.save(return_folder + images)
    except:
        print(images)
        continue

    if i % 100 == 0: print(i)