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import glob
import os.path
import platform
import numpy as np
from PIL import Image
# mine sectors
if platform.system() == "Windows":
base_dir = "C:/data/mine-sectors/"
else:
base_dir = "/home/maduschek/ssd/mine-sector-detection/"
# base_dir = "/home/maduschek/data/cats_dogs/"
input_dir_train = base_dir + "images_trainset/"
target_dir_train = base_dir + "masks_trainset/"
input_dir_test = base_dir + "images_testset/"
target_dir_test = base_dir + "masks_testset/"
shape_img_list = []
shape_mask_list = []
class_instances = dict()
k = 0
for img_path, mask_path in zip(glob.glob(os.path.join(input_dir_train, "*.png")), glob.glob(os.path.join(target_dir_train, "*.png"))):
k += 1
# get shape of all images
img = Image.open(img_path)
if img.size not in shape_img_list:
shape_img_list.append(img.size)
print("img shape", img.size)
# get shape of all masks
mask = Image.open(mask_path)
if mask.size not in shape_mask_list:
shape_mask_list.append(mask.size)
print("mask size", mask.size)
# get all possible classes
vals, counts = np.unique(np.asarray(mask), return_counts=True)
for val, count in zip(vals, counts):
if val in class_instances:
class_instances[val] += count
else:
class_instances[val] = count
if k % 100 == 0:
os.system("clear")
print(k)
for key in class_instances.keys():
print("class ", key, ": ", class_instances[key])
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