import os
import numpy as np
import pandas as pd
from PIL import Image
from atoms_detection.image_preprocessing import dl_prepro_image
from atoms_detection.dataset import CoordinatesDataset
from utils.paths import CROPS_PATH, CROPS_DATASET, PT_DATASET
from utils.constants import Split, CropsColumns
np.random.seed(777)
window_size = (21, 21)
halfx_window = ((window_size[0] - 1) // 2)
halfy_window = ((window_size[1] - 1) // 2)
def get_gaussian_kernel(size=33, mean=0, sigma=0.2):
# Initializing value of x-axis and y-axis
# in the range -1 to 1
x, y = np.meshgrid(np.linspace(-1, 1, size), np.linspace(-1, 1, size))
dst = np.sqrt(x * x + y * y)
# Calculating Gaussian array
kernel = np.exp(-((dst - mean) ** 2 / (2.0 * sigma ** 2)))
return kernel
def generate_support_img(coordinates, window_size):
support_img = np.zeros((1024, 1024))
kernel = get_gaussian_kernel(size=window_size[0])
halfx_window = ((window_size[0] - 1) // 2)
halfy_window = ((window_size[1] - 1) // 2)
for x, y in coordinates:
x_range = (x - halfx_window, x + halfx_window + 1)
y_range = (y - halfy_window, y + halfy_window + 1)
x_diff = [0, 0]
y_diff = [0, 0]
if x_range[0] < 0:
x_diff[0] = 0 - x_range[0]
if x_range[1] > 1024:
x_diff[1] = x_range[1] - 1024
if y_range[0] < 0:
y_diff[0] = 0 - y_range[0]
if y_range[1] > 1024:
y_diff[1] = y_range[1] - 1024
x_diff = tuple(int(item) for item in x_diff)
y_diff = tuple(int(item) for item in y_diff)
real_kernel = kernel[x_diff[0]:window_size[0] - x_diff[1], y_diff[0]:window_size[1] - y_diff[1]]
real_x_crop = (x_range[0] + x_diff[0], x_range[1] - x_diff[1])
real_y_crop = (y_range[0] + y_diff[0], y_range[1] - y_diff[1])
real_x_crop = tuple(int(item) for item in real_x_crop)
real_y_crop = tuple(int(item) for item in real_y_crop)
support_img[real_x_crop[0]:real_x_crop[1], real_y_crop[0]:real_y_crop[1]] += real_kernel
support_img = support_img.T
return support_img
def open_image(img_filename):
img = Image.open(img_filename)
np_img = np.asarray(img).astype(np.float32)
np_img = dl_prepro_image(np_img)
img = Image.fromarray(np_img)
return img
def create_crop(img: Image, x_center: int, y_center: int):
crop_coords = (
x_center - halfx_window,
y_center - halfy_window,
x_center + halfx_window + 1,
y_center + halfy_window + 1
)
crop = img.crop(crop_coords)
return crop
def create_crops_dataset(crops_folder: str, coords_csv: str, crops_dataset: str):
if not os.path.exists(crops_folder):
os.makedirs(crops_folder)
crop_name_list = []
orig_name_list = []
x_list = []
y_list = []
label_list = []
n_positives = 0
label = 1
dataset = CoordinatesDataset(coords_csv)
print('Creating positive crops...')
for data_filename, label_filename in dataset.iterate_data(Split.TRAIN):
if label_filename is None:
continue
print(data_filename)
orig_img_name = os.path.basename(data_filename)
img_name = os.path.splitext(orig_img_name)[0]
img = open_image(data_filename)
coordinates = dataset.load_coordinates(label_filename)
for x_center, y_center in coordinates:
crop = create_crop(img, x_center, y_center)
crop_name = "{}_{}_{}.tif".format(img_name, x_center, y_center)
crop.save(os.path.join(crops_folder, crop_name))
crop_name_list.append(crop_name)
orig_name_list.append(orig_img_name)
x_list.append(x_center)
y_list.append(y_center)
label_list.append(label)
n_positives += 1
label = 0
no_train_images = dataset.split_length(Split.TRAIN)
neg_crops_per_image = [n_positives // no_train_images + (1 if x < n_positives % no_train_images else 0) for x in range(no_train_images)]
print('Creating negative crops...')
for (data_filename, label_filename), no_neg_crops in zip(dataset.iterate_data(Split.TRAIN), neg_crops_per_image):
print(data_filename)
orig_img_name = os.path.basename(data_filename)
img_name = os.path.splitext(orig_img_name)[0]
img = open_image(data_filename)
if label_filename:
coordinates = dataset.load_coordinates(label_filename)
support_map = generate_support_img(coordinates, window_size)
else:
support_map = None
for _ in range(no_neg_crops):
x_rand = np.random.randint(0, 1024)
y_rand = np.random.randint(0, 1024)
if support_map is not None:
while support_map[x_rand, y_rand] != 0:
x_rand = np.random.randint(0, 1024)
y_rand = np.random.randint(0, 1024)
x_center, y_center = x_rand, y_rand
crop = create_crop(img, x_center, y_center)
crop_name = "{}_{}_{}.tif".format(img_name, x_center, y_center)
crop.save(os.path.join(crops_folder, crop_name))
crop_name_list.append(crop_name)
orig_name_list.append(orig_img_name)
x_list.append(x_center)
y_list.append(y_center)
label_list.append(label)
df_data = {
CropsColumns.FILENAME: crop_name_list,
CropsColumns.ORIGINAL: orig_name_list,
CropsColumns.X: x_list,
CropsColumns.Y: y_list,
CropsColumns.LABEL: label_list
}
df = pd.DataFrame(df_data, columns=[
CropsColumns.FILENAME,
CropsColumns.ORIGINAL,
CropsColumns.X,
CropsColumns.Y,
CropsColumns.LABEL
])
df_pos = df[df.Label == 1]
df_neg = df[df.Label == 0]
pos_len = len(df_pos)
neg_len = len(df_neg)
pos_train, pos_val, pos_test = np.split(df_pos.sample(frac=1), [int(0.8*pos_len), int(0.9*pos_len)])
neg_train, neg_val, neg_test = np.split(df_neg.sample(frac=1), [int(0.8*neg_len), int(0.9*neg_len)])
pos_train[CropsColumns.SPLIT] = Split.TRAIN
pos_val[CropsColumns.SPLIT] = Split.VAL
pos_test[CropsColumns.SPLIT] = Split.TEST
neg_train[CropsColumns.SPLIT] = Split.TRAIN
neg_val[CropsColumns.SPLIT] = Split.VAL
neg_test[CropsColumns.SPLIT] = Split.TEST
df_with_splits = pd.concat((pos_train, neg_train, pos_val, neg_val, pos_test, neg_test), axis=0)
df_with_splits.to_csv(crops_dataset, header=True, index=False)
if __name__ == "__main__":
create_crops_dataset(CROPS_PATH, PT_DATASET, CROPS_DATASET)