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image-filters.py
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image-filters.py
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import json
import numpy as np
from skimage.exposure import rescale_intensity
from skimage import io
from skimage.color import rgb2hed, rgb2gray
from skimage import util
import os
from os import listdir
from os.path import isfile, join
import shutil
import random
import copy
base_dir = "e:\\dev\\datasets\\ann_results\\ann_results_3 (belt)\\separated"
base_train_images_dir = "e:\\dev\\datasets\\ann_results\\ann_results_3 (belt)\\separated\\train\\images"
base_val_images_dir = "e:\\dev\\datasets\\ann_results\\ann_results_3 (belt)\\separated\\val\\images"
destination_base_dir = "e:\\Dev\\datasets\\detectron-input\\hggb\\origin_with_filters"
destination_train_images_dir = "e:\\Dev\\datasets\\detectron-input\\hggb\\origin_with_filters\\train"
destination_val_images_dir = "e:\\Dev\\datasets\\detectron-input\\hggb\\origin_with_filters\\val"
with open(os.path.join(base_dir, "train\\instances_train.json"), "r") as annotationsTrain:
annotationsTrainData = json.load(annotationsTrain)
with open(os.path.join(base_dir, "val\\instances_val.json"), "r") as annotationsVal:
annotationsValData = json.load(annotationsVal)
newAnnotationsTrainData = copy.deepcopy(annotationsTrainData)
newAnnotationsValData = dict()
newAnnotationsValData["images"] = dict()
newAnnotationsValData["annotations"] = dict()
def get_max_id(dict, dict_key):
max = 0
for img in dict[dict_key]:
if img["id"] > max:
max = img["id"]
return max
max_train_img_id = get_max_id(newAnnotationsTrainData, "images")
max_train_img_ann_id = get_max_id(newAnnotationsTrainData, "annotations")
max_val_img_id = 0
max_val_img_ann_id = 0
def increment_max_img_id():
global max_train_img_id
max_train_img_id += 1
return max_train_img_id
def increment_max_img_ann_id():
global max_train_img_ann_id
max_train_img_ann_id += 1
return max_train_img_ann_id
def get_destination_img_path(destination_images_dir, filter_number, filename, extension):
return os.path.join(destination_images_dir, filename + "_f{}".format(str(filter_number)) + extension)
def copy_instance_annotations(img_entity, img_annotations, new_img_name, destination_dict):
max_img_id = increment_max_img_id()
img_entity = copy.deepcopy(img_entity)
img_annotations = copy.deepcopy(img_annotations)
img_entity["id"] = max_img_id
img_entity["file_name"] = new_img_name
destination_dict["images"].append(img_entity)
for img_ann in img_annotations:
max_img_ann_id = increment_max_img_ann_id()
img_ann["id"] = max_img_ann_id
img_ann["image_id"] = max_img_id
destination_dict["annotations"].append(img_ann)
def get_img_annotations(img_name, annotations):
for img in annotations["images"]:
if img["file_name"] == img_name:
img_entity = img
break
img_annotations = []
for ann in annotations["annotations"]:
if ann["image_id"] == img_entity["id"]:
img_annotations.append(ann)
return copy.deepcopy(img_entity), copy.deepcopy(img_annotations)
def add_filters_to_img(destination_images_dir, img_path, img_name, annotations, destination_dict):
base, extension = os.path.splitext(img_name)
img_rgb = io.imread(img_path)
img_hed = rgb2hed(img_rgb)
f1_filepath = get_destination_img_path(destination_images_dir, 1, base, extension)
io.imsave(f1_filepath, rgb2gray(img_rgb))
img_entity, img_annotations = get_img_annotations(img_name, annotations)
copy_instance_annotations(img_entity, img_annotations, base + "_f1" + extension, destination_dict)
# Rescale hematoxylin and DAB signals and give them a fluorescence look
h = rescale_intensity(img_hed[:, :, 0], out_range=(0, 1))
d = rescale_intensity(img_hed[:, :, 2], out_range=(0, 1))
zdh = np.dstack((np.zeros_like(h), d, h))
f2_filepath = get_destination_img_path(destination_images_dir, 2, base, extension)
io.imsave(f2_filepath, zdh)
copy_instance_annotations(img_entity, img_annotations, base + "_f2" + extension, destination_dict)
zdh = np.dstack((np.zeros_like(h), d, d))
f3_filepath = get_destination_img_path(destination_images_dir, 3, base, extension)
io.imsave(f3_filepath, zdh)
copy_instance_annotations(img_entity, img_annotations, base + "_f3" + extension, destination_dict)
zdh = np.dstack((np.zeros_like(h), h, h))
f4_filepath = get_destination_img_path(destination_images_dir, 4, base, extension)
io.imsave(f4_filepath, zdh)
copy_instance_annotations(img_entity, img_annotations, base + "_f4" + extension, destination_dict)
f5_filepath = get_destination_img_path(destination_images_dir, 5, base, extension)
io.imsave(f5_filepath, util.random_noise(img_rgb[:,:,:3], mode="salt", amount=random.uniform(0.05, 0.15)))
copy_instance_annotations(img_entity, img_annotations, base + "_f5" + extension, destination_dict)
train_images = [f for f in listdir(base_train_images_dir) if isfile(join(base_train_images_dir, f))]
for img in train_images:
destination_img_path = os.path.join(destination_train_images_dir, img)
shutil.copy(os.path.join(base_train_images_dir, img), destination_img_path)
add_filters_to_img(destination_train_images_dir, os.path.join(base_train_images_dir, img), img, annotationsTrainData, newAnnotationsTrainData)
with open(os.path.join(destination_base_dir, "instances_train.json"), "w") as annTrainWithFilters:
json.dump(newAnnotationsTrainData, annTrainWithFilters)
for img in annotationsValData["images"]:
increment_max_img_id()
for ann in annotationsValData["annotations"]:
if ann["image_id"] == img["id"]:
ann["image_id"] = max_train_img_id
increment_max_img_ann_id()
ann["id"] = max_train_img_ann_id
img["id"] = max_train_img_id
newAnnotationsValData = copy.deepcopy(annotationsValData)
val_images = [f for f in listdir(base_val_images_dir) if isfile(join(base_val_images_dir, f))]
for img in val_images:
shutil.copy(os.path.join(base_val_images_dir, img), os.path.join(destination_val_images_dir, img))
add_filters_to_img(destination_val_images_dir, os.path.join(base_val_images_dir, img), img, annotationsValData, newAnnotationsValData)
with open(os.path.join(destination_base_dir, "instances_val.json"), "w") as annValWithFilters:
json.dump(newAnnotationsValData, annValWithFilters)