def check_input_file(path,masks):
ret, img_1 = load_img(path+masks[0], grayscale=True)
assert (ret == 0)
height, width = img_1.shape
num_img = len(masks)
for next_index in range(1,num_img):
next_ret, next_img=load_img(path+masks[next_index],grayscale=True)
assert (next_ret ==0 )
next_height, next_width = next_img.shape
assert(height==next_height and width==next_width)
return height, width
def combine_all_mask(height, width,input_path,mask_pool):
"""
:param height:
:param width:
:param input_path:
:param mask_pool:
:return: final mask from roads_mask and buildings_mask
prior: road(1)>bulidings(2)
"""
final_mask=np.zeros((height,width),np.uint8)
for idx,file in enumerate(mask_pool):
ret,img = load_img(input_path+file,grayscale=True)
assert (ret == 0)
label_value=0
if 'road' in file:
label_value =ROAD_VALUE
elif 'building' in file:
label_value=BUILDING_VALUE
# label_value = idx+1
for i in tqdm(range(height)):
for j in range(width):
if img[i,j]>=FOREGROUND:
print ("img[{},{}]:{}".format(i,j,img[i,j]))
if label_value==ROAD_VALUE:
final_mask[i,j]=label_value
elif label_value==BUILDING_VALUE and final_mask[i,j]!=ROAD_VALUE:
final_mask[i,j]=label_value
# print ("final_mask[{},{}]:{}".format(i, j, final_mask[i, j]))
return final_mask
def vote_per_image(height, width, path, masks):
mask_list = []
for tt in range(len(masks)):
ret, img = load_img(path + masks[tt], grayscale=True)
assert (ret == 0)
mask_list.append(img)
vote_mask = np.zeros((height, width), np.uint8)
for i in tqdm(range(height)):
for j in range(width):
record = np.zeros(256, np.uint8)
for n in range(len(mask_list)):
mask = mask_list[n]
pixel = mask[i, j]
record[pixel] += 1
"""Alarming"""
# if record.argmax()==0: # if argmax of 0 = 125 or 255, not prior considering background(0)
# record[0] -=1
label = record.argmax()
# print ("{},{} label={}".format(i,j,label))
vote_mask[i, j] = label
return vote_mask
import cv2
import sys, os
from ulitities.base_functions import load_img, get_file
input_path = '../../data/traindata/unet/buildings/label/'
output_path = '../../data/traindata/unet/buildings/visulize/'
if __name__ == '__main__':
if not os.path.isdir(input_path):
print("No input directory:{}".format(input_path))
sys.exit(-1)
if not os.path.isdir(output_path):
print("No output directory:{}".format(output_path))
os.mkdir(output_path)
srcfiles, tt = get_file(input_path)
assert (tt != 0)
for index, file in enumerate(srcfiles):
ret, img = load_img(file, grayscale=True)
assert (ret == 0)
img = img * 100
filename = os.path.split(file)[1]
outfile = os.path.join(output_path, filename)
print(outfile)
cv2.imwrite(outfile, img)
#coding:utf-8
import cv2
import numpy as np
import matplotlib.pyplot as plt
from ulitities.base_functions import load_img, compare_two_image_size
road_label_file = '../../data/originaldata/unet/roads/label/5.png'
building_label_file = '../../data/originaldata/unet/buildings/label/5.png'
output_file = '../../data/originaldata/all/label/5.png'
if __name__=='__main__':
ret1, road_label = load_img(road_label_file, grayscale=True)
assert(ret1==0)
ret2, building_label = load_img(building_label_file, grayscale=True)
assert (ret2== 0)
compare_two_image_size(road_label, building_label, grayscale=True)
height, width = road_label.shape
output_label = np.zeros((height,width), np.uint8)
index = np.where(road_label==1)
output_label[index]=1
idx = np.where(building_label == 1)
output_label[idx] = 2
if __name__ == '__main__':
src_files,num = get_file(input_src_path)
assert (num!=0)
valid_labels = []
if FLAG_USING_UNET:
valid_labels = unet_labels
else:
valid_labels = segnet_labels
for src_file in tqdm(src_files):
label_file = input_label_path + os.path.split(src_file)[1]
ret,src_img = load_img(src_file)
assert(ret==0)
ret,label_img = load_img(label_file, grayscale=True)
assert (ret == 0)
local_labels = np.unique(label_img)
for tmp in local_labels:
if tmp not in valid_labels:
print ("\nWarning: some label is not valid value")
print ("\nFile: {}".format(label_file))
HAS_INVALID_VALUE = True
if HAS_INVALID_VALUE == True: