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post_process.py
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'''
Author : now more
Connect : [email protected]
LastEditors: Please set LastEditors
Description :
LastEditTime: 2020-11-27 03:42:46
'''
import os
import threading
import cv2 as cv
import numpy as np
from skimage.morphology import remove_small_holes, remove_small_objects
from argparse import ArgumentParser
from PIL import Image
Image.MAX_IMAGE_PIXELS = None
def to_categorical(y, num_classes=None, dtype='float32'):
"""Converts a class vector (integers) to binary class matrix.
E.g. for use with categorical_crossentropy.
# Arguments
y: class vector to be converted into a matrix
(integers from 0 to num_classes).
num_classes: total number of classes.
dtype: The data type expected by the input, as a string
(`float32`, `float64`, `int32`...)
# Returns
A binary matrix representation of the input. The classes axis
is placed last.
"""
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
class MyThread(threading.Thread):
def __init__(self, func, args=()):
super(MyThread, self).__init__()
self.func = func
self.args = args
def run(self):
self.result = self.func(*self.args)
def get_result(self):
try:
return self.result # 如果子线程不使用join方法，此处可能会报没有self.result的错误
except Exception:
return None
def label_resize_vis(label, img=None, alpha=0.5):
'''
:param label:原始标签
:param img: 原始图像
:param alpha: 透明度
:return: 可视化标签
'''
def label_to_RGB(image, classes=6):
RGB = np.zeros(shape=[image.shape[0], image.shape[1], 3], dtype=np.uint8)
if classes == 6: # potsdam and vaihingen
palette = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0], [255, 255, 0], [255, 0, 0]]
if classes == 4: # barley
palette = [[255, 255, 255], [0, 255, 0], [255, 255, 0], [255, 0, 0]]
for i in range(classes):
index = image == i
RGB[index] = np.array(palette[i])
return RGB
# label = cv.resize(label.copy(), None, fx=0.1, fy=0.1)
anno_vis = label_to_RGB(label, classes=4)
if img is None:
return anno_vis
else:
overlapping = cv.addWeighted(img, alpha, anno_vis, 1 - alpha, 0)
return overlapping
def remove_small_objects_and_holes(class_type, label, min_size, area_threshold, in_place=True):
print("------------- class_n : {} start ------------".format(class_type))
if class_type == 3:
# kernel = cv.getStructuringElement(cv.MORPH_RECT,(500,500))
# label = cv.dilate(label,kernel)
# kernel = cv.getStructuringElement(cv.MORPH_RECT,(10,10))
# label = cv.erode(label,kernel)
label = remove_small_objects(label == 1, min_size=min_size, connectivity=1, in_place=in_place)
label = remove_small_holes(label == 1, area_threshold=area_threshold, connectivity=1, in_place=in_place)
else:
label = remove_small_objects(label == 1, min_size=min_size, connectivity=1, in_place=in_place)
label = remove_small_holes(label == 1, area_threshold=area_threshold, connectivity=1, in_place=in_place)
print("------------- class_n : {} finished ------------".format(class_type))
return label
def RGB_to_label(image=None, classes=6):
if classes == 6: # potsdam and vaihingen
palette = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0], [255, 255, 0], [255, 0, 0]]
if classes == 4: # barley
palette = [[255, 255, 255], [0, 255, 0], [255, 255, 0], [255, 0, 0]]
label = np.zeros(shape=[image.shape[0], image.shape[1]], dtype=np.uint8)
for i in range(len(palette)):
index = image == np.array(palette[i])
index[..., 0][index[..., 1] == False] = False
index[..., 0][index[..., 2] == False] = False
label[index[..., 0]] = i
return label
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--image_n", type=int, default=2, help="传入1或2，指定")
parser.add_argument("--image_path", type=str, default='./outputs', help="传入image_n_predict所在路径")
parser.add_argument("--threshold", type=int, default=2000)
arg = parser.parse_args()
image_n = arg.image_n
image_path = arg.image_path
threshold = arg.threshold
if image_n == 1:
source_image = cv.imread("../../data/barley/images_size0.1/image_1.png")
elif image_n == 2:
source_image = cv.imread("../../data/barley/images_size0.1/image_2.png")
else:
raise ValueError("image_n should be 1 or 2, Got {} ".format(image_n))
img_mask_dir = os.path.join(image_path, f'image_{image_n}_mask.png')
img_dir = os.path.join(image_path, f'image_{image_n}.png')
if os.path.exists(img_mask_dir):
image = np.asarray(Image.open(img_mask_dir))
elif os.path.exists(img_dir):
image = np.asarray(Image.open(img_dir))
else:
raise ValueError(f"Not found image_{image_n}_mask.png or image_{image_n}.png")
if len(image.shape) == 3:
image = RGB_to_label(image, classes=4)
image_save = Image.fromarray(image)
image_save.save(os.path.join(image_path, f'image_{image_n}_mask.png'))
image = cv.resize(image, None, fx=0.1, fy=0.1, interpolation=cv.INTER_NEAREST) # because over memory
label = to_categorical(image, num_classes=4, dtype='uint8')
threading_list = []
for i in range(4):
t = MyThread(remove_small_objects_and_holes, args=(i, label[:, :, i], threshold, threshold, True))
threading_list.append(t)
t.start()
# 等待所有线程运行完毕
result = []
for t in threading_list:
t.join()
result.append(t.get_result()[:, :, None])
label = np.concatenate(result, axis=2)
label = np.argmax(label, axis=2).astype(np.uint8)
cv.imwrite('./outputs/image_' + str(image_n) + "_predict.png", label)
mask = label_resize_vis(label, source_image)
cv.imwrite('./outputs/vis_image_' + str(image_n) + "_predict.jpg", mask[..., ::-1])