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utils.py
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utils.py
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import os
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import imagehash
class Queue(object):
def __init__(self, size):
self.size = size
self.front = -1
self.rear = -1
self.queue = []
def enqueue(self, ele): # 入队操作
if self.is_full():
# raise exception("queue is full")
pass
else:
self.queue.append(ele)
self.rear = self.rear + 1
def dequeue(self): # 出队操作
if self.is_empty():
# raise exception("queue is empty")
pass
else:
self.queue.pop(0)
self.front = self.front + 1
def is_full(self):
return self.rear - self.front == self.size
def is_empty(self):
return self.front == self.rear
def clear_queue(self):
self.rear = self.front = -1
self.queue = []
def show_queue(self):
print(self.queue)
def show_gray_img(images_dir):
image_list = [x for x in os.listdir(images_dir) if x.endswith(("jpg", "jpeg", "png", "bmp"))]
for image_name in image_list:
image_path = os.path.join(images_dir, image_name)
image = cv2.imread(image_path)
image_convert = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
w = int(image.shape[1] / (image.shape[0] * 1.0 / 32))
image = cv2.resize(image, (w, 32))
image_convert = cv2.resize(image_convert, (w, 32))
cv2.imshow(image_name, image)
cv2.imshow(image_name+"_convert", image_convert)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 在opencv图片中嵌入中文
def cv2_img_add_text(img, text, left, top, text_color=(0, 255, 0), text_size=20):
if isinstance(img, np.ndarray): # 判断是否OpenCV图片类型，若是，需转换为Image
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img)
font_text = ImageFont.truetype("font/SourceHanSerif.otf", text_size, encoding="utf-8")
draw.text((left, top), text, text_color, font=font_text)
# 返回从Image转换得到的cv2图像
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
def get_img_difference(img1, img2, hash_type="perception"):
im1 = Image.fromarray(cv2.cvtColor(cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR), cv2.COLOR_BGR2RGB))
im2 = Image.fromarray(cv2.cvtColor(cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR), cv2.COLOR_BGR2RGB))
if hash_type == "perception":
img_hash = imagehash.phash(im1)
next_hash = imagehash.phash(im2)
elif hash_type == "average":
img_hash = imagehash.average_hash(im1)
next_hash = imagehash.average_hash(im2)
elif hash_type == "wavelet":
img_hash = imagehash.whash(im1)
next_hash = imagehash.whash(im2)
return img_hash - next_hash
# def test_whash():
# for i in range(5536, 5760):
# im1 = Image.open('/home/user/single_test_5/cropped_pic_1_{}/4_canny2_1_{}_0.jpg'.format(i, i))
# im2 = Image.open('/home/user/single_test_5/cropped_pic_1_{}/4_canny2_1_{}_0.jpg'.format(i + 1, i + 1))
#
# img_hash = imagehash.whash(im1)
# next_hash = imagehash.whash(im2)
#
# print(str(i) + "and" + str(i + 1) + ',' + str(img_hash - next_hash))
# if img_hash - next_hash > 10:
# im3 = Image.open('/home/user/single_test_5/cropped_pic_1_{}/4_canny2_1_{}_0.jpg'.format(i - 1, i - 1))
# im4 = Image.open('/home/user/single_test_5/cropped_pic_1_{}/4_canny2_1_{}_0.jpg'.format(i + 2, i + 2))
#
# pre_hash = imagehash.whash(im3)
# after_next_hash = imagehash.whash(im4)
#
# print(str(i - 1) + "and" + str(i + 1) + ',' + str(pre_hash - next_hash))
# print(str(i) + "and" + str(i + 2) + ',' + str(img_hash - after_next_hash))
# if pre_hash - next_hash > 10 and img_hash - after_next_hash > 10:
# print(str(i) + 'and' + str(i + 1) + 'are different')
# 判断两个矩形框是否有重叠部分，并输出重叠部分位置
def get_overlap_coordinate(location_a, location_b):
x = [location_a[0], location_a[2], location_b[0], location_b[2]]
y = [location_a[1], location_a[5], location_b[1], location_b[5]]
overlap_location = []
x.sort()
y.sort()
if y[1] != location_a[5]:
for index, value in enumerate(x):
if value == location_a[0]:
if x[index+1] != location_a[2]:
overlap_location = [y[1], y[2], x[1], x[2]]
elif value == location_b[0]:
if x[index+1] != location_b[2]:
overlap_location = [y[1], y[2], x[1], x[2]]
# if overlap_location:
# print(overlap_location)
# else:
# pass
return overlap_location
def test_get_overlap_coordinate():
get_overlap_coordinate([600, 750, 900, 1050], [650, 700, 950, 1000])
get_overlap_coordinate([600, 700, 950, 1050], [650, 750, 900, 1000])
get_overlap_coordinate([600, 750, 900, 1050], [650, 700, 950, 1000])
get_overlap_coordinate([600, 750, 950, 1050], [650, 700, 900, 1000])
get_overlap_coordinate([600, 750, 900, 1000], [650, 700, 950, 1050])
get_overlap_coordinate([600, 700, 950, 1000], [650, 750, 900, 1050])
get_overlap_coordinate([600, 700, 900, 1050], [650, 750, 950, 1000])
get_overlap_coordinate([600, 650, 900, 950], [700, 750, 1000, 1050])
def get_union_coordinate(location_a, location_b):
x = [location_a[0], location_a[2], location_b[0], location_b[2]]
y = [location_a[1], location_a[5], location_b[1], location_b[5]]
union_location = []
x.sort()
y.sort()
if y[1] != location_a[5]:
for index, value in enumerate(x):
if value == location_a[0]:
if x[index+1] != location_a[2]:
union_location = [y[0], y[3], x[0], x[3]]
elif value == location_b[0]:
if x[index+1] != location_b[2]:
union_location = [y[0], y[3], x[0], x[3]]
return union_location
def draw_ctpn_result_boxes(location_list, img):
return_img = img.copy()
c = (0, 255, 0)
for index in range(len(location_list)):
top = location_list[index][0]
bottom = location_list[index][1]
left = location_list[index][2]
right = location_list[index][3]
cv2.line(return_img, (left, top), (right, top), c, 2)
cv2.line(return_img, (left, top), (left, bottom), c, 2)
cv2.line(return_img, (right, bottom), (right, top), c, 2)
cv2.line(return_img, (left, bottom), (right, bottom), c, 2)
return return_img
if __name__ == '__main__':
# main('/home/user/桌面/gray')
# main('/home/user/PycharmProjects/my_crnn_trainer/my_data_generator/out/train')
pass