def main():
img = read_image('2.jpg') / 255
img[np.where(img > 0.6)] = 1
img[np.where(img <= 0.6)] = 0
connected_domains = get_connected_domains_list(img)
for index, domain in enumerate(connected_domains):
write_image('cont' + str(index) + '.jpg', domain * 255)
def main():
img = read_image('1.jpg')
shape = img.shape
# img = cv2.resize(img, (int(shape[1] / 2), int(shape[0] / 2)))
color_extractor = ColorExtractor(img)
res = color_extractor.extract_color()
write_image('2.jpg', res * 255)
def main():
pixel = read_image('txt.jpg') / 255
bbox = BboxGeneratorV2(pixel, is_corrected=True).gen_bboxes()
for box in bbox:
img = read_image('txt.jpg')
area = get_text_area_from_bbox(img, box)
write_image('area.jpg', area)
def main():
original_pixel = cv2.imread('pxl.jpg')[:, :, 0] / 255
bbox_gen = ObliqueBBOXGenerator(original_pixel)
oblique_bbox = bbox_gen.get_oblique_bbox()
img = read_image('imgs/1.jpg')
areas = bbox_gen.get_transparent_text_area(img, oblique_bbox)
for area in areas:
write_image('ar.jpg', area)
def save_image(image_str, i, file_name):
if enlarge_radio == 1:
image_path = TRAINING_DATA_DIR + file_name.replace('.json',
'') + '.jpg'
else:
image_path = TRAINING_DATA_DIR + file_name.replace('.json', '') + \
str(enlarge_radio).replace('.', '_') + '.jpg'
fh = open(image_path, "wb")
fh.write(base64.b64decode(image_str))
fh.close()
img = read_image(image_path)
shape = img.shape
img = cv2.resize(
img, (int(shape[1] * enlarge_radio), int(shape[0] * enlarge_radio)))
write_image(image_path, img)
def main():
# img = to_gray(img)
# mean_value = img.mean()
# print(mean_value)
# paths = get_all_files_under_directory('E:/qianfen_area')
# for path in paths:
# img = read_image(path)
# img = enlighten_image_np(111, img)
# _, name = os.path.split(path)
# write_image('E:/new_area/' + name, img)
""""""
img = read_image('1.jpg')
img = lpls_enlighten(img)
write_image('2.jpg', img)
def main():
txt_and_image_paths = 'D:/label_result_2020_3_10/label_result/箱门中文字识别/txt'
des_line_dir = 'D:/label_result_2020_3_10/label_result/箱门中文字识别/line/'
paths = get_all_file_from_dir(txt_and_image_paths)
try:
for i, p in enumerate(paths):
if '.jpg' not in p:
continue
img_name = os.path.split(p)[1].replace('.jpg', '')
img = read_image(p)
txt_path = p.replace('.jpg', '.txt')
points, is_error = get_points(txt_path)
if is_error:
print(p)
draw_image = get_detect_result(points, img)
write_image(des_line_dir + img_name + '.jpg', draw_image)
except Exception as e:
print(e)
def char_to_image(char):
"""
char
"""
des_dir = combine_file_path('resource/char') + '/'
for font in FONT_OBJECTS:
size = font.getsize(char)
scene_text = Image.new('RGBA', size)
draw = ImageDraw.Draw(scene_text)
draw.text((0, 0), char, font=font)
scene_text = np.array(scene_text)
start_index = 0
for i in range(30):
if scene_text[i, :, :].max() == 0:
start_index = i
else:
break
new_area = scene_text[start_index:, :, :]
write_image(des_dir + char + '_' + get_uuid_str() + '.jpg',
255 - new_area)
def _corrected_bbox(bg, bbox):
"""
corrected bbox
"""
distance1 = get_distance(bbox[0], bbox[1])
distance2 = get_distance(bbox[1], bbox[2])
if distance2 == 0 or distance1 == 0:
return None
if distance1 > distance2:
line1, line2 = [bbox[0], bbox[3]], [bbox[1], bbox[2]]
ver_line1 = approaching_horizontal_point0(bg, line1)
ver_line2 = approaching_horizontal_point2(bg, line2)
hor_line1 = bbox[0], bbox[1]
hor_line2 = bbox[2], bbox[3]
tmp_bg1 = draw_line_from_points(ver_line1, bg, line_pixel=1)
tmp_bg2 = draw_line_from_points(ver_line2, bg, line_pixel=1)
tmp_bg3 = draw_line_from_points(hor_line1, bg, line_pixel=1)
tmp_bg4 = draw_line_from_points(hor_line2, bg, line_pixel=1)
bg = bg + tmp_bg1 + tmp_bg2 + tmp_bg3 + tmp_bg4
write_image('tmp_bg.jpg', bg * 255)
pt1 = cross_point(ver_line1, hor_line1)
pt2 = cross_point(ver_line1, hor_line2)
pt3 = cross_point(ver_line2, hor_line1)
pt4 = cross_point(ver_line2, hor_line2)
else:
line1, line2 = [bbox[0], bbox[1]], [bbox[2], bbox[3]]
hor_line1 = approaching_vertical_point0(bg, line1)
hor_line2 = approaching_vertical_point2(bg, line2)
ver_line1 = bbox[0], bbox[3]
ver_line2 = bbox[1], bbox[2]
pt1 = cross_point(ver_line1, hor_line1)
pt2 = cross_point(ver_line1, hor_line2)
pt3 = cross_point(ver_line2, hor_line1)
pt4 = cross_point(ver_line2, hor_line2)
points = resort_points([pt1, pt2, pt3, pt4])
return points
def __corrected_bbox_second_time(self, bg, bbox):
"""
corrected second time
"""
center_point = (bbox[0][0] + bbox[2][0]) / 2, (bbox[0][1] +
bbox[2][1]) / 2
des_line = np.zeros_like(bg)
bbox[0], bbox[1] = self.get_new_corrected_point(bg,
bbox[0],
bbox[1],
direction='down')
bbox[1], bbox[2] = self.get_new_corrected_point(bg,
bbox[1],
bbox[2],
direction='left')
bbox[2], bbox[3] = self.get_new_corrected_point(bg,
bbox[2],
bbox[3],
direction='up')
bbox[3], bbox[1] = self.get_new_corrected_point(bg,
bbox[3],
bbox[0],
direction='right')
des_line = draw_line_from_points([bbox[0], bbox[1]],
des_line,
line_pixel=1)
des_line = draw_line_from_points([bbox[1], bbox[2]],
des_line,
line_pixel=1)
des_line = draw_line_from_points([bbox[2], bbox[3]],
des_line,
line_pixel=1)
des_line = draw_line_from_points([bbox[3], bbox[0]],
des_line,
line_pixel=1)
write_image('des_line.jpg', des_line * 255)
def approaching_horizontal_point2(bg, line1):
"""
"""
center_point = (line1[0][0] + line1[1][0]) / 2 + 5, (line1[0][1] +
line1[1][1]) / 2
pt1, pt2 = line1
original_pt1, original_pt2 = copy.deepcopy(pt1), copy.deepcopy(pt2)
original_pt1 = original_pt1[0] + 2, original_pt1[1]
original_pt2 = original_pt2[0] + 2, original_pt2[1]
pt1, pt2 = original_pt1, center_point
while True:
pt1, pt2 = translation_line_first_side(pt1, pt2, direction='left')
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
write_image('tmp_bg.jpg', tmp_bg * 255)
max_value = np.max(tmp_bg)
if max_value > 1:
location = np.where(tmp_bg > 1)
first_location, last_location = _sort_location(location)
first_point = first_location
break
pt1, pt2 = center_point, original_pt2
while True:
pt1, pt2 = translation_line_second_side(pt1, pt2, direction='left')
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
write_image('tmp_bg.jpg', tmp_bg * 255)
max_value = np.max(tmp_bg)
if max_value > 1:
location = np.where(tmp_bg > 1)
first_location, last_location = _sort_location(location)
second_point = first_location
break
pt1, pt2 = first_point, second_point
while True:
pt1, pt2 = translation_line(pt1, pt2, direction='right')
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
write_image('tmp_bg.jpg', tmp_bg * 255)
max_value = np.max(tmp_bg)
if max_value <= 1:
return pt1, pt2
def main():
points = [[642, 592], [724, 658], [724, 692], [652, 628]]
img = read_image('imgs/1.jpg')
res = draw_points(points, img)
write_image('imgs/ll.jpg', res)
def main():
mask = read_image('text.jpg') / 255
gap = read_image('gap.jpg') / 255
new_mask = mask * gap * 255
write_image('new_mask.jpg', new_mask)
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： tmp
Description :
Author : 'li'
date： 2020/8/11
-------------------------------------------------
Change Activity:
2020/8/11:
-------------------------------------------------
"""
import numpy as np
import cv2
from llib.cv_utility.image_opt_utility import write_image
file_name = "1.jpg"
img = cv2.imread(file_name)
# gray = np.float32(gray)
# rocg = Recognise.Recognise()
image = np.power(img / 255.0, 0.7)
# image = rocg.gamma_rectify(gray, 0.4)
write_image('33.jpg', image * 255)
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： get_all_file_path
Description :
Author : 'li'
date： 2020/1/18
-------------------------------------------------
Change Activity:
2020/1/18:
-------------------------------------------------
"""
import os
from llib.cv_utility.image_opt_utility import read_image, write_image
from utility.file_path_utility import get_all_files_under_directory
img_dir = 'J:/BaiduNetdiskDownload/0506'
des_path = 'J:/BaiduNetdiskDownload/plate_recognize/img/'
paths = get_all_files_under_directory(img_dir)
for path in paths:
_, name = os.path.split(path)
image = read_image(path)
write_image(des_path + name, image)
def main():
training_data = gen_random_words()
for img, label in training_data:
write_image(label + '.jpg', img)
def get_new_corrected_point(bg, pt1, pt2, direction='down'):
"""
get new corrected points
"""
original_pt1, original_pt2 = copy.deepcopy(pt1), copy.deepcopy(pt2)
is_move_points = False
while True:
last_pt1, last_pt2 = copy.deepcopy(pt1), copy.deepcopy(pt2)
pt1, pt2 = translation_line(pt1, pt2, direction=direction)
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
max_value = np.max(tmp_bg)
if max_value <= 1:
is_move_points = True
else:
break
write_image('tmp_bg.jpg', tmp_bg * 255)
if is_move_points:
pt1, pt2 = last_pt1, last_pt2
original_pt1, original_pt2 = copy.deepcopy(pt1), copy.deepcopy(pt2)
"""first side"""
while True:
last_pt1, last_pt2 = copy.deepcopy(original_pt1), copy.deepcopy(
original_pt2)
pt1, pt2 = translation_line_first_side(pt1,
pt2,
direction=direction)
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
max_value = np.max(tmp_bg)
if max_value <= 1:
is_move_points = True
else:
break
write_image('tmp_bg.jpg', tmp_bg * 255)
final_pt1, final_pt2 = None, None
if is_move_points:
final_pt1 = last_pt1
"""second side"""
while True:
last_pt1, last_pt2 = copy.deepcopy(original_pt1), copy.deepcopy(
original_pt2)
pt1, pt2 = translation_line_second_side(pt1,
pt2,
direction=direction)
tmp_bg = np.zeros_like(bg)
tmp_bg = draw_line_from_points([pt1, pt2], tmp_bg, line_pixel=1)
tmp_bg = tmp_bg + bg
max_value = np.max(tmp_bg)
if max_value <= 1:
is_move_points = True
else:
break
if is_move_points:
final_pt2 = last_pt2
write_image('tmp_bg.jpg', tmp_bg * 255)
if is_move_points:
tmp_bg = draw_line_from_points([final_pt1, final_pt2],
tmp_bg,
line_pixel=1)
tmp_bg = tmp_bg + bg
write_image('tmp_bg.jpg', tmp_bg * 255)
return final_pt1, final_pt2
return original_pt1, original_pt2
