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import cv2
import numpy as np
from config.CONFIG_UIED import Config
C = Config()
def read_img(path, resize_height=None, kernel_size=None):
def resize_by_height(org):
w_h_ratio = org.shape[1] / org.shape[0]
resize_w = resize_height * w_h_ratio
re = cv2.resize(org, (int(resize_w), int(resize_height)))
return re
try:
img = cv2.imread(path)
if kernel_size is not None:
img = cv2.medianBlur(img, kernel_size)
if img is None:
print("*** Image does not exist ***")
return None, None
if resize_height is not None:
img = resize_by_height(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img, gray
except Exception as e:
print(e)
print("*** Img Reading Failed ***\n")
return None, None
def gray_to_gradient(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_f = np.copy(img)
img_f = img_f.astype("float")
kernel_h = np.array([[0,0,0], [0,-1.,1.], [0,0,0]])
kernel_v = np.array([[0,0,0], [0,-1.,0], [0,1.,0]])
dst1 = abs(cv2.filter2D(img_f, -1, kernel_h))
dst2 = abs(cv2.filter2D(img_f, -1, kernel_v))
gradient = (dst1 + dst2).astype('uint8')
return gradient
def reverse_binary(bin, show=False):
"""
Reverse the input binary image
"""
r, bin = cv2.threshold(bin, 1, 255, cv2.THRESH_BINARY_INV)
if show:
cv2.imshow('binary_rev', bin)
cv2.waitKey()
return bin
def binarization(org, grad_min, show=False, write_path=None, wait_key=0):
grey = cv2.cvtColor(org, cv2.COLOR_BGR2GRAY)
grad = gray_to_gradient(grey) # get RoI with high gradient
rec, binary = cv2.threshold(grad, grad_min, 255, cv2.THRESH_BINARY) # enhance the RoI
morph = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, (3, 3)) # remove noises
if write_path is not None:
cv2.imwrite(write_path, morph)
if show:
cv2.imshow('binary', morph)
if wait_key is not None:
cv2.waitKey(wait_key)
return morph
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