Shulou(Shulou.com)06/03 Report--

This article mainly explains "how to write a code recognition frame to select a font and divide it into a separate picture". The explanation in the article is simple and clear, and it is easy to learn and understand. let's study and learn how to write a code recognition frame to select a font and divide it into a separate picture.

# juzicode.com/vx: orange code

Import os,sys,time,cv2

Import numpy as np

Dbg_is_show = False

Def show_img (win_name,img,wait_time=0,img_ratio=0.15,is_show=True):

If is_show is not True:

Return

Rows = img.shape [0]

Cols = img.shape [1]

Cv2.namedWindow (win_name, cv2.WINDOW_NORMAL) # cv2.WINDOW_AUTOSIZE)

Cv2.resizeWindow (win_name, (int (cols*img_ratio), int (rows*img_ratio)

Cv2.imshow (win_name,img)

Cv2.waitKey (wait_time)

If not os.path.exists ('out'):

Os.mkdir ('out')

Print ('juzicode.com/vx: orange code')

Print (cv2.__version__)

Img_src = cv2.imread ('src.jpg')

Print (img_src.shape)

Show_img ('img_src',img_src,is_show=dbg_is_show)

# get grayscale image

Img_b, img_g, img_r = cv2.split (img_src)

Show_img ('img_r',img_r,is_show=dbg_is_show)

Img_gray = cv2.bitwise_not (img_r)

Img_gray= cv2.medianBlur (img_gray,5)

# binarization

Thresh_bin,img_bin= cv2.threshold (img_gray,127,255,cv2.THRESH_BINARY)

Show_img ('img_bin',img_bin,is_show=dbg_is_show)

Kernel = cv2.getStructuringElement (cv2.MORPH_RECT, (3,3))

Img_eroded = cv2.erode (img_bin,kernel)

Show_img ('img_eroded',img_eroded)

Kernel = cv2.getStructuringElement (cv2.MORPH_RECT, (29,29))

Img_dilated = cv2.dilate (img_eroded,kernel)

Show_img ('img_dilated',img_dilated)

Res = cv2.findContours (img_dilated,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

Contours=res [1]

Print ('len (contours):', len (contours))

For i in range (0jinlen (contours)):

X, y, w, h = cv2.boundingRect (contours [I])

Print (iPaper len (contours [I]))

If len (contours [I])

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