为什么要在检测文本之前重塑 MSER 轮廓？

Question

我正在使用来自 opencv-python 的 MSER 来检测使用此 Whosebug question 中的代码的文本。谁能帮我理解为什么在计算对象的凸包之前将轮廓 p 重塑为 (-1, 1, 2)？

代码如下：

import cv2
import numpy as np

#Create MSER object
mser = cv2.MSER_create()

#Your image path i-e receipt path
img = cv2.imread('/home/rafiullah/PycharmProjects/python-ocr-master/receipts/73.jpg')

#Convert to gray scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

vis = img.copy()

#detect regions in gray scale image
regions, _ = mser.detectRegions(gray)

hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]

cv2.polylines(vis, hulls, 1, (0, 255, 0))

cv2.imshow('img', vis)

cv2.waitKey(0)

mask = np.zeros((img.shape[0], img.shape[1], 1), dtype=np.uint8)

for contour in hulls:

    cv2.drawContours(mask, [contour], -1, (255, 255, 255), -1)

#this is used to find only text regions, remaining are ignored
text_only = cv2.bitwise_and(img, img, mask=mask)

cv2.imshow("text only", text_only)

cv2.waitKey(0)

Answer 1

整形不整形无所谓

整形是不必要的。 cv2.convexHull() 可以采用任一输入格式。下图显示 regions 中的 contours 整形与否，结果都是一样的。

hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
hulls1 = [cv2.convexHull(p) for p in regions]

这是 p 轮廓在重塑后的变化：

>>> p
array([[305, 382],
       [306, 382],
       [308, 380],
       [309, 380]...


>>> p.reshape(-1, 1, 2)
array([[[305, 382]],    
       [[306, 382]],    
       [[308, 380]],    
       [[309, 380]]...

为什么要在检测文本之前重塑 MSER 轮廓？

Why to reshape MSER contours before detecting texts?

python

opencv

reshape

mser

text-classification