使用 OpenCV 从图像中检测和提取签名

Question

我正在导入附加的图像。导入图像后，我想删除水平线，检测签名然后提取它，在签名周围创建矩形，裁剪矩形并保存。

我正在努力将签名的整个区域识别为一个轮廓或一组轮廓。

我已经尝试过findcontour各种方法来检测签名区域。请参考下面的代码。

Python脚本：

imagePath

#read image
image = cv2.imread(imagePath,cv2.COLOR_BGR2RGB)

#Convert to greyscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # grayscale

#Apply threshold
ret,thresh1 = cv2.threshold(gray, 0, 255,cv2.THRESH_OTSU|cv2.THRESH_BINARY_INV)

plt.imshow(thresh1,cmap = 'gray')


#preprocessing
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,15))
dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)
plt.imshow(dilation,cmap = 'gray')


#Detect contours
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours[0]

height, width, _ = image.shape
min_x, min_y = width, height
max_x = max_y = 0   
for contour, hier in zip(contours, hierarchy):
    (x,y,w,h) = cv2.boundingRect(contour)
    min_x, max_x = min(x, min_x), max(x+w, max_x)
    min_y, max_y = min(y, min_y), max(y+h, max_y)
    if w > 80 and h > 80:
        cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)

if max_x - min_x > 0 and max_y - min_y > 0:
    fin=cv2.rectangle(image, (min_x, min_y), (max_x, max_y), (255, 0, 0), 2)



plt.imshow(fin)



final=cv2.drawContours(image, contours,-1,(0,0,255),6)

plt.imshow(final,cmap = 'gray')

最终目标是在整个签名周围创建矩形

试图概括其他图像：

Answer 1

与移除水平线不同，执行颜色阈值处理可能更容易。这个想法是将签名隔离到掩码上，然后提取它。我们将图像转换为 HSV 格式，然后使用下/上颜色阈值

lower = np.array([90, 38, 0])
upper = np.array([145, 255, 255])
mask = cv2.inRange(image, lower, upper)

面具

为了检测签名，我们可以获取所有轮廓的边界框，然后使用 Numpy 找到左上角和右下角坐标

boxes = []
for c in cnts:
    (x, y, w, h) = cv2.boundingRect(c)
    boxes.append([x,y, x+w,y+h])

boxes = np.asarray(boxes)
left = np.min(boxes[:,0])
top = np.min(boxes[:,1])
right = np.max(boxes[:,2])
bottom = np.max(boxes[:,3])

现在我们有了边界框坐标，我们可以使用Numpy 切片来裁剪ROI

ROI = result[top:bottom, left:right].copy()

import numpy as np
import cv2

image = cv2.imread('new.png')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([90, 38, 0])
upper = np.array([145, 255, 255])
mask = cv2.inRange(image, lower, upper)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)

cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

boxes = []
for c in cnts:
    (x, y, w, h) = cv2.boundingRect(c)
    boxes.append([x,y, x+w,y+h])

boxes = np.asarray(boxes)
left = np.min(boxes[:,0])
top = np.min(boxes[:,1])
right = np.max(boxes[:,2])
bottom = np.max(boxes[:,3])

result[close==0] = (255,255,255)
ROI = result[top:bottom, left:right].copy()
cv2.rectangle(result, (left,top), (right,bottom), (36, 255, 12), 2)

cv2.imshow('result', result)
cv2.imshow('ROI', ROI)
cv2.imshow('close', close)
cv2.imwrite('result.png', result)
cv2.imwrite('ROI.png', ROI)
cv2.waitKey()