在OpenCV(或skimage)中使用投影轮廓反求方法后,将旋转图像的背景更改为白色而不是黑色
我在我的二进制图像上使用了在OpenCV(或skimage)中使用投影轮廓反求方法后,将旋转图像的背景更改为白色而不是黑色,opencv,image-processing,python-imaging-library,scikit-image,opencv-python,Opencv,Image Processing,Python Imaging Library,Scikit Image,Opencv Python,我在我的二进制图像上使用了投影配置文件方法来获得deskew版本。一切都很好,但旋转后的图像有黑色区域,其中应用了倾斜如何将该区域转换为白色而不是黑色。下面是投影剖面的代码 def correct_skew(image, delta=1, limit=5): """ image : input delta : sampling in the -limit,limit + delta range limit : range o
投影配置文件
方法来获得deskew版本。一切都很好,但旋转后的图像有黑色区域,其中应用了倾斜如何将该区域转换为白色而不是黑色。下面是投影剖面的代码
def correct_skew(image, delta=1, limit=5):
"""
image : input
delta : sampling in the -limit,limit + delta range
limit : range of angles to explore
"""
# Function that returns the score of histogram for the given angle at which we check
def determine_score(arr, angle):
"""
arr : binarized image
angle : angle at which we calcuate the score
"""
data = inter.rotate(arr, angle, reshape=False, order=0)
histogram = np.sum(data, axis=1)
score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
return histogram, score
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
scores = []
angles = np.arange(-limit, limit + delta, delta)
for angle in angles:
histogram, score = determine_score(thresh, angle)
scores.append(score)
best_angle = angles[scores.index(max(scores))]
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC)
return best_angle, rotated
这是桌面扫描后的图像:
原始二进制图像:
cv2.warpaffine文档说明该函数采用可选参数,即
borderValue
。默认情况下,此值为(0,0,0)
,您可以通过调用warpaffine例程来更改此值,如下所示:
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode = cv2.BORDER_CONSTANT, borderValue=np.array([255, 255, 255]))
谢谢你,巴德!工作顺利。