我正在使用Python编写OCR应用程序来识别数字。我使用OpenCV在图像上查找轮廓、裁剪图像,然后将图像预处理为28x28以适应MNIST数据集。我的图像不是正方形的,因此当我调整图像大小时,似乎会失去很多质量。有什么技巧或建议可以尝试吗?
这是原始图像
这是编辑后的图像
这是应该有的质量
我已经尝试了一些来自http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html的技巧,如膨胀和开运算。但它并没有让它更好,只是让它变得模糊...
这是我正在使用的代码(查找轮廓,裁剪它,调整大小,然后进行阈值处理,最后将其居中)。
这是我正在使用的代码(查找轮廓,裁剪它,调整大小,然后进行阈值处理,最后将其居中)。
import numpy as np
import cv2
import imutils
import scipy
from imutils.perspective import four_point_transform
from scipy import ndimage
images = np.zeros((4, 784))
correct_vals = np.zeros((4, 10))
i = 0
def getBestShift(img):
cy, cx = ndimage.measurements.center_of_mass(img)
rows, cols = img.shape
shiftx = np.round(cols / 2.0 - cx).astype(int)
shifty = np.round(rows / 2.0 - cy).astype(int)
return shiftx, shifty
def shift(img, sx, sy):
rows, cols = img.shape
M = np.float32([[1, 0, sx], [0, 1, sy]])
shifted = cv2.warpAffine(img, M, (cols, rows))
return shifted
for no in [1, 3, 4, 5]:
image = cv2.imread("images/" + str(no) + ".jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 50, 200, 255)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if the contour has four vertices, then we have found
# the thermostat display
if len(approx) == 4:
displayCnt = approx
break
warped = four_point_transform(gray, displayCnt.reshape(4, 2))
gray = cv2.resize(255 - warped, (28, 28))
(thresh, gray) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
while np.sum(gray[0]) == 0:
gray = gray[1:]
while np.sum(gray[:, 0]) == 0:
gray = np.delete(gray, 0, 1)
while np.sum(gray[-1]) == 0:
gray = gray[:-1]
while np.sum(gray[:, -1]) == 0:
gray = np.delete(gray, -1, 1)
rows, cols = gray.shape
if rows > cols:
factor = 20.0 / rows
rows = 20
cols = int(round(cols * factor))
gray = cv2.resize(gray, (cols, rows))
else:
factor = 20.0 / cols
cols = 20
rows = int(round(rows * factor))
gray = cv2.resize(gray, (cols, rows))
colsPadding = (int(np.math.ceil((28 - cols) / 2.0)), int(np.math.floor((28 - cols) / 2.0)))
rowsPadding = (int(np.math.ceil((28 - rows) / 2.0)), int(np.math.floor((28 - rows) / 2.0)))
gray = np.lib.pad(gray, (rowsPadding, colsPadding), 'constant')
shiftx, shifty = getBestShift(gray)
shifted = shift(gray, shiftx, shifty)
gray = shifted
cv2.imwrite("processed/" + str(no) + ".png", gray)
cv2.imshow("imgs", gray)
cv2.waitKey(0)