我正在学习 Adrian Rosebrock 关于在 RPi 上识别数字的教程,所以没有 tesseract 或其他任何东西: https://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/
但它不识别小数点,所以我一直在非常努力地尝试创建一个有助于做到这一点的部件。我想我已经接近了,但我不确定我做错了什么。
这是预处理后的图片
这是在尝试识别部分之后发生的事情
如您所见,我在某处做错了。已经尝试在 houghCircles 中调整 param1 和 param2
更多例子:
谁能指导我应该做什么?我真的迷路了
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我正在使用的图片
我正在使用的代码
from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
import numpy
DIGITS_LOOKUP = {
# Old Library
#(1, 1, 1, 0, 1, 1, 1): 0, # same as new 8
(0, 0, 1, 0, 0, 1, 0): 1,
(1, 0, 1, 1, 1, 1, 0): 2,
(1, 0, 1, 1, 0, 1, 1): 3,
(0, 1, 1, 1, 0, 1, 0): 4,
(1, 1, 0, 1, 0, 1, 1): 5,
#(1, 1, 0, 1, 1, 1, 1): 6,
(1, 0, 1, 0, 0, 1, 0): 7,
(1, 1, 1, 1, 1, 1, 1): 8,
(1, 1, 1, 1, 0, 1, 1): 9,
# New Digital Library
(0, 0, 1, 1, 1, 0, 1): 0,
(1, 0, 1, 0, 0, 1, 1): 2,
(0, 0, 1, 1, 0, 1, 1): 4,
(0, 0, 0, 0, 0, 1, 1): 4,
(1, 1, 0, 0, 0, 1, 1): 5,
(1, 1, 0, 1, 1, 0, 1): 5,
(1, 0, 0, 0, 0, 1, 1): 5,
(1, 1, 1, 0, 0, 0, 0): 7,
(1, 1, 0, 1, 1, 1, 1): 8,
(1, 1, 1, 0, 1, 1, 1): 8
}
image = cv2.imread("10.jpg")
image = imutils.resize(image, height=100)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 120, 255, 1)
cv2.imshow("1", edged)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
displayCnt = approx
break
warped = four_point_transform(gray, displayCnt.reshape(4, 2))
output = four_point_transform(image, displayCnt.reshape(4, 2))
thresh = cv2.threshold(warped, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("2", thresh)
print(thresh.shape)
circles = cv2.HoughCircles(warped, cv2.HOUGH_GRADIENT, 7, 14, param1=0.1, param2=20, minRadius=3, maxRadius=7)
# ensure at least some circles were found
if circles is not None:
circles = numpy.round(circles[0, :]).astype("int")
for (x, y, r) in circles:
cv2.circle(output, (x, y), r, (0, 255, 0), 4)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
# show the output image
cv2.imshow("test", output)
cv2.waitKey(0)
由于小数点可能是正方形而不是圆形,使用 cv2.HoughCircles()
可能不是最佳选择。此外,由于您可能有背景噪音,因此尝试查找连接的组件可能会给您误报结果。
下面是使用 cv2.boundingRect()
和 cv2.contourArea()
检测小数点的方法。我们可以设置阈值最小和最大区域,这样它只会检测小数点,同时避免检测噪声。
尝试检测图像
from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
import numpy
DIGITS_LOOKUP = {
(1, 1, 1, 0, 1, 1, 1): 0,
(0, 0, 1, 0, 0, 1, 0): 1,
(1, 0, 1, 1, 1, 1, 0): 2,
(1, 0, 1, 1, 0, 1, 1): 3,
(0, 1, 1, 1, 0, 1, 0): 4,
(1, 1, 0, 1, 0, 1, 1): 5,
(1, 1, 0, 1, 1, 1, 1): 6,
(1, 0, 1, 0, 0, 1, 0): 7,
(1, 1, 1, 1, 1, 1, 1): 8,
(1, 1, 1, 1, 0, 1, 1): 9
}
image = cv2.imread("10.jpg")
image = imutils.resize(image, height=100)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 120, 255, 1)
cv2.imshow("1", edged)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
displayCnt = approx
break
warped = four_point_transform(gray, displayCnt.reshape(4, 2))
thresh = cv2.threshold(warped, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("2", thresh)
digit_cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
digit_cnts = imutils.grab_contours(digit_cnts)
threshold_max_area = 25
threshold_min_area = 5
contour_image = thresh.copy()
for c in digit_cnts:
(x,y,w,h) = cv2.boundingRect(c)
area = cv2.contourArea(c)
if area < threshold_max_area and area > threshold_min_area:
cv2.drawContours(contour_image,[c], 0, (100,5,10), 3)
cv2.imshow("detect decimal", contour_image)
cv2.waitKey(0)
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