import cv2
import numpy as npdef show_img(img, name="temp"):img = cv2.resize(img, (0, 0), fx=3, fy=3)cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()def show_img_normal(img, name="temp"):img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()# 排序方法
def sort_contours(cnts, method='left-to-right'):reverse = Falsei = 0if method == 'right-to-left' or method == 'bottom-to-top':reverse = Trueif method == 'bottom-to-top' or method == 'top-to-bottom':i = 1boundingBoxes = [cv2.boundingRect(c) for c in cnts](cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))return (cnts, boundingBoxes)# 读取图像
number_o = cv2.imread("./source/e5628b04eb93564c23ed0f58a360a10.png")
# 转灰度图
number_gary = cv2.cvtColor(number_o, cv2.COLOR_BGR2GRAY)
# 二值化操作
_, number_threshold = cv2.threshold(number_gary, 100, 255, cv2.THRESH_BINARY_INV)
# show_img(number_threshold, "number_threshold")
# 轮廓检测,只检测外轮廓,绘制到原图上
contours, hierarchy = cv2.findContours(number_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
number_contours = cv2.drawContours(number_o, contours, -1, (0, 255, 0), 1)
# show_img(number_contours, "number_contours")# 绘制所有轮廓的方形边界
number_boundingRect = number_o.copy()
number_imgs = []
contours = sort_contours(contours)[0]
# contours.sort_cont(key=cv2.INTER_AREA, reverse=True)
for cnt in contours:x, y, w, h = cv2.boundingRect(cnt)cv2.rectangle(number_boundingRect, (x, y), (x + w, y + h), (0, 0, 255), 1)# 显示每一个模板,作为单独图片number_one = number_threshold[y: y + h, x:x + w]number_one = cv2.resize(number_one, (35, 50))number_imgs.append(number_one)# show_img(number_one, "every")# 显示每一个模板,所有.展示外边框信息数据
show_img(number_boundingRect, "number_boundingRect")im_o = cv2.rotate(cv2.imread("./source/data/1.jpg"), cv2.ROTATE_90_COUNTERCLOCKWISE)
im_gary = cv2.cvtColor(im_o, cv2.COLOR_BGR2GRAY)
# show_img_normal(im_gary, "im_gary")# 构建卷积核
kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 2))# 执行礼帽操作,高亮一下有效信息
im_tophat = cv2.morphologyEx(im_gary, cv2.MORPH_TOPHAT, kernel=kernel2, iterations=1)
# show_img_normal(im_tophat, "im_tophat")# 闭操作,将临近相似区域连起来
im_close = cv2.morphologyEx(im_tophat, cv2.MORPH_CLOSE, kernel2, iterations=8)
# show_img_normal(im_close, "im_close")# 二值化,分离信息
_, im_threshold = cv2.threshold(im_close, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# show_img_normal(im_threshold, "im_threshold")# 中值滤波去除噪音点
im_medianBlur = cv2.medianBlur(im_threshold, 9)
# show_img_normal(im_medianBlur, "im_medianBlur")# 再执行闭操作,连起来相似区域
im_close2 = cv2.morphologyEx(im_medianBlur, cv2.MORPH_CLOSE, kernel2, iterations=2)
# show_img_normal(im_close2, "im_close2")# 执行轮廓提取
contours, hierarchy = cv2.findContours(im_close2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)validCnt = []
# 所有的边框边界
im_boundingRect_all = cv2.cvtColor(im_close2.copy(), cv2.COLOR_GRAY2BGR)
im_boundingRect_valid = cv2.cvtColor(im_close2.copy(), cv2.COLOR_GRAY2BGR)# 记录所有的面积数据
area = np.empty((len(contours),))# 遍历一下,填充面积,后面算平均值和标准差,来过滤过小数据
for index, cnt in enumerate(contours):area[index] = cv2.contourArea(cnt)mean = np.mean(area)
std = np.std(area)for cnt in contours:x, y, w, h = cv2.boundingRect(cnt)# 计算面积areaX = cv2.contourArea(cnt)# 计算长宽比aspectRatio = w / h# 绘制长宽比和值cv2.rectangle(im_boundingRect_all, (x, y), (x + w, y + h), (0, 255, 0), 2)cv2.putText(im_boundingRect_all, f"{aspectRatio:.3f}", (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)# 只有指定比例和面积不是过小的 > mean + std的,选出来if (2.8 < aspectRatio < 3.2 or 4.2 < aspectRatio < 5.8 or 8 < aspectRatio < 10) and (areaX > mean - 0 * std):validCnt.append(cnt)cv2.rectangle(im_boundingRect_valid, (x, y), (x + w, y + h), (0, 255, 255), 2)cv2.putText(im_boundingRect_valid, f"{aspectRatio:.3f}", (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 255), 2)# show_img_normal(im_boundingRect_all, "im_boundingRect_all")
# show_img_normal(im_boundingRect_valid, "im_boundingRect_valid")# 将有效区域定义出来ROI区域,进行边缘检测,拆分单个字符
for cnt in validCnt:x, y, w, h = cv2.boundingRect(cnt)# 显示roi区域im_roi = im_gary[y - 15:y + h + 5, x - 15:x + w + 5]# show_img(im_roi)# 二值化_, im_roi_threshold = cv2.threshold(im_roi, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)# show_img(im_roi_threshold, "im_roi_threshold")# 闭操作合并一些边界k = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))im_roi_close = cv2.morphologyEx(im_roi_threshold, cv2.MORPH_CLOSE, k, iterations=2)# show_img(im_roi_close, "im_roi_close")# 轮廓检测im_roi_bound = cv2.cvtColor(im_roi.copy(), cv2.COLOR_GRAY2BGR)im_roi_contours, hierarchy = cv2.findContours(im_roi_close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)im_roi_contours = sort_contours(im_roi_contours)[0]# im_roi_bound = im_roi_close.copy()for cnt_roi in im_roi_contours:xr, yr, wr, hr = cv2.boundingRect(cnt_roi)cv2.rectangle(im_roi_bound, (xr, yr), (xr + wr, yr + hr), (255, 0, 0), 1)im_roi_close_one = cv2.resize(im_roi_close[yr: yr + hr, xr:xr + wr], (35, 50))# 显示切分出来的单个数字# show_img(im_roi_close_one, "im_roi_close_one")# 和10个数字比scores = []for number_img_one in number_imgs:score = cv2.matchTemplate(im_roi_close_one, number_img_one, cv2.TM_CCOEFF_NORMED)scores.append(score)maxIndex = scores.index(max(scores))cv2.putText(im_o, str(maxIndex), (x + xr, y + yr), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 255), 2)cv2.putText(im_roi_bound, str(maxIndex), ( xr, yr + 10), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 255), 1)# 显示切分的轮廓# show_img(im_roi_bound, "im_roi_bound")
show_img_normal(im_o, "im_o")
