基于python和opencv的图像分割旋转裁剪

转自/sinat_36458870/article/details/78825571，有修改

1.代码

# encoding:utf-8import mathimport cv2import numpy as npimport matplotlib.pyplot as pltdef get_image(path): # 获取图片img = cv2.imread(path)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)return img, graydef Gaussian_Blur(gray): # 高斯去噪(去除图像中的噪点)blurred = cv2.GaussianBlur(gray, (9, 9), 0)return blurreddef Sobel_gradient(blurred): # 计算梯度gradX = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=1, dy=0)gradY = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=0, dy=1)gradient = cv2.subtract(gradX, gradY)gradient = cv2.convertScaleAbs(gradient)return gradX, gradY, gradientdef Thresh_and_blur(gradient): # 设定阈值blurred = cv2.GaussianBlur(gradient, (9, 9), 0)(_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)return threshdef image_morphology(thresh): # 图形形态学kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)closed = cv2.erode(closed, None, iterations=4)closed = cv2.dilate(closed, None, iterations=4)return closeddef findcnts_and_box_point(closed): # 计算最大轮廓的旋转包围box(cnts, test) = cv2.findContours(closed.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]rect = cv2.minAreaRect(c)box = np.int0(cv2.boxPoints(rect))print(box)return boxdef drawcnts_and_cut(original_img, box): # 目标图像裁剪draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)Xs = [i[0] for i in box]Ys = [i[1] for i in box]angle = round(-math.atan((Ys[1] - Ys[0]) / (abs(Xs[1] - Xs[0]))) * 360 / (2 * np.pi), 1)print(angle)rows, cols = draw_img.shape[:2]# 计算框中心center = ((Xs[1]+Xs[3]) / 2.0, (Ys[1]+Ys[3]) / 2.0)# 计算框长宽xx = ((Ys[2]-Ys[3])**2 + (Xs[2]-Xs[3])**2)**0.5yy = ((Ys[2]-Ys[1])**2 + (Xs[2]-Xs[1])**2)**0.5M = cv2.getRotationMatrix2D(center, 360 - round(angle, 1), 1)dst = cv2.warpAffine(draw_img, M, (cols, rows))crop_img = cv2.getRectSubPix(dst, (int(xx), int(yy)), center)return draw_img, crop_imgdef work():img_path = r'./01.bmp'save_path = r'./01_save.bmp'# original_img, gray = get_image(img_path)gray = cv2.imread("01.bmp", 0)original_img = gray.copy()blurred = Gaussian_Blur(gray)gradX, gradY, gradient = Sobel_gradient(blurred)thresh = Thresh_and_blur(gradient)closed = image_morphology(thresh)box = findcnts_and_box_point(closed)draw_img, crop_img = drawcnts_and_cut(original_img, box)img_list = [original_img, blurred, gradX, gradY, gradient,thresh, closed,draw_img, crop_img]img_name = ["原图像", "高斯模糊", "x方向梯度", "y方向梯度", "梯度","二值化", "图像形态学", "图片分割", "旋转切割结果"]plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False_, axs = plt.subplots(3, 3, figsize=(12, 12))for i in range(3):for j in range(3):axs[i][j].imshow(img_list[i * 3 + j], cmap='gray')axs[i][j].set_title(img_name[i * 3 + j])axs[i][j].axes.get_xaxis().set_visible(False)axs[i][j].axes.get_yaxis().set_visible(False)plt.show()work()

2.现象