#coding=utf-8 import numpy as np import cv2 import time # from matplotlib import pyplot as plt import math from scipy.ndimage import filters # # def strokeFiter(): # pass; def angle(x,y): return int(math.atan2(float(y),float(x))*180.0/3.1415) def h_rot(src, angle, scale=1.0): w = src.shape[1] h = src.shape[0] rangle = np.deg2rad(angle) nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0])) rot_mat[0,2] += rot_move[0] rot_mat[1,2] += rot_move[1] return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4) pass def v_rot(img, angel, shape, max_angel): size_o = [shape[1],shape[0]] size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0]) interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0])) pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]]) if(angel>0): pts2 = np.float32([[interval,0],[0,size[1] ],[size[0],0 ],[size[0]-interval,size_o[1]]]) else: pts2 = np.float32([[0,0],[interval,size[1] ],[size[0]-interval,0 ],[size[0],size_o[1]]]) M = cv2.getPerspectiveTransform(pts1,pts2) dst = cv2.warpPerspective(img,M,size) return dst,M def skew_detection(image_gray): h, w = image_gray.shape[:2] eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5) angle_sur = np.zeros(180,np.uint) eigen = eigen.reshape(h, w, 3, 2) flow = eigen[:,:,2] vis = image_gray.copy() vis[:] = (192 + np.uint32(vis)) / 2 d = 12 points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) for x, y in points: vx, vy = np.int32(flow[int(y), int(x)]*d) # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA) ang = angle(vx,vy) angle_sur[(ang+180)%180] +=1 # torr_bin = 30 angle_sur = angle_sur.astype(np.float) angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min()) angle_sur = filters.gaussian_filter1d(angle_sur,5) skew_v_val = angle_sur[20:180-20].max() skew_v = angle_sur[30:180-30].argmax() + 30 skew_h_A = angle_sur[0:30].max() skew_h_B = angle_sur[150:180].max() skew_h = 0 if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3): if skew_h_A>=skew_h_B: skew_h = angle_sur[0:20].argmax() else: skew_h = - angle_sur[160:180].argmax() return skew_h,skew_v def fastDeskew(image): image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) skew_h,skew_v = skew_detection(image_gray) # print("校正角度 h ",skew_h,"v",skew_v) deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60) return deskew,M if __name__ == '__main__': fn = './dataset/0.jpg' img = cv2.imread(fn) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) skew_h,skew_v = skew_detection(gray) img = v_rot(img,(90-skew_v ),img.shape,60) # img = h_rot(img,skew_h) # if img.shape[0]>img.shape[1]: # img = h_rot(img, -90) # plt.show() cv2.imshow("img",img) cv2.waitKey()