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finemapping.py
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#coding=utf-8
import cv2
import numpy as np
from . import niblack_thresholding as nt
from . import deskew
def fitLine_ransac(pts,zero_add = 0 ):
if len(pts)>=2:
[vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
lefty = int((-x * vy / vx) + y)
righty = int(((136- x) * vy / vx) + y)
return lefty+30+zero_add,righty+30+zero_add
return 0,0
#精定位算法
def findContoursAndDrawBoundingBox(image_rgb):
line_upper = [];
line_lower = [];
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
# for k in np.linspace(-1.5, -0.2,10):
for k in np.linspace(-50, 0, 15):
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
# binary_niblack = gray_image > thresh_niblack
# binary_niblack = binary_niblack.astype(np.uint8) * 255
binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k)
# cv2.imshow("image1",binary_niblack)
# cv2.waitKey(0)
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
bdbox = cv2.boundingRect(contour)
if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
line_upper.append([bdbox[0],bdbox[1]])
line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
line_experiment.append([bdbox[0],bdbox[1]])
line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
# grouped_rects.append(bdbox)
rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
leftyA, rightyA = fitLine_ransac(np.array(line_lower),3)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
image,M = deskew.fastDeskew(image)
return image
#多级
def findContoursAndDrawBoundingBox2(image_rgb):
line_upper = [];
line_lower = [];
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
for k in np.linspace(-1.6, -0.2,10):
# for k in np.linspace(-15, 0, 15):
# #
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
# binary_niblack = gray_image > thresh_niblack
# binary_niblack = binary_niblack.astype(np.uint8) * 255
binary_niblack = nt.niBlackThreshold(gray_image,19,k)
# cv2.imshow("binary_niblack_opencv",binary_niblack_)
# cv2.imshow("binary_niblack_skimage", binary_niblack)
# cv2.waitKey(0)
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
bdbox = cv2.boundingRect(contour)
if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
line_upper.append([bdbox[0],bdbox[1]])
line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
line_experiment.append([bdbox[0],bdbox[1]])
line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
# grouped_rects.append(bdbox)
rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
image,M= deskew.fastDeskew(image)
return image