OpenCV捕捉到網格的角落

Question

我想要一個像素化的圖像，並使其看起來更像一個cad繪圖/藍圖。

這裏是源圖像：

我使用python和OpenCV 2.到目前爲止，我能夠找到使用Harris角檢測一些角落，但我打我的OpenCV的知識限制。

這裏是什麼輸出會是什麼樣子的例子：

主要目標：

1. 90°角
2. 的行是唯一的垂直或水平（源圖像稍微偏斜）

到目前爲止，這裏是一個什麼似乎是工作ISH（蟒蛇）概述：

points = cv2.cornerHarris(grey, blockSize = 2, ksize = 13, k = 0.1) 
i = 0 
while i < len(points): 
    a = points[i].src.copy() 
    weld_targets = [] 

    # Compair i to points > i: 
    for j in range(i + 1, len(points)): 
    b = points[j].src 
    if a.distance(b) < weld_distance: 
     weld_targets.append(j) 

    if len(weld_targets) > 0: 
    for index in reversed(weld_targets): 
     a.add(points[index].src.copy()) 
     del points[index] 
    a.divide(len(weld_targets) + 1) 
    grid_size = 5 
    grid_offset = 5 
    points[i] = TranslationPoint(a.x, a.y, grid_size, grid_offset) 
    else: 
    i += 1 
# Then snapping all the points to a grid: 

給予我的東西，如： （粉色=電網焊接/捕捉後厲聲點，藍色=哈里斯角點） 從這裏我可以通過查看原始（藍色）點之間是否大多是黑色來連接粉紅色點。

想法改進/ openCV函數可以幫助嗎？

UPDATE： 這是工作大多是與任何激光雷達掃描：

SM_KERNEL_SIZE = 5 
SM_KERNEL = np.ones((SM_KERNEL_SIZE, SM_KERNEL_SIZE), np.uint8) 
SOFT_KERNEL = np.asarray([ 
    [0.2, 0.4, 0.6, 0.4, 0.2], 
    [0.4, 0.6, 1.0, 0.6, 0.4], 
    [0.6, 1.0, 1.0, 1.0, 0.6], 
    [0.4, 0.6, 1.0, 0.6, 0.4], 
    [0.2, 0.4, 0.6, 0.4, 0.2], 
]) 
img = cv.erode(img, SMALL_KERNEL, iterations = 2) 
img = cv.dilate(img, SMALL_KERNEL, iterations = 2) 
for x in range(width - 1): 
    for y in range(height - 1): 
    if self.__img[y, x, 0] == 0 and self.__img[y, x, 1] == 0 and self.__img[y, x, 2] == 0: 
     snap_x = round(x/GRID_SIZE) * GRID_SIZE 
     snap_y = round(y/GRID_SIZE) * GRID_SIZE 
     dot_img[snap_y, snap_x] = WALL_FLAG 

# Look a points that form a GRID_SIZE x GRID_Size square removing 
# the point on the smallest line 
dot_img = self.__four_corners(dot_img, show_preview = show_preview) 

# Remove points that have no neighbors (neighbor = distance(other_point) < GRID_SIZE 
# Remove points that have 1 neighbor that is a corner 
# Keep neighbors on a significant line (significant line size >= 4 * GRID_SIZE) 
dot_img = self.__erode(dot_img, show_preview = show_preview) 

# Connect distance(other_point) <= GRID_SIZE 
wall_img = self.__wall_builder(dot_img, show_preview = False) 

return wall_img 

我要看看我們是否能夠開源項目，並把它添加到GitHub上，以便其他可以添加到這個很酷項目！

Answer 1

這裏是我的建議，

我會對此進行篩選。

import matplotlib.cm as cm 
import matplotlib.pyplot as plt 
import cv2 
import numpy as np 

dirName = "data" 
imgName = "cad_draw.jpg" 
imgFilepath = os.path.join(dirName, imgName) 
img = cv2.imread(imgFilepath) 
print(imgName, img.shape) 
numpyImg = np.asarray(img) 
grayscaleImg = cv2.cvtColor(numpyImg, cv2.COLOR_BGR2GRAY) 
sift = cv2.xfeatures2d.SIFT_create() 
kp = sift.detect(grayscaleImg,None) 
img_sift=np.zeros_like(img) 
img_sift=cv2.drawKeypoints(img_sift, kp, img_sift) 
plt.imshow(img_sift, cmap=cm.gray) 

這會給我下面的圖片

平行，我也將使用輸入圖像線段檢測

lsd_params = dict(_refine=cv2.LSD_REFINE_ADV, _scale=0.45,  _sigma_scale=0.5, _quant=2.0, _ang_th=22.5, _log_eps=0, _density_th=0.7, _n_bins=1024) 
print(lsd_params) 
LineSegmentDetector = cv2.createLineSegmentDetector(**lsd_params) 
lines,widths,prec,nfa=LineSegmentDetector.detect(grayscaleImg) 
img_lines = np.zeros_like(img) 
assert(len(lines) == len(widths)) 
print(len(lines)) 
for l,w in zip(lines, widths): 
    cv2.line(img_lines, (l[0][0], l[0][1]),(l[0][2],l[0][3]), (255,255,255),1) 

plt.imshow(img_lines, cmap=cm.gray) 

這會給我下面的圖片

現在我將推理的關鍵點和檢測線段s來製作更長的線段，我想，您可以根據您的具體應用需求來做。我還會帶來像RANSAC這樣的概念，將緊密排列的行集中在一行中等等。