Files
@ 4d9660f111e4
Branch filter:
Location: OneEye/exp/stone_detect.py - annotation
4d9660f111e4
4.6 KiB
text/x-python
grid: refactored out transformation matrix construction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | 8be76da66456 8be76da66456 8be76da66456 855e8825c380 8be76da66456 92f4748d07b3 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 8be76da66456 8be76da66456 92f4748d07b3 92f4748d07b3 8be76da66456 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 8be76da66456 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 92f4748d07b3 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 8be76da66456 855e8825c380 8be76da66456 8be76da66456 855e8825c380 855e8825c380 855e8825c380 855e8825c380 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 8be76da66456 8be76da66456 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 90d22d070710 92f4748d07b3 92f4748d07b3 92f4748d07b3 90d22d070710 90d22d070710 90d22d070710 8be76da66456 8be76da66456 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 92f4748d07b3 855e8825c380 92f4748d07b3 92f4748d07b3 855e8825c380 92f4748d07b3 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 855e8825c380 8be76da66456 8be76da66456 8be76da66456 8be76da66456 92f4748d07b3 8be76da66456 8be76da66456 90d22d070710 92f4748d07b3 92f4748d07b3 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 92f4748d07b3 90d22d070710 90d22d070710 90d22d070710 90d22d070710 92f4748d07b3 90d22d070710 90d22d070710 90d22d070710 90d22d070710 90d22d070710 92f4748d07b3 92f4748d07b3 92f4748d07b3 92f4748d07b3 8be76da66456 | import sys
sys.path.append("../src")
import os
import math
import random
import cv2 as cv
import numpy as np
import scipy.cluster
import scipy.ndimage
from annotations import DataFile,computeBoundingBox
from hough import show
from analyzer.epoint import EPoint
random.seed(361)
class Line():
def __init__(self,a,b):
self.a=a
self.b=b
self.points={a,b}
def getSortedPoints(self):
return tuple(sorted(self.points))
def kmeans(img):
arr=np.reshape(img,(-1,3)).astype(np.float)
colors=np.array([[0,0,0],[255,255,255],[193,165,116]],np.float)
print(colors)
(centers,distortion)=scipy.cluster.vq.kmeans(arr,colors)
print("k-means centers:",centers)
return centers
def quantize(img,centers):
origShape=img.shape
data=np.reshape(img,(-1,3))
(keys,dists)=scipy.cluster.vq.vq(data,centers)
pixels=np.array([centers[k] for k in keys],dtype=np.uint8).reshape(origShape)
return pixels
def filterStones(contours,bwImg,stoneDims):
contourImg=cv.cvtColor(bwImg,cv.COLOR_GRAY2BGR)
res=[]
for (i,c) in enumerate(contours):
keep=True
moments=cv.moments(c)
center=(moments["m10"]/(moments["m00"] or 1), moments["m01"]/(moments["m00"] or 1))
area=cv.contourArea(c)
(x,y,w,h)=cv.boundingRect(c)
if w>stoneDims[0] or h>stoneDims[1]*1.5 or w<2 or h<2:
cv.drawMarker(contourImg,tuple(map(int,center)),(0,0,255),cv.MARKER_TILTED_CROSS,12)
keep=False
coverage1=area/(w*h or 1)
hull=cv.convexHull(c)
coverage2=area/(cv.contourArea(hull) or 1)
if coverage2<0.8:
cv.drawMarker(contourImg,tuple(map(int,center)),(0,127,255),cv.MARKER_DIAMOND,12)
keep=False
if keep:
res.append((EPoint(*center),c))
cv.drawMarker(contourImg,tuple(map(int,center)),(255,0,0),cv.MARKER_CROSS)
print("accepted:",len(res))
print("rejected:",len(contours)-len(res))
show(contourImg)
return res
def point2lineDistance(a,b,p):
# https://en.wikipedia.org/wiki/Point-line_distance#Line_defined_by_two_points
ab=b-a
num=abs(ab.y*p.x - ab.x*p.y + b.x*a.y - a.x*b.y)
denum=math.sqrt(ab.y**2+ab.x**2)
return num/denum # double_area / side_length == height
def groupLines(points,minCount,tolerance):
random.shuffle(points)
sample=points[:57]
for (i,a) in enumerate(sample):
for (j,b) in enumerate(sample):
if j<=i: continue
ab=Line(a,b)
for c in points:
if c is a or c is b: continue
if point2lineDistance(a,b,c)<=tolerance:
ab.points.add(c)
if len(ab.points)>=minCount:
yield ab
if __name__=="__main__":
filepath=sys.argv[1]
annotations=DataFile(sys.argv[2])
filename=os.path.basename(filepath)
(x1,y1,x2,y2)=computeBoundingBox(annotations[filename][0])
(w,h)=(x2-x1,y2-y1)
img=cv.imread(filepath)
(x3,x4,y3,y4)=(x1+w//4,x1+3*w//4,y1+h//4,y1+3*h//4)
print("x3,x4,y3,y4:",x3,x4,y3,y4)
rect=img[y3:y4,x3:x4,:]
centers=kmeans(rect)
print("x1,x2,y1,y2:",(x1,x2,y1,y2))
img[y1:y2,x1:x2,:]=quantize(img[y1:y2,x1:x2,:],centers)
print("image quantized")
rect=img[y1:y2,x1:x2]
unit=np.array([1,1,1],dtype=np.uint8)
kernel=np.ones((3,3),np.uint8)
maskB=cv.inRange(rect,centers[0]-unit,centers[0]+unit)
maskB=cv.morphologyEx(maskB,cv.MORPH_OPEN,kernel,iterations=1)
maskB=cv.erode(maskB,kernel,iterations=2)
maskW=cv.inRange(rect,centers[1]-unit,centers[1]+unit)
maskW=cv.erode(maskW,kernel,iterations=2)
show(img,filename)
show(maskB,filename)
show(maskW,filename)
stones=cv.bitwise_or(maskB,maskW)
show(stones)
stoneDims=(w/19,h/19)
print("stone dims:",tuple(x/2 for x in stoneDims),"-",stoneDims)
(contours,hierarchy)=cv.findContours(stones,cv.RETR_LIST,cv.CHAIN_APPROX_SIMPLE)
stoneLocs=filterStones(contours,stones,stoneDims)
linesImg=cv.cvtColor(np.zeros((h,w),np.uint8),cv.COLOR_GRAY2BGR)
cv.drawContours(linesImg,[c for (point,c) in stoneLocs],-1,(255,255,255),-1)
for (p,c) in stoneLocs:
cv.drawMarker(linesImg,(int(p.x),int(p.y)),(255,0,0),cv.MARKER_CROSS)
lineDict=dict()
minCount=min(max(math.sqrt(len(stoneLocs))-2,3),7)
print("min count:",minCount)
for line in groupLines([point for (point,contour) in stoneLocs],minCount,2):
key=line.getSortedPoints()
if key in lineDict: # we already have a line with the same incident points
continue
lineDict[line.getSortedPoints()]=line
obsolete=set()
for ab in lineDict.values():
if ab is line: continue
if line.points<ab.points: # == impossible
del lineDict[line.getSortedPoints()]
break
if ab.points<line.points:
obsolete.add(ab.getSortedPoints())
for key in obsolete: del lineDict[key]
for line in sorted(lineDict, key=len, reverse=True)[:16]:
print(len(line),line)
(xa,ya)=line[0]
(xb,yb)=line[-1]
cv.line(linesImg,(int(xa),int(ya)),((int(xb),int(yb))),(255,255,0),1)
show(linesImg)
|