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standardDetector.py
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standardDetector.py
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import cv2
import numpy as numpy
from operator import itemgetter
import math
from copy import deepcopy
from multiprocessing import Pool, Array, RawArray
import ctypes
import standardDetector
'''
This module implements a function called findStandard which works on cv2 images of 3 colors
findStandard takes one argument (the image) and outputs a projection of the standard.
It will run fairly slowly for large images at 6MP or higher. If the downsample flag is set to true it will downsample to X pixel width of 2000
It WILL fail when there are more than one standard in the picture.
It is fairly robust to even partially covered or irregularly shaped standards to a small extent.
Running this module by itself will find the standard for all jpg files in the same folder and save them to disk in the same folder
'''
HIGH_MEMORY = True #this option enables multiprocessing to speed things up
MIN_DIST = 10 #beyond this dist two squares are not considered to be the same square
MAX_VECTERROR = 0.97 #cos theta > 0.9 where theta is the angle between the vectors
MAX_NORMALIZED_PERIMETER_ERROR = 0.4
MAX_NUMBER_SQUARES_FROM_MEAN = 4
standardColors = numpy.array(
[[[171, 191, 99],
[ 41, 161, 229],
[166, 136, 0],
[ 50, 50, 50]],
[[176, 129, 130],
[ 62, 189, 160],
[150, 84, 188],
[ 85, 84, 83]],
[[ 65, 108, 90],
[105, 59, 91],
[ 22, 200, 238],
[121, 121, 120]],
[[157, 123, 93],
[ 98, 84, 195],
[ 56, 48, 176],
[161, 161, 160]],
[[129, 149, 196],
[168, 92, 72],
[ 72, 149, 71],
[201, 201, 200]],
[[ 67, 81, 115],
[ 45, 123, 220],
[147, 62, 43],
[240, 245, 245]]]).astype(numpy.uint8)
labStandardColors = cv2.cvtColor(standardColors, cv2.COLOR_BGR2LAB).astype(numpy.float32)
labRotated = []
for i in range(0,4):
labRotated.append(numpy.rot90(labStandardColors,-i))
labRotated = numpy.array(labRotated)
class Square:
def __init__(self):
pass
def processContour(self, contour, index, contours, hierarchy, minWidth=30):
self.works = False
#takes in a contour from the image and processes it into a square
#if contour is not a square then it will return False, num where num is the number of contours processed
#furthermore it will set the contour at contours[i] to be False as well.
# else it will replace the contour at contours[i] with a Square
if isinstance(contour,Square):
return contour, 0
if isinstance(contour, bool):
return False, 0
if cv2.contourArea(contour) < 20*20:
contours[index] = False
return False, 1
for eps in range(2,20):
contour = cv2.approxPolyDP(contour, float(eps), True)
contour = cv2.approxPolyDP(contour, float(eps), True)
if not (cv2.isContourConvex(contour) and len(contour) == 4):
contours[index] = False
return False, 1
points = []
dists = []
angles = []
for i, point in enumerate(contour):
points.append(point[0])
for i, point in enumerate(points):
dists.append(numpy.linalg.norm(point- points[i-1]))
a = numpy.arctan2(points[i-1][0]-points[i][0], points[i-1][1]-points[i][1])
while a < 0:
a += numpy.pi
angles.append(a)
#make sure length of each side is roughly the same
#make sure angles of each side is roughly parallel
mean = numpy.mean(dists)
if not ((numpy.abs(angles[2]-angles[0]) < 0.2 or numpy.abs(angles[2]-numpy.pi-angles[0]) < 0.2 or numpy.abs(angles[2]+numpy.pi-angles[0]) < 0.2)
and (numpy.abs(angles[3]-angles[1]) < 0.2 or numpy.abs(angles[3]-numpy.pi-angles[1]) < 0.2 or numpy.abs(angles[3]+numpy.pi-angles[1]) < 0.2)
and abs(dists[0] - (dists[0]+dists[2])/2)
+ abs(dists[2] - (dists[0]+dists[2])/2)
+ abs(dists[1] - (dists[1]+dists[3])/2)
+ abs(dists[3] - (dists[1]+dists[3])/2)< 5.0
and abs((dists[1]+dists[3])/2 - (dists[0]+dists[2])/2) < 30):
contours[index] = False
print dists
return False, 1
parentIndex = hierarchy[0][index][3]
count = 0
self.points = points
self.dists = dists
self.perimeter = numpy.sum(dists)
self.angles = angles
self.contour = deepcopy(contour)
self.color = (255,255,255)
self.labColor = None
self.gridX = 0
self.gridY = 0
self.center = numpy.mean(points, axis=0).astype(int)
self.tupleCenter = (self.center[0], self.center[1])
while parentIndex > 0:
if not isinstance(contours[parentIndex], bool) and not isinstance(contours[parentIndex],Square):
truth, c = Square().processContour(contours[parentIndex], parentIndex, contours, hierarchy, minWidth)
count += c
if isinstance(contours[parentIndex],Square):
contours[index] = False
return False, 1+count
else:
parentIndex = hierarchy[0][parentIndex][3]
#passed all the tests
# now set to square
contours[index] = self
self.works = True
return self, 1+count
def projectAonB(A, B):
dist = numpy.sqrt(B[1]*B[1] + B[0]*B[0])
return (A[1]*B[1] + A[0]*B[0]) / dist
colorYield = numpy.zeros((6,4))
def calcDistance((labColor, shape)):
print labColor
sharedlabimg = numpy.frombuffer(standardDetector.sharedlabimg_base.get_obj(), dtype=numpy.float32)
sharedlabimg = sharedlabimg.reshape(shape)
return numpy.linalg.norm(sharedlabimg-labColor, axis=2)
def initProcess(share):
standardDetector.sharedlabimg_base = share
def findStandard(img, downSample=True):
print 'Downsample'
if downSample:
s = img.shape
img = cv2.resize(img, (int(s[1] * 2560 / s[0]), 2560))
total = numpy.zeros(img.shape).astype(numpy.uint8)
labimg = cv2.cvtColor(img, cv2.COLOR_BGR2LAB).astype(numpy.float32)
thresh = 200
margin = 0.2 #black in between line on the standard is roughly 0.2 of the square width
squares = []
output = numpy.copy(img)
#gen circular mask for houghGrid
a, b = 30,30
rad = 30
y,x = numpy.ogrid[-a:61-a, -b:61-b]
mask = x*x + y*y <= rad*rad
horizontalOffsets = []
verticalOffsets = []
#for calculating orientation
if HIGH_MEMORY:
size = labimg.size
print labimg.size, labimg.shape
sharedlabimg_base = Array(ctypes.c_float, size)
p = Pool(initializer=initProcess,initargs=(sharedlabimg_base,))
sharedlabimg = numpy.frombuffer(sharedlabimg_base.get_obj(), dtype=numpy.float32)
sharedlabimg = sharedlabimg.reshape(labimg.shape)
print labimg.size, labimg.shape
print sharedlabimg.size, sharedlabimg.shape
sharedlabimg[:,:,:]=labimg[:,:,:]
'''
#test to see what sharedlabimg looks like after that
sharedlabimg = numpy.frombuffer(sharedlabimg_base.get_obj(), dtype=numpy.float32)
sharedlabimg = sharedlabimg.reshape(labimg.shape)
cv2.imshow('test2', labimg[::6,::6])
cv2.imshow('test', sharedlabimg[::6,::6])
if cv2.waitKey(0) == ord('q'):
quit()
'''
nMap = p.map(calcDistance, [(c,labimg.shape) for c in labStandardColors.reshape((labStandardColors.shape[0]*labStandardColors.shape[1],labStandardColors.shape[2]))])
#print 'Find squares of each color'
for r, row in enumerate(standardColors):
for c, color in enumerate(row):
labColor = labStandardColors[r][c]
print 'Calculating color ', labColor
#print 'Calculate lab distance'
if not HIGH_MEMORY:
n = numpy.linalg.norm(labimg-labColor, axis=2)
else:
n = nMap[r*len(row)+c]
n = n * 255 / n.max()
n = n.astype(numpy.uint8)
#print 'Threshold'
#n = cv2.adaptiveThreshold(n, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, int(n.shape[1]*0.02) | 1, 6)
ret, n = cv2.threshold(n, 50, 255, cv2.THRESH_BINARY_INV)
#print 'Morphology'
n = cv2.morphologyEx(n, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT,(3,3)))
#cv2.imshow(str(i*4+c), cv2.resize(n, dsize=(0,0), fx=0.2, fy=0.2))
#print 'Contours'
contours,h = cv2.findContours(n, cv2.RETR_TREE , cv2.CHAIN_APPROX_SIMPLE )
#sometimes findcontours doesn't reutnr numpy arrays
for i, contour in enumerate(contours):
contours[i] = numpy.array(contour)
toDraw = []
indices = []
#print 'Process contours'
for i, contour in enumerate(contours):
s = Square()
s, count = s.processContour(contour, i, contours, h, minWidth=(labimg.shape[1] / 100))
if s:
contours[i] = s
curSquares = []
for square in contours:
if isinstance(square, Square):
square.color = (int(color[0]), int(color[1]), int(color[2]))
curSquares.append(square)
labels = numpy.zeros((img.shape[0], img.shape[1])).astype(numpy.uint8)
means = []
#print 'Calculate LAB'
for i in range(0,len(curSquares)):
cv2.drawContours(labels, [curSquares[i].contour], -1, i+1, -1)
roi = cv2.boundingRect(curSquares[i].contour)
mean = cv2.mean(labimg[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]] , numpy.array(labels[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]] == i+1).astype(numpy.uint8))
curSquares[i].labColor = (mean[0], mean[1], mean[2])
##print curSquares[i].labColor, numpy.linalg.norm(curSquares[i].labColor-labColor)
#cv2.drawContours(total, [curSquares[i].contour], -1, (255,255,255), -1)
means.append((numpy.linalg.norm(curSquares[i].labColor-labColor), curSquares[i]))
means.sort(key=itemgetter(0), reverse=True)
colorYield[r][c] += len(means)
##print r, c, colorYield[r][c]
#print 'Add squares, calculate horizontal offsets and vertical offset'
if len(means) > 0:
for mean in means:
square = mean[1]
#check if there is already a square there
fail = False
for anotherSquare in squares:
if numpy.linalg.norm(anotherSquare.center-square.center) < MIN_DIST:
#too close!
fail = True
##print 'Too close'
break
if fail:
continue
squares.append(square)
points = mean[1].contour
#draw estimated location of colorchecker
t = square.center
horizontalOffset = ((points[1][0] - points[0][0]) / 2 + (points[2][0] - points[3][0]) / 2) * 1.3
verticalOffset = ((points[2][0] - points[1][0]) / 2 + (points[3][0] - points[0][0]) / 2) * 1.3
swap = False
if len(horizontalOffsets) == 0:
swap = abs(horizontalOffset[0]*(1) + horizontalOffset[1]*(0)) < abs(verticalOffset[0]*(1) + verticalOffset[1]*(0))
if swap:
horizontalOffsets.append(verticalOffset)
verticalOffsets.append(horizontalOffset)
horizontalOffset = horizontalOffsets[-1]
verticalOffset = verticalOffsets[-1]
else:
horizontalOffsets.append(horizontalOffset)
verticalOffsets.append(verticalOffset)
if horizontalOffset[0] < 0:
horizontalOffset = -horizontalOffset
horizontalOffsets[-1] = -horizontalOffsets[-1]
if verticalOffset[1] < 0:
verticalOffset = -verticalOffset
verticalOffsets[-1] = -verticalOffsets[-1]
else:
#check to see which one we're closer to,
swap = numpy.abs(horizontalOffset[0]*horizontalOffsets[0][0] + horizontalOffset[1]*horizontalOffsets[0][1]) < numpy.abs(verticalOffset[0]*horizontalOffsets[0][0] + verticalOffset[1]*horizontalOffsets[0][1])
##print horizontalOffset[0]*horizontalOffsets[0][0] + horizontalOffset[1]*horizontalOffsets[0][1], verticalOffset[0]*horizontalOffsets[0][0] + verticalOffset[1]*horizontalOffsets[0][1], horizontalOffsets[0]
if swap:
horizontalOffsets.append(verticalOffset)
verticalOffsets.append(horizontalOffset)
horizontalOffset = horizontalOffsets[-1]
verticalOffset = verticalOffsets[-1]
else:
horizontalOffsets.append(horizontalOffset)
verticalOffsets.append(verticalOffset)
if projectAonB(horizontalOffset, horizontalOffsets[0]) < 0:
horizontalOffset = -horizontalOffset
horizontalOffsets[-1] = -horizontalOffsets[-1]
if projectAonB(verticalOffset, verticalOffsets[0]) < 0:
verticalOffset = -verticalOffset
verticalOffsets[-1] = -verticalOffsets[-1]
#print 'Done'
#calculate estimated location of colorchecker
#print 'Calculating offsets'
horizontalOffsets = numpy.array(horizontalOffsets)
verticalOffsets = numpy.array(verticalOffsets)
h, v = numpy.mean(horizontalOffsets, axis=0), numpy.mean(verticalOffsets, axis=0)
diagonalOffsetDistance = numpy.max(numpy.array([numpy.linalg.norm(h+v), numpy.linalg.norm(v-h)]))
##print h,v,diagonalOffsetDistance
averagePerimeter = numpy.mean(numpy.array([s.perimeter for s in squares]))
averagePosition = numpy.mean(numpy.array([s.center for s in squares]), axis=0)
cv2.circle(total, (int(averagePosition[0]), int(averagePosition[1])), 5, (255,128,255), 5)
meanHO, meanVO = numpy.mean(horizontalOffsets, axis=0), numpy.mean(verticalOffsets, axis=0)
##print len(horizontalOffsets), len(verticalOffsets), len(squares)
a = numpy.array([[horizontalOffsets[count], verticalOffsets[count], squares[count]] for count in range(0,len(squares)) if numpy.dot(horizontalOffsets[count],meanHO) / numpy.linalg.norm(horizontalOffsets[count]) / numpy.linalg.norm(meanHO) > MAX_VECTERROR and numpy.dot(verticalOffsets[count],meanVO) / numpy.linalg.norm(verticalOffsets[count]) / numpy.linalg.norm(meanVO) > MAX_VECTERROR and abs(squares[count].perimeter - averagePerimeter) / averagePerimeter < MAX_NORMALIZED_PERIMETER_ERROR and numpy.linalg.norm(averagePosition - squares[count].center) < MAX_NUMBER_SQUARES_FROM_MEAN * diagonalOffsetDistance])
if len(a) > 0:
horizontalOffsets = a[:,0]
verticalOffsets = a[:,1]
squares = a[:,2]
h, v = numpy.mean(horizontalOffsets, axis=0), numpy.mean(verticalOffsets, axis=0)
##print h, v
hx = h[0]
hy = h[1]
vx = v[0]
vy = v[1]
basis = numpy.linalg.inv(numpy.matrix([[hx,vx], [hy,vy]]))
for square in squares:
cv2.circle(total, (square.center[0], square.center[1]), 5, (255,255,255), 5)
cv2.drawContours(total, [square.contour], -1, square.color, 5)
#change basis vectors
target = numpy.matrix([[square.center[0]], [square.center[1]]])
out = basis * target
square.gridX = out.item((0,0))
square.gridY = out.item((1,0))
squares =sorted(squares, key=lambda square: square.gridX*square.gridX+square.gridY*square.gridY)
offsetX = sorted(squares, key=lambda square:square.gridX)[0].gridX
offsetY = sorted(squares, key=lambda square:square.gridY)[0].gridY
maxX = 6
maxY = 4
squareDict = {}
topLeftSquare = None
topLeft = 24
topRightSquare = None
topRight = 24
bottomLeftSquare = None
bottomLeft = 24
bottomRightSquare = None
bottomRight = 24
totalGX = 0
totalGY = 0
totalXOff = 0
totalYOff = 0
for square in squares:
##print square.gridX, square.gridY
square.gridX -= offsetX
square.gridY -= offsetY
count = 0
tsquares = None
residuals = 0
bestresiduals = 1000000
besttsquares = None
bestmaxX = 0
bestmaxY = 0
#print 'Find corner squares, residuals and offsets'
#smart finding of maxX and maxY givest best possible chance of finding fit
while count < 4:
minX = numpy.mean([square.gridX for square in squares if square.gridX >= count+0.5 and square.gridX <= count+1.5]) / (count+1)
minY = numpy.mean([square.gridY for square in squares if square.gridY >= count+0.5 and square.gridY <= count+1.5]) / (count+1)
count += 1
if math.isnan(minX) or math.isnan(minY):
continue
maxX = 0
maxY = 0
residuals = 0
tsquares = deepcopy(squares)
for square in tsquares:
tx = square.gridX
ty = square.gridY
residuals += abs(square.gridX/minX-round(square.gridX/minX)) + abs(square.gridY/minY-round(square.gridY/minY))
square.gridX = round(square.gridX/minX)
square.gridY = round(square.gridY/minY)
gridX = int(square.gridX)
gridY = int(square.gridY)
##print tx, ty, minX, minY, gridX, gridY
if int(square.gridX) > maxX:
maxX = int(square.gridX)
totalXOff = tx
if int(square.gridY) > maxY:
maxY = int(square.gridY)
totalYOff = ty
if not gridY in squareDict:
squareDict[gridY] = {}
if not gridX in squareDict[gridY]:
squareDict[gridY][gridX] = square
if 4-gridX + 6-gridY < bottomRight:
bottomRight = 4-gridX + 6-gridY
bottomRightSquare = square
if gridX + gridY < topLeft:
topLeft = gridX + gridY
topLeftSquare = square
if 4-gridX + gridY < topRight:
topRight = 4-gridX + gridY
topRightSquare = square
if gridX + 6-gridY < bottomLeft:
bottomLeft = gridX + 6-gridY
bottomLeftSquare = square
if residuals < bestresiduals and ((maxX < 6 and maxY < 4) or (maxX < 4 and maxY < 6)):
bestresiduals = residuals
besttsquares = tsquares
bestmaxX = maxX
bestmaxY = maxY
##print maxX, maxY, 'max'
#compare to base case
maxX = 0
maxY = 0
residuals = 0
#print 'Find more residuals'
tsquares = deepcopy(squares)
for square in tsquares:
tx = square.gridX
ty = square.gridY
##print tx,ty
residuals += abs(square.gridX-round(square.gridX)) + abs(square.gridY-round(square.gridY))
square.gridX = round(square.gridX)
square.gridY = round(square.gridY)
gridX = int(square.gridX)
gridY = int(square.gridY)
if int(square.gridX) > maxX:
maxX = int(square.gridX)
totalXOff = tx
if int(square.gridY) > maxY:
maxY = int(square.gridY)
totalYOff = ty
if not gridY in squareDict:
squareDict[gridY] = {}
if not gridX in squareDict[gridY]:
squareDict[gridY][gridX] = square
if 4-gridX + 6-gridY < bottomRight:
bottomRight = 4-gridX + 6-gridY
bottomRightSquare = square
if gridX + gridY < topLeft:
topLeft = gridX + gridY
topLeftSquare = square
if 4-gridX + gridY < topRight:
topRight = 4-gridX + gridY
topRightSquare = square
if gridX + 6-gridY < bottomLeft:
bottomLeft = gridX + 6-gridY
bottomLeftSquare = square
if residuals < bestresiduals and ((maxX < 6 and maxY < 4) or (maxX < 4 and maxY < 6)):
bestresiduals = residuals
besttsquares = tsquares
bestmaxX = maxX
bestmaxY = maxY
squares = besttsquares
maxX = bestmaxX
maxY = bestmaxY
#print 'Found final maxX and maxY'
if maxX != 0:
ax = totalXOff / float(maxX)
else:
ax = 1
if maxY != 0:
ay = totalYOff / float(maxY)
else:
ay = 1
recalculatedHorizontalOffset = ax * h
recalculatedVerticalOffset = ay * v
##print recalculatedHorizontalOffset, recalculatedVerticalOffset
#connect them all
for square in squares:
for nsquare in squares:
if abs(nsquare.gridX-square.gridX)+abs(nsquare.gridY-square.gridY) == 1:
cv2.line(total, square.tupleCenter, nsquare.tupleCenter, 255, 5)
#make fake squares
#print 'Make fake squares'
for cy in range(-6,6):
for cx in range(-6, 6):
if cy in squareDict and cx in squareDict[cy]:
pass
else:
if not cy in squareDict:
squareDict[cy] = {}
s = Square()
nearestIndex = sorted([(abs(square.gridX-cx)+abs(square.gridY-cy), i) for i, square in enumerate(squares)], key=itemgetter(0))[0][1]
s.center = ((cx-squares[nearestIndex].gridX)*recalculatedHorizontalOffset+(cy-squares[nearestIndex].gridY)*recalculatedVerticalOffset+squares[nearestIndex].center).astype(int)
if s.center[0] > 0 and s.center[1] > 0 and s.center[0] < labimg.shape[1] and s.center[1] < labimg.shape[0]:
s.labColor = labimg[s.center[1], s.center[0]]
squareDict[cy][cx] = s
cv2.circle(total, (s.center[0], s.center[1]), 5, (255,255,255), 5)
##print cx,cy, 'make'
possibilities = []
#print 'Check possibilities'
for i, rotatedPossible in enumerate(labRotated):
width = rotatedPossible.shape[1]
height = rotatedPossible.shape[0]
tmaxX = maxX
tmaxY = maxY
for y in range(0,height-tmaxY):
for x in range(0,width-tmaxX):
terror = 0
count = 0
for cy in range(0,tmaxY+1):
for cx in range(0,tmaxX+1):
if cy in squareDict and cx in squareDict[cy]:
square = squareDict[cy][cx]
labColor = rotatedPossible[y+cy][x+cx]
terror += numpy.linalg.norm(square.labColor-labColor)
count += 1
##print terror, count, width, height, tmaxX, tmaxY, i, x, y
possibilities.append([1/float(count), terror/float(count), (y,x,i)])
#print 'Find best possibilities'
possibilities.sort(key=itemgetter(0,1))
rotMatrix = numpy.array([[0,-1],[1,0]])
if len(possibilities) > 0:
ans = possibilities[0][2]
col = numpy.array([[0,0],[0,5],[3,5],[3,0]])
regPoints = numpy.matrix(numpy.transpose(col))
##print numpy.linalg.matrix_power(rotMatrix, ans[2])
regPoints = numpy.array(numpy.transpose(numpy.linalg.matrix_power(rotMatrix, ans[2])*regPoints))
regPoints[:,0] -= numpy.min(regPoints[:,0])
regPoints[:,1] -= numpy.min(regPoints[:,1])
##print regPoints
position = squares[0].center
xoff = squares[0].gridX
yoff = squares[0].gridY
for i, regPoint in enumerate(regPoints):
regPoint -= numpy.array([ans[1], ans[0]])
regPoint -= numpy.array([xoff, yoff])
regPoint = ((regPoint[1]*recalculatedVerticalOffset +regPoint[0]*recalculatedHorizontalOffset)+position).astype(int)
color = (int(standardColors[col[i][1],col[i][0]][0]),int(standardColors[col[i][1],col[i][0]][1]),int(standardColors[col[i][1],col[i][0]][2]))
#snap to squares
snap = []
for square in squares:
if numpy.linalg.norm(square.center-regPoint) < MIN_DIST*5:
snap.append(square)
for cy in squareDict:
for cx in squareDict[cy]:
square = squareDict[cy][cx]
if numpy.linalg.norm(square.center-regPoint) < MIN_DIST*5:
snap.append(square)
if len(snap) > 0:
regPoint = sorted(snap, key = lambda square:numpy.linalg.norm(square.center-regPoint))[0].center
regPoints[i] = regPoint
cv2.circle(total, (regPoint[0], regPoint[1]), 20, color ,20)
pt = cv2.getPerspectiveTransform(numpy.array(regPoints).astype(numpy.float32), numpy.array([[50,50], [50,550], [350,550], [350,50]]).astype(numpy.float32))
total = cv2.warpPerspective(img, pt, (400,600))
else:
print 'NO POSSIBILITIES FOUND', width, height, maxX, maxY, possibilities
total = cv2.resize(total, dsize=(0,0), fx=0.2, fy=0.2)
return total
def getRGBFromWarpedImage(img, spotsize=10):
'''
assume points are located at 50,150,250,350 etc
spotsize controls the sample size (square shape)
'''
ret = numpy.zeros((6,4,3))
for y in range(0,6):
for x in range(0,4):
py = int((y+0.5)*100)
px = int((x+0.5)*100)
n = numpy.mean(numpy.mean(img[py-spotsize:py+spotsize,px-spotsize:px+spotsize], axis=0), axis=0)
ret[y][x] = n
return ret
def putRGBOnStandard(img, colors, spotsize=10):
'''
assume points are located at 50,150,250,350 etc
spotsize controls the sample size (square shape)
'''
ret = numpy.copy(img)
for y in range(0,6):
for x in range(0,4):
py = int((y+0.5)*100)
px = int((x+0.5)*100)
ret[py-spotsize:py+spotsize,px-spotsize:px+spotsize] = colors[y][x]
return ret
if __name__ == '__main__':
import os
files = [file for file in os.listdir('.') if file[-4:] == '.jpg']
for file in files:
print file
standard = findStandard(cv2.imread(file), False)
cv2.imwrite(file[:-4]+'standard.jpg', standard)