# HG changeset patch
# User Laman
# Date 2019-01-07 00:54:02
# Node ID aeca91e5816715cb16326df423d901e717406fc0
# Parent  6f867d8eac54e90da4ee47e57a9f04d1097c28e1

exp: image color analysis

diff --git a/exp/histogram.py b/exp/histogram.py
new file mode 100644
--- /dev/null
+++ b/exp/histogram.py
@@ -0,0 +1,99 @@
+import os
+import sys
+
+import cv2 as cv
+import numpy as np
+import scipy.cluster
+
+from annotations import DataFile,computeBoundingBox
+
+
+def createHistogram(img):
+	# Convert BGR to HSV
+	hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
+	# H in range(0,180)
+	# S in range(0,256)
+	# V in range(0,256)
+	planes=cv.split(hsv)
+	hhist=cv.calcHist(planes,[0],None,[256],(0,180),accumulate=False)
+	shist=cv.calcHist(planes,[1],None,[256],(0,256),accumulate=False)
+	vhist=cv.calcHist(planes,[2],None,[256],(0,256),accumulate=False)
+
+	width=512
+	height=400
+	binSize=width//256
+
+	histImage = np.zeros((height, width, 3), dtype=np.uint8)
+	cv.normalize(hhist, hhist, alpha=0, beta=height, norm_type=cv.NORM_MINMAX)
+	cv.normalize(shist, shist, alpha=0, beta=height, norm_type=cv.NORM_MINMAX)
+	cv.normalize(vhist, vhist, alpha=0, beta=height, norm_type=cv.NORM_MINMAX)
+
+	for i in range(1, 256):
+		cv.line(histImage, ( binSize*(i-1), height - int(round(hhist[i-1][0])) ),
+						( binSize*(i), height - int(round(hhist[i][0])) ),
+						( 255, 0, 0), thickness=2)
+		cv.line(histImage, ( binSize*(i-1), height - int(round(shist[i-1][0])) ),
+						( binSize*(i), height - int(round(shist[i][0])) ),
+						( 0, 255, 0), thickness=2)
+		cv.line(histImage, ( binSize*(i-1), height - int(round(vhist[i-1][0])) ),
+						( binSize*(i), height - int(round(vhist[i][0])) ),
+						( 0, 0, 255), thickness=2)
+
+	cv.imshow('Source image', img)
+	cv.imshow('calcHist Demo', histImage)
+	cv.waitKey()
+
+
+def quantize(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(centers)
+	return centers
+
+
+def computeClosest(x,centers):
+	res=centers[0]
+	d=np.linalg.norm(res-x)
+	for c in centers:
+		d_=np.linalg.norm(c-x)
+		if d_<d:
+			res=c
+			d=d_
+	return res
+
+
+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)
+rect=img[y3:y4,x3:x4,:]
+centers=quantize(rect)
+
+# colors=[(0,0,0),(255,255,255),(255,200,0)]
+# colorMap=colors[:]
+# for (i,c) in enumerate(centers):
+# 	colorMap[i]=computeClosest(c,colors)
+
+for x in range(x1,x2):
+	for y in range(y1,y2):
+		pix=img[y,x]
+		img[y,x]=computeClosest(pix,centers)
+
+rect=img[y1:y2,x1:x2]
+maskB=cv.inRange(rect,np.array([0,0,0]),np.array([60,60,60]))
+maskB=cv.erode(maskB,np.ones((3,3),np.uint8))
+maskW=cv.inRange(rect,np.array([160,160,160]),np.array([256,256,256]))
+maskW=cv.erode(maskW,np.ones((3,3),np.uint8))
+
+cv.imshow(filename,img)
+cv.waitKey()
+cv.imshow(filename,maskB)
+cv.waitKey()
+cv.imshow(filename,maskW)
+cv.waitKey()