new

makeDB.py

@@ -0,0 +1,106 @@
+import sys,os
+import cv2
+import numpy as np 
+import pandas as pd
+from genLabel import loadImg,getImgHW,getFileName,getLabelFileLabels,pathsFiles
+
+def distanceAB(a,b):
+    return np.sqrt(np.sum((a-b)**2))
+
+def calculateRatio(pts,id1,id2,id3,id4):
+    assert(id1<len(pts))
+    assert(id2<len(pts))
+    assert(id3<len(pts))
+    assert(id4<len(pts))
+    return distanceAB(pts[id1], pts[id2])/distanceAB(pts[id3],pts[id4])
+
+def calculateFeature(pts,H,W):
+    for i in range(len(pts)):
+        #print(pts[i][0],pts[i][1])
+        pts[i][0] = pts[i][0]*W
+        pts[i][1] = pts[i][1]*H
+    #print(pts)
+    pts = np.array(pts)
+    #print(pts.shape)
+
+    # print('pts24=',pts[24])
+    # print('pts18=',pts[18])
+    # print(pts[24][0],pts[18][0])
+
+    x = np.min([pts[24][0],pts[18][0]])
+    y = np.min([pts[24][1],pts[18][1]])
+    eyebrowCenter = np.array([x,y]) + np.abs(pts[24]-pts[18])/2
+    #print('eyebrowCenter=',eyebrowCenter)
+
+    #print(distanceAB(3,4))
+    #print(distanceAB(np.array([1,2]),np.array([4,6])))
+    #print(distanceAB(np.array([4,6]),np.array([1,2])))
+    F_Facial_Index = distanceAB(eyebrowCenter, pts[7])/distanceAB(pts[14],pts[0])
+    F_Mandibular_Index = calculateRatio(pts,66,7,4,10)
+
+    F_Intercanthal = calculateRatio(pts,37,45,32,27)
+    F_OrbitalWidth = calculateRatio(pts,27,29,37,45)
+    F_EyeFissure  = calculateRatio(pts,28,30,27,29)
+    F_VermilionHeight = calculateRatio(pts,51,66,66,57)
+    F_MouthFaceWidth = calculateRatio(pts,48,54,0,14)
+    F_Noise1 = calculateRatio(pts,39,46,39,41)
+    F_Noise2 = calculateRatio(pts,39,40,39,41)
+    F_Noise3 = calculateRatio(pts,39,38,39,41)
+    F_Noise4 = calculateRatio(pts,39,38,67,41)
+    F_Noise5 = calculateRatio(pts,39,41,67,41)
+    #print('name:',name,'Features=',F_Facial_Index,F_Mandibular_Index,F_Intercanthal,F_OrbitalWidth,F_EyeFissure,F_VermilionHeight,F_MouthFaceWidth,F_Noise1,F_Noise2,F_Noise3,F_Noise4,F_Noise5)
+
+    return [F_Facial_Index,F_Mandibular_Index,F_Intercanthal,F_OrbitalWidth,F_EyeFissure,F_VermilionHeight,F_MouthFaceWidth,F_Noise1,F_Noise2,F_Noise3,F_Noise4,F_Noise5]
+
+    data = {'F_Facial_Index':F_Facial_Index, 'F_Mandibular_Index':F_Mandibular_Index,
+            'F_Intercanthal':F_Intercanthal,'F_OrbitalWidth':F_OrbitalWidth,'F_EyeFissure':F_EyeFissure,
+            'F_VermilionHeight':F_VermilionHeight,'F_MouthFaceWidth':F_MouthFaceWidth,'F_Noise1':F_Noise1,
+            'F_Noise2':F_Noise2,'F_Noise3':F_Noise3,'F_Noise4':F_Noise4,'F_Noise5':F_Noise5}
+    #df = pd.DataFrame(data=data,index=[0])
+    #print(df)
+    return data
+
+dbFile = r'.\db\facial.csv'
+def makeDb():
+    base = r'.\db\train\\'
+    base = os.path.abspath(base)
+    imgPath = base + r'\images'
+    LabelPath = base + r'\labels'
+
+    df = pd.DataFrame()
+    columns = ['F_Facial_Index', 'F_Mandibular_Index', 'F_Intercanthal',
+       'F_OrbitalWidth', 'F_EyeFissure', 'F_VermilionHeight',
+       'F_MouthFaceWidth', 'F_Noise1', 'F_Noise2', 'F_Noise3', 'F_Noise4',
+       'F_Noise5']
+
+    for i in pathsFiles(imgPath,'jpg'):
+        #print(i)
+        img = loadImg(i)
+        H,W = getImgHW(img)
+        fileName = getFileName(i)
+        fileName = fileName[:fileName.rfind('.')]
+
+        label = LabelPath + '\\' + fileName + '.pts'
+        pts = getLabelFileLabels(label)
+
+        print(fileName,'label=', label,'pts=', len(pts))
+        data = calculateFeature(pts,H,W)
+        data = np.array(data).reshape(-1,len(data))
+        line = pd.DataFrame(data,columns=columns)
+        line.insert(0, "Id", fileName, True)
+
+        df = df.append(line)
+
+    df.set_index(["Id"], inplace=True)
+    print(df.head())
+    print(df.columns)
+    df.to_csv(dbFile)
+
+def getDb():
+    return pd.read_csv(dbFile)
+
+def main():
+    makeDb()
+
+if __name__ == '__main__':
+    main()

afterTraining/predictKeyPoints.py → predictKeyPoints.py

@@ -9,9 +9,6 @@
 from commonModule.ImageBase import *
 from genLabel import writeAnotationFile
 
-def getImg(file):
-    return loadImg(file)
-
 def argCmdParse():
     parser = argparse.ArgumentParser()
     parser.add_argument('-s', '--source', help = 'source image')
@@ -27,6 +24,7 @@ def preditImg(img, modelName = r'./weights/trainFacialRecognition.h5'):
     x = x.reshape((1,x.shape[0],x.shape[1],1))
     print(x.shape)
     pts = model.predict(x)
+    pts = pts.reshape((68,2))
     return pts
 
 def main():
@@ -35,7 +33,7 @@ def main():
     dstFile = arg.dst
     print(file,dstFile)
 
-    img = getImg(file)
+    img = loadImg(file)
     pts = preditImg(img)
     '''
     print(img.shape)
@@ -51,7 +49,6 @@ def main():
     pts = pts.reshape((68,2))
     print('pts=',len(pts),pts.shape,pts)
 
-
     writeAnotationFile(dstFile,pts) #'./res/myface_gray.pts'
 
 if __name__=='__main__':

predictPerson.py

@@ -0,0 +1,52 @@
+import os,sys
+from commonModule.ImageBase import *
+from predictKeyPoints import *
+from makeDB import calculateFeature,getDb,distanceAB
+from testLabel import testFaceLabelPredict,showimage
+
+def argCmdParse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s', '--source', help = 'source image')
+    #parser.add_argument('-d', '--dst', help = 'save iamge')
+    return parser.parse_args()
+
+def predictPerson(feature):
+    df = getDb()
+    print(df.head())
+    #allFeatures = df.iloc[:,1:].values
+    #print('allFeatures.shape=',allFeatures.shape)
+    allDistance=[]
+    allIds = []
+    for i in range(df.shape[0]):
+        id = df.iloc[i,0]
+        i = df.iloc[i,1:].values
+        #print(id,i)
+        allIds.append(id)
+
+        dis = distanceAB(i,feature)
+        #print('dis=',dis)
+        allDistance.append(dis)
+
+    #print('allDistance=',allDistance)
+    disMin = min(allDistance)
+    minIndex = allDistance.index(disMin)
+    print('disMin=',disMin,'minIndex=',minIndex,'id=',allIds[minIndex])
+
+
+def main():
+    arg = argCmdParse()    
+    file = r'./res/001A29.jpg' #arg.source  #
+
+    img = loadImg(file)
+    H,W = getImgHW(img)
+    pts = preditImg(img)
+    #print('pts=',len(pts),pts.shape,pts)
+
+    showimage(testFaceLabelPredict(file))
+
+    feature = calculateFeature(pts,H,W)
+    print('feature=',len(feature),feature)
+    predictPerson(feature)
+
+if __name__=='__main__':
+    main()

testLabel.py

@@ -9,9 +9,9 @@
 sys.path.append(r'.\afterTraining\\')
 
 from genLabel import getLabelFileLabels
-from afterTraining.predictKeyPoints import preditImg
-from afterTraining.commonModule.imagePlot import plotImagList
-from afterTraining.commonModule.ImageBase import changeBgr2Rbg
+from predictKeyPoints import preditImg
+from commonModule.imagePlot import plotImagList
+from commonModule.ImageBase import changeBgr2Rbg
 
 def argCmdParse():
     parser = argparse.ArgumentParser()
@@ -34,11 +34,10 @@ def showimage(img,str='image',autoSize=False):
 def writeImg(img,filePath):
     cv2.imwrite(filePath,img)
 
-def drawPointIndexImg(img, i,pt):
+def drawPointIndexImg(img, i,pt,color = (255, 0, 0),fontsize=0.6):
     font = cv2.FONT_HERSHEY_SIMPLEX 
     org =(int(pt[0]), int(pt[1]))
-    fontScale = 1
-    color = (255, 0, 0) 
+    fontScale = fontsize
     thickness = 1
     return cv2.putText(img, str(i), org, font, fontScale, color, thickness, cv2.LINE_AA) 
 
@@ -49,8 +48,14 @@ def drawPointImgFromPts(img,pts,color):
     for i,pt in enumerate(pts):
         img = drawPointImg(img,pt,color=color)
 
-        # if i%2:
-        #     img = drawPointIndexImg(img,i,pt)
+        #img = drawPointIndexImg(img,i,pt,fontsize=0.5)
+        if 1:
+            if i<27:
+                img = drawPointIndexImg(img,i,pt,fontsize=0.5)
+            elif i<48:
+                img = drawPointIndexImg(img,i-27,pt,color=(0,255,0),fontsize=0.5)
+            else:
+                img = drawPointIndexImg(img,i-47,pt,color=(0,255,255),fontsize=0.5)
     return img
 
 def loadImg(file,mode=cv2.IMREAD_COLOR):
@@ -76,11 +81,11 @@ def getLabelFileLabels(fileLabel):
 
 def testFaceLabelPredict(file):
     img = loadImg(file)
-    pts = preditImg(img,modelName=r'.\afterTraining\weights\trainFacialRecognition.h5')
+    pts = preditImg(img)
     #print(pts)
-    pts = pts.reshape((68,2))
+    #pts = pts.reshape((68,2))
     return testFaceLabelPts(img,pts)
-              
+
 def testFaceLabel(file,label,color=(0,0,255),locCod=False): #locCod coordinats or ratio
     img = loadImg(file)
     return testFaceLabelImg(img,label,color=color,locCod=locCod)
@@ -122,7 +127,7 @@ def testFromDrawpt():
 
     predictImg = changeBgr2Rbg(predictImg)
     trueImg = changeBgr2Rbg(trueImg)
-    c1Img = changeBgr2Rbg(c1Img)
+    c1Img = changeBgr2Rbg(c1Img) #true and predict overlap
 
     imgList,nameList = [],[]
     imgList.append(predictImg),nameList.append('Predict')
@@ -136,8 +141,22 @@ def testFromDrawpt():
     #showimage(c1Img)
     #showimage(trueImg)
 
+def drawPtsAndNumber():
+    file = r'.\afterTraining\res\\' + '001A29.jpg'
+    labelTrue = r'.\afterTraining\res\\' + '001A29_True2.pts'
+
+    #file = r'.\afterTraining\res\\' + '009A22a.jpg'
+    #labelTrue = r'.\afterTraining\res\\' + '009A22a.pts'
+
+    trueImg = testFaceLabel(file,labelTrue,locCod=False)
+    trueImg = changeBgr2Rbg(trueImg)
+    #showimage(trueImg)
+    imgList,nameList = [],[]
+    imgList.append(trueImg),nameList.append('trueImg')
+    plotImagList(imgList,nameList,showticks=False)
 
 def main():
+    return drawPtsAndNumber()
     return testFromDrawpt()
 
     arg = argCmdParse()