def join_parts(body_without_hands,hands) :
body=cv2.imread(body_without_hands)
arms1=cv2.imread(hands)
body_prt=body.copy()
body_prt[body.shape[0]/3:body.shape[0],:,:]=255
cv2.imwrite('body_prt.jpg',body_prt)
prt_cnt=encContour('body_prt.jpg')
leftindex=index_flatside(prt_cnt,"left")
print "bodyleft",prt_cnt[leftindex,0]
rightindex=index_flatside(prt_cnt,"right")
print "bodyright",prt_cnt
blank=np.zeros(arms1.shape,dtype=np.uint8)
blank[:,:,:]=255
arms2=arms1.copy()
#print arms1.shape[1]
arms1[:,arms1.shape[1]/2:arms1.shape[1]]=blank[:,arms1.shape[1]/2:arms1.shape[1]]
arms2[:,0 : arms1.shape[1]/2]=blank[:,0 : arms1.shape[1]/2]
cv2.imwrite('arms1.jpg',arms1)
cv2.imwrite('arms2.jpg',arms2)
armsc1=encContour('arms1.jpg')
armsc2=encContour('arms2.jpg')
arm1index=index_flatside(armsc1,"right")
arm2index=index_flatside(armsc2,"left")
print "left top",armsc1[arm1index[1],0],'\nright top',armsc2[arm2index[1],0],"\nleftbody",prt_cnt[leftindex[1],0],"\nrightbody",prt_cnt[rightindex[1],0]
armsc1[:,0]=armsc1[:,0]-armsc1[arm1index[0],0]+prt_cnt[leftindex[0],0]
armsc2[:,0]=armsc2[:,0]-armsc2[arm2index[0],0]+prt_cnt[rightindex[0],0]
cv2.drawContours(blank,[armsc1,armsc2,encContour(body_without_hands)],-1,(0),1)
plt.imshow(blank)
plt.show()
def giveHeights(ref, height, args):
rext = give_extpoints(encContour(ref))
scaling = float(rext["bottom"][1] - rext["top"][1]) / height
for i in args:
print i
xext = give_extpoints(encContour(i))
print "Height of " + i + " is :", float(xext["bottom"][1] -
xext["top"][1]) / scaling
def side_fit(test, reference):
'''
Under process
Inputs:
test: side view of test image (without head)
reference: side view of reference image (without head)
Output:
test: cropped and resized test image
reference: final reference image having same height as the test image
'''
test_contour = encContour(test)
reference_contour = encContour(reference)
test = (np.zeros((test.shape[0],test.shape[1],3), np.uint8)).copy()
reference = (np.zeros((reference.shape[0],reference.shape[1],3), np.uint8)).copy()
cv2.drawContours(test, test_contour, -1 ,(255,255,255),1)
cv2.drawContours(reference, reference_contour, -1 ,(255,255,255),1)
t_l=tuple(test_contour[test_contour[:,:,0].argmin()][0])
t_r=tuple(test_contour[test_contour[:,:,0].argmax()][0])
t_t=tuple(test_contour[test_contour[:,:,1].argmin()][0])
t_b=tuple(test_contour[test_contour[:,:,1].argmax()][0])
error = (t_b[1] - t_t[1]) / 100
test = test[t_t[1] + error :t_b[1], t_l[0]:t_r[0]]
r_l=tuple(reference_contour[reference_contour[:,:,0].argmin()][0])
r_r=tuple(reference_contour[reference_contour[:,:,0].argmax()][0])
r_t=tuple(reference_contour[reference_contour[:,:,1].argmin()][0])
r_b=tuple(reference_contour[reference_contour[:,:,1].argmax()][0])
error = (r_b[1] - r_t[1]) / 100
reference = reference[r_t[1] + error :r_b[1], r_l[0]:r_r[0]]
height = 700
test = cvt_small(test, height)
reference = cvt_small(reference, height)
'''
Now divide body in 13 parts
Reference : http://www.drawinghowtodraw.com/drawing-lessons/drawing-faces-lessons/cc-figure-drawing-anatomy-caricatures.html
'''
scaling = horizontal_scale(test[0:(t_b[1] - t_t[1])*1/13, 0: r_b[1] - r_t[1]],
reference[0:(t_b[1] - t_t[1])*1/13, 0: r_b[1] - r_t[1]])
reference = cv2.resize(reference,(int(round(reference.shape[1]*scaling)), height))
'''
cv2.imshow('reference',reference)
cv2.imshow('test',test)
cv2.waitKey(0)
'''
return test, reference
def resize_image(imgf,imgs): f=encContour(imgf) s=encContour(imgs) ft=tuple(f[f[:,:,1].argmin()][0]) fb=tuple(f[f[:,:,1].argmax()][0]) st=tuple(s[s[:,:,1].argmin()][0]) sb=tuple(s[s[:,:,1].argmax()][0]) fh=fb[1]-ft[1] sh=sb[1]-st[1] ratio=fh/sh s[:,0,1]=(s[:,0,1]-st[1])*ratio+st[1] cv2.drawContours(imgs,s,0,(255),2) return imgf,imgs
def ratioheight(mfront,mfrontf,mside,rimg) : m=encContour(mfront) mf=encContour(mfrontf) ms=encContour(mside) r=encContour(rimg) mp=give_extpoints(m) mfp=give_extpoints(mf) msp=give_extpoints(ms) rp=give_extpoints(r) heightm=mp['bottom'][1]-mp['top'][1] heightmf=mfp['bottom'][1]-mfp['top'][1] heightms=msp['bottom'][1]-msp['top'][1] heightr=rp['bottom'][1]-rp['top'][1] ratios=[float(heightm)/heightr,float(heightmf)/heightr,float(heightms)/heightr] return ratios
def resize_model(mfront, mfrontf, mside, rimg):
'''
inputs:
mfront: front view of reference model
mfrontf: front view of reference model arms stretched
mside: side view of reference model
rimg: test image ( front or side )
'''
mf = encContour(mfront)
ms = encContour(mside)
mff = encContour(mfrontf)
rimg = encContour(rimg)
et_mf = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mf = tuple(mf[mf[:, :, 1].argmax()][0])
et_ms = tuple(ms[ms[:, :, 1].argmin()][0])
eb_ms = tuple(ms[ms[:, :, 1].argmax()][0])
et_mff = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mff = tuple(mff[mff[:, :, 1].argmax()][0])
et_rimg = tuple(rimg[rimg[:, :, 1].argmin()][0])
eb_rimg = tuple(rimg[rimg[:, :, 1].argmax()][0])
hf = eb_mf[1] - et_mf[1]
hs = eb_ms[1] - et_ms[1]
hff = eb_mff[1] - et_mff[1]
hrimg = eb_rimg[1] - et_rimg[1]
ratios = [float(hf) / hrimg, float(hs) / hrimg, float(hff) / hrimg]
f = cv2.imread(mfront)
s = cv2.imread(mside)
ff = cv2.imread(mfrontf)
#print f.shape,s.shape
#exit(0)
floating1 = float(f.shape[1]) / ratios[0]
floating2 = float(f.shape[0]) / ratios[0]
f1 = cv2.resize(f, (int(floating1), int(floating2)),
interpolation=cv2.INTER_CUBIC)
floating1 = float(s.shape[1]) / ratios[1]
floating2 = float(s.shape[0]) / ratios[1]
s1 = cv2.resize(s, (int(floating1), int(floating2)),
interpolation=cv2.INTER_CUBIC)
floating1 = float(ff.shape[1]) / ratios[0]
floating2 = float(ff.shape[0]) / ratios[0]
ff1 = cv2.resize(ff, (int(floating1), int(floating2)),
interpolation=cv2.INTER_CUBIC)
cv2.imwrite('mfront.jpg', f1)
cv2.imwrite('mside.jpg', s1)
cv2.imwrite('mfrontf.jpg', ff1)
return 'mfront.jpg', 'mside.jpg', 'mfrontf.jpg'
def resize_model(mfront,mfrontf,mside,rimg):
mf=encContour(mfront)
ms=encContour(mside)
mff=encContour(mfrontf)
rimgc=encContour(rimg)
et_mf = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mf = tuple(mf[mf[:, :, 1].argmax()][0])
et_ms = tuple(ms[ms[:, :, 1].argmin()][0])
eb_ms = tuple(ms[ms[:, :, 1].argmax()][0])
et_mff = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mff = tuple(mff[mff[:, :, 1].argmax()][0])
et_rimg = tuple(rimgc[rimgc[:, :, 1].argmin()][0])
eb_rimg = tuple(rimgc[rimgc[:, :, 1].argmax()][0])
hf=eb_mf[1]-et_mf[1]
hs=eb_ms[1]-et_ms[1]
hff=eb_mff[1]-et_mff[1]
hrimg=eb_rimg[1]-et_rimg[1]
ratios=[float(hf)/hrimg,float(hs)/hrimg,float(hff)/hrimg]
f=mfront.copy()
s=(mside.copy())
ff=(mfrontf.copy())
#print f.shape,s.shape
#exit(0)
floating1=float(f.shape[1])/ratios[0]
floating2=float(f.shape[0])/ratios[0]
f1=cv2.resize(f,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(s.shape[1])/ratios[1]
floating2=float(s.shape[0])/ratios[1]
s1=cv2.resize(s,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(ff.shape[1])/ratios[2]
floating2=float(ff.shape[0])/ratios[2]
ff1=cv2.resize(ff,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
resized_mfront = f1.copy()
resized_mfrontf = ff1.copy()
resized_mside = s1.copy()
#cv2.imwrite('mfront.jpg',f1)
#cv2.imwrite('mside.jpg',s1)
#cv2.imwrite('mfrontf.jpg',ff1)
ratios=ratioheight(resized_mfront, resized_mfrontf, resized_mside, rimg)
for i in ratios :
if i < 0.93 or i >1.07 :
return resize_model(resized_mfront, resized_mfrontf, resized_mside, rimg)
return resized_mfront , resized_mfrontf , resized_mside
def resize_model(mfront,mfrontf,mside,rimg):
'''
inputs:
mfront: front view of reference model
mfrontf: front view of reference model arms stretched
mside: side view of reference model
rimg: test image ( front or side )
'''
mf=encContour(mfront)
ms=encContour(mside)
mff=encContour(mfrontf)
rimg=encContour(rimg)
et_mf = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mf = tuple(mf[mf[:, :, 1].argmax()][0])
et_ms = tuple(ms[ms[:, :, 1].argmin()][0])
eb_ms = tuple(ms[ms[:, :, 1].argmax()][0])
et_mff = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mff = tuple(mff[mff[:, :, 1].argmax()][0])
et_rimg = tuple(rimg[rimg[:, :, 1].argmin()][0])
eb_rimg = tuple(rimg[rimg[:, :, 1].argmax()][0])
hf=eb_mf[1]-et_mf[1]
hs=eb_ms[1]-et_ms[1]
hff=eb_mff[1]-et_mff[1]
hrimg=eb_rimg[1]-et_rimg[1]
ratios=[float(hf)/hrimg,float(hs)/hrimg,float(hff)/hrimg]
f=cv2.imread(mfront)
s=cv2.imread(mside)
ff=cv2.imread(mfrontf)
#print f.shape,s.shape
#exit(0)
floating1=float(f.shape[1])/ratios[0]
floating2=float(f.shape[0])/ratios[0]
f1=cv2.resize(f,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(s.shape[1])/ratios[1]
floating2=float(s.shape[0])/ratios[1]
s1=cv2.resize(s,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(ff.shape[1])/ratios[0]
floating2=float(ff.shape[0])/ratios[0]
ff1=cv2.resize(ff,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
cv2.imwrite('mfront.jpg',f1)
cv2.imwrite('mside.jpg',s1)
cv2.imwrite('mfrontf.jpg',ff1)
return 'mfront.jpg','mside.jpg','mfrontf.jpg'
def resize_model(mfront,mfrontf,mside,rimg):
mf=encContour(mfront)
ms=encContour(mside)
mff=encContour(mfrontf)
rimgc=encContour(rimg)
et_mf = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mf = tuple(mf[mf[:, :, 1].argmax()][0])
et_ms = tuple(ms[ms[:, :, 1].argmin()][0])
eb_ms = tuple(ms[ms[:, :, 1].argmax()][0])
et_mff = tuple(mf[mf[:, :, 1].argmin()][0])
eb_mff = tuple(mff[mff[:, :, 1].argmax()][0])
et_rimg = tuple(rimgc[rimgc[:, :, 1].argmin()][0])
eb_rimg = tuple(rimgc[rimgc[:, :, 1].argmax()][0])
hf=eb_mf[1]-et_mf[1]
hs=eb_ms[1]-et_ms[1]
hff=eb_mff[1]-et_mff[1]
hrimg=eb_rimg[1]-et_rimg[1]
ratios=[float(hf)/hrimg,float(hs)/hrimg,float(hff)/hrimg]
f=cv2.imread(mfront)
s=cv2.imread(mside)
ff=cv2.imread(mfrontf)
#print f.shape,s.shape
#exit(0)
floating1=float(f.shape[1])/ratios[0]
floating2=float(f.shape[0])/ratios[0]
f1=cv2.resize(f,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(s.shape[1])/ratios[1]
floating2=float(s.shape[0])/ratios[1]
s1=cv2.resize(s,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
floating1=float(ff.shape[1])/ratios[2]
floating2=float(ff.shape[0])/ratios[2]
ff1=cv2.resize(ff,(int(floating1),int(floating2)),interpolation=cv2.INTER_CUBIC)
cv2.imwrite('mfront.jpg',f1)
cv2.imwrite('mside.jpg',s1)
cv2.imwrite('mfrontf.jpg',ff1)
ratios=ratioheight('mfront.jpg','mfrontf.jpg','mside.jpg',rimg)
for i in ratios :
if i < 0.93 or i >1.07 :
return resize_model('mfront.jpg','mfrontf.jpg','mside.jpg',rimg)
return 'mfront.jpg','mside.jpg','mfrontf.jpg'
def join_parts(body_without_hands, hands):
body = cv2.imread(body_without_hands)
arms1 = cv2.imread(hands)
body_prt = body.copy()
body_prt[body.shape[0] / 3:body.shape[0], :, :] = 255
cv2.imwrite('body_prt.jpg', body_prt)
prt_cnt = encContour('body_prt.jpg')
leftindex = index_flatside(prt_cnt, "left")
print "bodyleft", prt_cnt[leftindex, 0]
rightindex = index_flatside(prt_cnt, "right")
print "bodyright", prt_cnt
blank = np.zeros(arms1.shape, dtype=np.uint8)
blank[:, :, :] = 255
arms2 = arms1.copy()
#print arms1.shape[1]
arms1[:, arms1.shape[1] / 2:arms1.shape[1]] = blank[:, arms1.shape[1] /
2:arms1.shape[1]]
arms2[:, 0:arms1.shape[1] / 2] = blank[:, 0:arms1.shape[1] / 2]
cv2.imwrite('arms1.jpg', arms1)
cv2.imwrite('arms2.jpg', arms2)
armsc1 = encContour('arms1.jpg')
armsc2 = encContour('arms2.jpg')
arm1index = index_flatside(armsc1, "right")
arm2index = index_flatside(armsc2, "left")
print "left top", armsc1[arm1index[1], 0], '\nright top', armsc2[
arm2index[1],
0], "\nleftbody", prt_cnt[leftindex[1],
0], "\nrightbody", prt_cnt[rightindex[1], 0]
armsc1[:, 0] = armsc1[:, 0] - armsc1[arm1index[0],
0] + prt_cnt[leftindex[0], 0]
armsc2[:, 0] = armsc2[:, 0] - armsc2[arm2index[0],
0] + prt_cnt[rightindex[0], 0]
cv2.drawContours(
blank, [armsc1, armsc2, encContour(body_without_hands)], -1, (0), 1)
plt.imshow(blank)
plt.show()
def plot3dHuman2 (front,arms,side,points): #Extracting enclosing contours s=encContour(side) f=encContour(front) arms1=cv2.imread(arms) blank=np.zeros(arms1.shape,dtype=np.uint8) blank[:,:,:]=255 arms2=cv2.imread(arms) # imposing blank ( white) image on half right ( and then left ) of the image having both the arms. arms1[:,arms1.shape[1]/2:arms1.shape[1]]=blank[:,arms1.shape[1]/2:arms1.shape[1]] arms2[:,0 : arms1.shape[1]/2]=blank[:,0 : arms1.shape[1]/2] # Now arms1 and arms2 have only the left and the right arm respectively armsc1=encContour(arms1) armsc2=encContour(arms2) extarms1=give_extindices(armsc1)["right"] extarms2=give_extindices(armsc2)["left"] #index_flatside gives the extreme indices on the specified side of the contour with slight deviation # index1 and index2 will store only the coordinates with lower y component ( i.e. topmost point ) index1=index_flatside(armsc1,"right")[0] index2=index_flatside(armsc2,"left")[0] extf=give_extpoints(f) exts=give_extpoints(s) f1=f.copy() s1=s.copy() # removing that extra bracket f1=f1[:,0] s1=s1[:,0] #Refer the file index_flatside for its usage bindex1=index_flatside(f,"left",0.33) bindex2=index_flatside(f,"right",0.33) matplotlib.rcParams['legend.fontsize']=10 fig=plt.figure() ax=fig.gca(projection='3d') # Changing reference (matplotlib places origin in the usual mathamatical way (not at the topmost left )) f1=f1[:]-[(extf["left"][0]+extf["right"][0])/2,extf["bottom"][1]] f1[:,1]=-f1[:,1] s1=s1[:]-[(exts["left"][0]+exts["right"][0])/2,exts["bottom"][1]] s1[:,1]=-s1[:,1] armsc1[:,0,1]=-armsc1[:,0,1] armsc2[:,0,1]=-armsc2[:,0,1] if len(points) != 0 : points[0][1]=-(points[0][1]-extf["bottom"][1]) points[1][1]=-(points[1][1]-extf["bottom"][1]) #Matching the topmost shoulder point ( bringing the arms to the main body ) armsc1[:,0]=armsc1[:,0]-armsc1[index1,0]+f1[bindex1[0]] armsc2[:,0]=armsc2[:,0]-armsc2[index2,0]+f1[bindex2[0]] #Defining the vertical range of arms using the main body (The real image may not be symmetric,one arm may be slightly higher than the other ) armsVrange=[min(f1[bindex1[1]][1],f1[bindex2[1]][1]),max(f1[bindex1[0]][1],f1[bindex2[0]][1])] # again removing that extra bracket for the arms armsc1=armsc1[:,0] armsc2=armsc2[:,0] extarms1=give_extpoints2(armsc1) extarms2=give_extpoints2(armsc2) # To get the lateral position of arms, matching the midpoint of side view at that point with the midpoint of arm temp=retval((f1[bindex1[0],1]+f1[bindex1[1],1])/2,s1) yl,yr=[0,0] if len(temp)==2: yl=(temp[0]+temp[1])/2 temp=retval((f1[bindex2[0],1]+f1[bindex2[1],1])/2,s1) if len(temp)==2 : yr=(temp[0]+temp[1])/2 # X,Y,Z stored the final plotting points in 3d X=[] ;Y=[];Z=[] # armsplotting decides whether to plot both the arms or not ( part plotting of arms or even plotting one arm is not supported ) armsplotting = False if len(points)!=0 : clickrange=[points[0][1],points[0][0]] clickrange=sorted(clickrange,reverse=False) for p in clickrange : if p <armsVrange[1] and p > armsVrange[0] : print " Don't click on arms " exit(1) if clickrange[0]<armsVrange[0] and clickrange[1] > armsVrange[1] : armsplotting=True else : armsplotting =True if armsplotting : #Plotting arms theta=np.linspace(0,2*np.pi,15) iterator=np.linspace(extarms1["right"][0],extarms1["left"][0],50) for i in iterator : x1=retval2(i,armsc1) if len(x1)!=2 : continue x_a=(x1[0]+x1[1])/2 radius = (abs(x1[1]-x1[0]))/2 for th in theta : X.append(i) Y.append(yl+radius*np.cos(th)) Z.append(x_a+radius*np.sin(th)) iterator=np.linspace(extarms2["right"][0],extarms2["left"][0],50) for i in iterator : x1=retval2(i,armsc2) if len(x1)!=2 : continue x_a=(x1[0]+x1[1])/2 radius = abs(x1[1]-x1[0])/2 for th in theta : X.append(i) Y.append(yr+radius*np.cos(th)) Z.append(x_a+radius*np.sin(th)) points=np.array(points) theta=np.linspace(0,2*np.pi,30) if len(points) != 0 : t= float( points[1,1]- points[0,1])/(max(max(f1[:,1]),max(s1[:,1]))*0.01) iterator=np.linspace(points[0,1],points[1,1],abs(t)) else : iterator=np.linspace(0,max(max(f1[:,1]),max(s1[:,1])),100) #For every value in iterator plotting the data points considering ellipses for fl in iterator: x1=retval(fl,f1) y1=retval(fl,s1) if len(y1) !=2 : continue if len(x1)%2 !=0 or len(x1) >5 or len(x1) == 0: continue x_a=(x1[0]+x1[1])/2 y_a=(y1[0]+y1[1])/2 a=(x1[1]-x1[0])/2 b=(y1[1]-y1[0])/2 for temp in x_a+a*np.cos(theta) : X.append(temp) for temp in y_a+b*np.sin(theta) : Y.append(temp) Z.append(fl) if len(x1) ==4 : x_a=(x1[2]+x1[3])/2 y_a=(y1[0]+y1[1])/2 a=(x1[2]-x1[3])/2 b=(y1[1]-y1[0])/2 for temp in x_a+a*np.cos(theta) : X.append(temp) for temp in y_a+b*np.sin(theta) : Y.append(temp) Z.append(fl) fig=plt.figure() ax=fig.add_subplot(111,projection='3d') #Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots ) ax.plot_wireframe(X,Y,Z,rstride=10,cstride=10) # Create cubic bounding box to simulate equal aspect ratio max_range = np.array([max(X)-min(X), max(Y)-min(Y),max(Z)-min(Z)]).max() Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(X)+min(X)) Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(Y)+min(Y)) Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(Z)+min(Z)) for xb, yb, zb in zip(Xb, Yb, Zb): ax.plot([xb], [yb], [zb], 'w') plt.show()
def ret_hscale(rimg, mimg, args):
# getting the enclosing contours of real image and model image
realc = encContour(rimg)
modelc = encContour(mimg)
extreal = give_extpoints(realc)
extmodel = give_extpoints(modelc)
midreal = int(0.25 * extreal["bottom"][1] + 0.75 * extreal["top"][1])
midmodel = int(0.25 * extmodel["bottom"][1] + 0.75 * extmodel["top"][1])
realindex = give_index_ct(realc, midreal + extreal["top"][1])
modelindex = give_index_ct(modelc, midmodel + extmodel["top"][1])
min = realc[realindex[0], 0, 0]
# for real
leftreal = realindex[0]
k = 0
for i in realindex:
k += 1
for i in range(1, len(realindex)):
if min > realc[realindex[i], 0, 0]:
leftreal = realindex[i]
min = realc[realindex[i], 0, 0]
min = modelc[modelindex[0], 0, 0]
# for model
leftmodel = modelindex[0]
k = 0
for i in modelindex:
k += 1
for i in range(1, len(modelindex)):
if min > modelc[modelindex[i], 0, 0]:
leftmodel = modelindex[i]
min = modelc[modelindex[i], 0, 0]
#figuring out the direction on which to move to reach the shoulder
directionreal = '+' if realc[leftreal + 1, 0, 1] + realc[
leftreal + 2, 0, 1] + realc[leftreal + 3, 0, 1] < realc[
leftreal, 0, 1] + realc[leftreal - 1, 0, 1] + realc[leftreal - 2,
0, 1] else '-'
directionmodel = '+' if modelc[leftmodel + 1, 0, 1] + modelc[
leftmodel + 2, 0, 1] + modelc[leftmodel + 3, 0, 1] < modelc[
leftmodel, 0, 1] + modelc[leftmodel - 1, 0,
1] + modelc[leftmodel - 2, 0, 1] else '-'
# Getting the shoulder point for real image
i = leftreal
avr, avm = [0, 0]
a = realc[i, 0]
while (1):
if directionreal == '+':
b = realc[i + 1, 0]
c = realc[i + 2, 0]
d = realc[i + 3, 0]
e = realc[i + 4, 0]
f = realc[i + 5, 0]
avr = [
float(b[0] + c[0] + d[0] + e[0] + f[0]) / 5,
float(b[1] + c[1] + d[1] + e[1] + f[1]) / 5
]
i += 5
else:
b = realc[i - 1, 0]
c = realc[i - 2, 0]
d = realc[i - 3, 0]
e = realc[i - 4, 0]
f = realc[i - 5, 0]
avr = [
float(b[0] + c[0] + d[0] + e[0] + f[0]) / 5,
float(b[1] + c[1] + d[1] + e[1] + f[1]) / 5
]
i -= 5
if avr[0] != a[0] and abs(float(avr[1] - a[1]) / (avr[0] - a[0])) < 1:
realshoulder = a
break
a = avr
# Getting the shoulder point for model image
i = leftmodel
a = modelc[i, 0]
while (1):
if directionreal == '+':
b = modelc[i + 1, 0]
c = modelc[i + 2, 0]
d = modelc[i + 3, 0]
e = modelc[i + 4, 0]
f = modelc[i + 5, 0]
avm = [
float(b[0] + c[0] + d[0] + e[0] + f[0]) / 5,
float(b[1] + c[1] + d[1] + e[1] + f[1]) / 5
]
i += 5
else:
b = modelc[i - 1, 0]
c = modelc[i - 2, 0]
d = modelc[i - 3, 0]
e = modelc[i - 4, 0]
f = modelc[i - 5, 0]
avm = [
float(b[0] + c[0] + d[0] + e[0] + f[0]) / 5,
float(b[1] + c[1] + d[1] + e[1] + f[1]) / 5
]
i -= 5
if avm[0] != a[0] and abs(float(avm[1] - a[1]) / (avm[0] - a[0])) < 1:
modelshoulder = a
break
a = avm
# print realshoulder,modelshoulder
#Calculating the width of model shoulder and real shoulder
a = give_index_ct(realc, realshoulder[1])
print "realshoulder", realc[a[0], 0], realc[a[1], 0]
b = give_index_ct(modelc, modelshoulder[1])
print "modelshoulder", modelc[b[0], 0], modelc[b[1], 0]
if len(a) % 2 != 0 or len(b) % 2 != 0:
print "some error message"
exit(1)
scaling = float(realc[a[1], 0, 0] - realc[a[0], 0, 0]) / (
modelc[b[1], 0, 0] - modelc[b[0], 0, 0])
print scaling
for i in args:
x = cv2.imread(i)
# print x.shape,int(x.shape[1]*scaling)
x1 = cv2.resize(x, (int(float(x.shape[1]) * scaling), x.shape[0]),
interpolation=cv2.INTER_CUBIC)
cv2.imwrite('scaled_' + i, x1)
return scaling
def plot3dHuman(front, side, points):
#Extracting enclosing contours
s = encContour(side)
f = encContour(front)
# Function which gives x values for a height t and modified contour f
def retval(t, f):
x = []
for i in range(0, len(f) - 1):
a = f[:, 1][i] - t
b = f[:, 1][i + 1] - t
if a * b == 0:
if a == 0:
x.append(f[:, 0][i])
else:
x.append(f[:, 0][i + 1])
else:
if a * b < 0:
x.append((f[:, 0][i] + f[:, 0][i + 1]) / 2)
sorted(x)
# At this stage x has the required values but there may be some repeated values
y = []
for i in range(0, len(x) - 1):
if x[i] == x[i + 1]:
continue
y.append(x[i])
if len(x) is not 0:
y.append(x[-1])
return y
# Getting extreme points
fl = tuple(f[f[:, :, 0].argmin()][0])
fr = tuple(f[f[:, :, 0].argmax()][0])
sl = tuple(s[s[:, :, 0].argmin()][0])
sr = tuple(s[s[:, :, 0].argmax()][0])
ft = tuple(f[f[:, :, 1].argmin()][0])
fb = tuple(f[f[:, :, 1].argmax()][0])
st = tuple(s[s[:, :, 1].argmin()][0])
sb = tuple(s[s[:, :, 1].argmax()][0])
#from height import height
#height('man_front.jpg')
#print ft,fb
#print st,sb
# manipulating the contour to set the lowest point to origin and the body in positive direction
f1 = f
f1 = f1[:, 0] - [fr[0] - (fl[0] + fr[0]) / 2, fb[1]]
if len(points) != 0:
points[:, 1] = points[:, 1] - fb[1]
points[:, 1] = -points[:, 1]
f1[:, 1] = -f1[:, 1]
s1 = s
s1 = s1[:, 0] - [sl[0] + (sl[0] + sr[0]) / 2, sb[1]]
s1[:, 1] = -s1[:, 1]
matplotlib.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
s1
#y=np.zeros(len(f1[:,0]),dtype=np.int64)
#ax.plot(f1[:,0],y,f1[:,1],label='front')
#x=np.zeros(len(s1[:,0]),dtype=np.int64)
#ax.plot(x,s1[:,0],s1[:,1],label='side')
ax.set_aspect('equal')
#print retval(800,f1)
#Using 30 data points per height
theta = np.linspace(0, 2 * np.pi, 30)
if len(points) != 0:
t = float(points[1, 1] -
points[0, 1]) / (max(max(f1[:, 1]), max(s1[:, 1])) * 0.01)
iterator = np.linspace(points[0, 1], points[1, 1], t)
else:
iterator = np.linspace(0, max(max(f1[:, 1]), max(s1[:, 1])), 100)
X = []
Y = []
Z = []
#For every value in iterator plotting the data points considering ellipses
for fl in iterator:
x1 = retval(fl, f1)
y1 = retval(fl, s1)
if len(x1) % 2 != 0:
continue
if len(x1) == 6:
x_a = (x1[0] + x1[1]) / 2
y_a = (y1[0] + y1[1]) / 2
x_a1 = (x1[4] + x1[5]) / 2
a = (x1[1] - x1[0]) / 2
a1 = (x1[5] - x1[4]) / 2
b = a
b1 = a1
for temp in (x1[0] + x1[1]) / 2 + a * np.cos(theta):
X.append(temp)
for temp in (y1[0] + y1[1]) / 2 + b * np.sin(theta):
Y.append(temp)
Z.append(fl)
for temp in (x1[4] + x1[5]) / 2 + a1 * np.cos(theta):
X.append(temp)
for temp in (y1[0] + y1[1]) / 2 + b1 * np.sin(theta):
Y.append(temp)
Z.append(fl)
xf = []
if len(x1) == 6:
xf = [x1[2], x1[3]]
elif len(x1) in [2, 4]:
xf = x1
else:
maxdiff = 0
d = 0
index = -1
while d < len(x1):
diff = x1[d + 1] - x1[d]
if diff > maxdiff:
maxdiff = diff
index = d
d += 2
xf = [x1[index], x1[index + 1]]
try:
if x1[index - 1] - x1[index - 2] < 1.20 * (
x1[index + 1] - x1[index]) and x1[index - 1] - x1[
index - 2] > 0.80 * (x1[index + 1] - x1[index]):
xf = x1[index - 2:index + 2]
x1 = xf
elif x1[index + 3] - x1[index + 2] < 1.20 * (
x1[index + 1] - x1[index]) and x1[index + 3] - x1[
index + 2] > 0.80 * (x1[index + 1] - x1[index]):
xf = x1[index:index + 4]
x1 = xf
except IndexError:
pass
# print len(x1),len(y1)
if xf == []:
continue
x_a = (xf[1] + xf[0]) / 2
y_a = (y1[0] + y1[1]) / 2
a = (xf[1] - xf[0]) / 2
b = (y1[1] - y1[0]) / 2
#print y1,b
for temp in x_a + a * np.cos(theta):
X.append(temp)
for temp in y_a + b * np.sin(theta):
Y.append(temp)
Z.append(fl)
if (len(x1) == 4):
a = (x1[3] - x1[2]) / 2
x_a = (x1[3] + x1[2]) / 2
y_a = (y1[0] + y1[1]) / 2
for temp in x_a + a * np.cos(theta):
X.append(temp)
for temp in y_a + b * np.sin(theta):
Y.append(temp)
Z.append(fl)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#print len(X),len(Y),len(Z)
#Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots )
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
# Create cubic bounding box to simulate equal aspect ratio
max_range = np.array([max(X) - min(X),
max(Y) - min(Y),
max(Z) - min(Z)]).max()
Xb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * (max(X) + min(X))
Yb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * (max(Y) + min(Y))
Zb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * (max(Z) + min(Z))
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
plt.show()
def plot3dHuman2(front, arms, side, points):
#Extracting enclosing contours
s = encContour(side)
f = encContour(front)
#armsc=encContour(arms)
# arms has both the arms so encContour would give the bigger contour but we want both the contours. Dividing the image into two and then using encContour for both the images
arms1 = cv2.imread(arms)
blank = np.zeros(arms1.shape, dtype=np.uint8)
blank[:, :, :] = 255
arms2 = cv2.imread(arms)
#print arms1.shape[1]
arms1[:, arms1.shape[1] / 2:arms1.shape[1]] = blank[:, arms1.shape[1] /
2:arms1.shape[1]]
arms2[:, 0:arms1.shape[1] / 2] = blank[:, 0:arms1.shape[1] / 2]
armsc1 = encContour(arms1)
armsc2 = encContour(arms2)
extarms1 = give_extindices(armsc1)["right"]
extarms2 = give_extindices(armsc2)["left"]
index1 = index_flatside(armsc1, "right")[0]
index2 = index_flatside(armsc2, "left")[0]
# Getting extreme points
'''
fl=tuple(f[f[:,:,0].argmin()][0])
fr=tuple(f[f[:,:,0].argmax()][0])
sl=tuple(s[s[:,:,0].argmin()][0])
sr=tuple(s[s[:,:,0].argmax()][0])
ft=tuple(f[f[:,:,1].argmin()][0])
fb=tuple(f[f[:,:,1].argmax()][0])
st=tuple(s[s[:,:,1].argmin()][0])
sb=tuple(s[s[:,:,1].argmax()][0])
'''
extf = give_extpoints(f)
exts = give_extpoints(s)
f1 = f.copy()
s1 = s.copy()
f1 = f1[:, 0]
s1 = s1[:, 0]
bindex1 = index_flatside(f, "left", 0.33)
bindex2 = index_flatside(f, "right", 0.33)
matplotlib.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
# Changing reference
f1 = f1[:] - [(extf["left"][0] + extf["right"][0]) / 2, extf["bottom"][1]]
f1[:, 1] = -f1[:, 1]
s1 = s1[:] - [(exts["left"][0] + exts["right"][0]) / 2, exts["bottom"][1]]
s1[:, 1] = -s1[:, 1]
armsc1[:, 0, 1] = -armsc1[:, 0, 1]
armsc2[:, 0, 1] = -armsc2[:, 0, 1]
if len(points) != 0:
#print points,fb[1]
points[0][1] = -(points[0][1] - extf["bottom"][1])
points[1][1] = -(points[1][1] - extf["bottom"][1])
#print points
#Matching the topmost shoulder point ( bringing the arms to the main body )
armsc1[:, 0] = armsc1[:, 0] - armsc1[index1, 0] + f1[bindex1[0]]
armsc2[:, 0] = armsc2[:, 0] - armsc2[index2, 0] + f1[bindex2[0]]
armsVrange = [
min(f1[bindex1[1]][1], f1[bindex2[1]][1]),
max(f1[bindex1[0]][1], f1[bindex2[0]][1])
]
armsc1 = armsc1[:, 0]
armsc2 = armsc2[:, 0]
extarms1 = give_extpoints2(armsc1)
extarms2 = give_extpoints2(armsc2)
temp = retval((f1[bindex1[0], 1] + f1[bindex1[1], 1]) / 2, s1)
yl, yr = [0, 0]
if len(temp) == 2:
yl = (temp[0] + temp[1]) / 2
temp = retval((f1[bindex2[0], 1] + f1[bindex2[1], 1]) / 2, s1)
if len(temp) == 2:
yr = (temp[0] + temp[1]) / 2
X = []
Y = []
Z = []
armsplotting = False
clickrange = [points[0][1], points[0][0]]
clickrange = sorted(clickrange, reverse=False)
for p in clickrange:
if p < armsVrange[1] and p > armsVrange[0]:
print " Don't click on arms "
exit(1)
if clickrange[0] < armsVrange[0] and clickrange[1] > armsVrange[1]:
armsplotting = True
if armsplotting:
#Plotting arms
theta = np.linspace(0, 2 * np.pi, 15)
iterator = np.linspace(extarms1["right"][0], extarms1["left"][0], 50)
for i in iterator:
x1 = retval2(i, armsc1)
#print x1
if len(x1) != 2:
continue
x_a = (x1[0] + x1[1]) / 2
radius = (abs(x1[1] - x1[0])) / 2
for th in theta:
X.append(i)
Y.append(yl + radius * np.cos(th))
Z.append(x_a + radius * np.sin(th))
iterator = np.linspace(extarms2["right"][0], extarms2["left"][0], 50)
for i in iterator:
x1 = retval2(i, armsc2)
if len(x1) != 2:
#print x1
continue
x_a = (x1[0] + x1[1]) / 2
radius = abs(x1[1] - x1[0]) / 2
for th in theta:
X.append(i)
Y.append(yr + radius * np.cos(th))
Z.append(x_a + radius * np.sin(th))
points = np.array(points)
theta = np.linspace(0, 2 * np.pi, 30)
if len(points) != 0:
t = float(points[1, 1] -
points[0, 1]) / (max(max(f1[:, 1]), max(s1[:, 1])) * 0.01)
iterator = np.linspace(points[0, 1], points[1, 1], abs(t))
else:
iterator = np.linspace(0, max(max(f1[:, 1]), max(s1[:, 1])), 100)
#For every value in iterator plotting the data points considering ellipses
for fl in iterator:
x1 = retval(fl, f1)
y1 = retval(fl, s1)
if len(y1) != 2:
continue
if len(x1) % 2 != 0 or len(x1) > 5 or len(x1) == 0:
continue
x_a = (x1[0] + x1[1]) / 2
y_a = (y1[0] + y1[1]) / 2
a = (x1[1] - x1[0]) / 2
b = (y1[1] - y1[0]) / 2
for temp in x_a + a * np.cos(theta):
X.append(temp)
for temp in y_a + b * np.sin(theta):
Y.append(temp)
Z.append(fl)
if len(x1) == 4:
x_a = (x1[2] + x1[3]) / 2
y_a = (y1[0] + y1[1]) / 2
a = (x1[2] - x1[3]) / 2
b = (y1[1] - y1[0]) / 2
#print y1,b
for temp in x_a + a * np.cos(theta):
X.append(temp)
for temp in y_a + b * np.sin(theta):
Y.append(temp)
Z.append(fl)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots )
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
# Create cubic bounding box to simulate equal aspect ratio
max_range = np.array([max(X) - min(X),
max(Y) - min(Y),
max(Z) - min(Z)]).max()
Xb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * (max(X) + min(X))
Yb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * (max(Y) + min(Y))
Zb = 0.5 * max_range * np.mgrid[
-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * (max(Z) + min(Z))
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
plt.show()
def disint(inpf, inps):
'''
inputs:
inpf: input front image file
inps: input side image file
output:
enclosing contour of:
1. head
2. body without head
3. side view of head
4. body without head side view
5. hands(front view)
6. body without hands(front view)
'''
imgf=cv2.imread(inpf)
imgs=cv2.imread(inps)
cont_f=encContour(inpf)
cont_s=encContour(inps)
#getting extreme points
l=tuple(cont_f[cont_f[:,:,0].argmin()][0])
r=tuple(cont_f[cont_f[:,:,0].argmax()][0])
t=tuple(cont_f[cont_f[:,:,1].argmin()][0])
b=tuple(cont_f[cont_f[:,:,1].argmax()][0])
ls=tuple(cont_s[cont_s[:,:,0].argmin()][0])
rs=tuple(cont_s[cont_s[:,:,0].argmax()][0])
ts=tuple(cont_s[cont_s[:,:,1].argmin()][0])
bs=tuple(cont_s[cont_s[:,:,1].argmax()][0])
length=r[0]-l[0]
ran=np.linspace(0,b[1],200)
for i in range(0,200):
if i==0:
continue
ran[i]=b[1]-ran[i]
c=0
z=0
X=[]
for i in ran:
x11=retval(i,cont_f,0) #0 for y-coordinate
if z==1:
break
if len(x11)==2 and c==0:
wl=x11[1]-x11[0]
c=1
if len(x11)==2 and c==1:
nl=x11[1]-x11[0]
if nl<0.65*wl:
X.append([x11[0],b[1]-i])
X.append([x11[1],b[1]-i])
z=1
break
imgf1=imgf
imgs1=imgs
h,w,d=imgf.shape
h2,w2,d2=imgs.shape
img1=np.zeros((h,w,d), dtype=np.uint8)
img1[:,:]=[255,255,255]
img2=np.zeros((h,w,d), dtype=np.uint8)
img2[:,:]=[255,255,255]
img2[0:int(i),:]=imgf1[0:int(i),:] #projecting the head in blank image
imgf1[0:int(i),:]=img1[0:int(i),:] #removing the head from copy of image
cv2.imwrite('head.jpg', img2)
cv2.imwrite('body_without_head.jpg', imgf1)
dist=int(i-t[1])
img5=np.zeros((h2,w2,d2), dtype=np.uint8)
img5[:,:]=[255,255,255]
img6=np.zeros((h2,w2,d2), dtype=np.uint8)
img6[:,:]=[255,255,255]
img6[0:ts[1]+dist,:]=imgs1[0:ts[1]+dist,:] #projecting the head in blank image
imgs1[0:ts[1]+dist,:]=img5[0:ts[1]+dist,:]
cv2.imwrite('head_side_view.jpg', img6)
cv2.imwrite('side_view_without_head.jpg', imgs1)
#cv2.imshow(img2, 'head')
Y=[]
height,width,dim=imgf.shape
#for hands
a=0
b=0
ran2=np.linspace(1,r[0],200)
for i in ran2:
yp=retval(i-1,cont_f,1)
y=retval(i,cont_f,1)
if len(yp)==2 and a==0:
p_slope=y[0]-yp[0]
a=1 #comparing slopes
continue
elif len(yp)==2 and len(y)==2 and a==1:
#print len(yp)
slope=y[1]-yp[1]
if p_slope!=0:
b=1
ratio=slope/p_slope
p_slope=slope
else:
continue
if b==1 and ratio>1.2:
Y.append([i,y[1]])
Y.append([width-i,y[1]])
Y.append([i,y[0]])
Y.append([width-i,y[0]])
break
#print Y
imgf2=cv2.imread(inpf)
img3=np.zeros((height,width,dim), dtype=np.uint8)
img3[:,:]=[255,255,255]
img4=np.zeros((height,width,dim), dtype=np.uint8)
img4[:,:]=[255,255,255]
img4[:,0:int(i)]=imgf2[:,0:int(i)]
img4[:,int(width-i):int(width)]=imgf2[:,int(width-i):int(width)] #projecting hands in blank image
imgf2[:,0:int(i)]=img3[:,0:int(i)] #removing hands from copy of original image
imgf2[:,int(width-i):int(width)]=img3[:,int(width-i):int(width)] #also taking the mirror image of one hand
cv2.imwrite('hands.jpg', img4)
cv2.imwrite('body_without_hands.jpg', imgf2)
img4=cv2.imread('hands.jpg')
imgf2=cv2.imread('body_without_hands.jpg')
#plt.imshow(img4)
#plt.show()
#plt.imshow(imgf2)
#plt.show()
return 'head.jpg', 'body_without_head.jpg', 'head_side_view.jpg', 'side_view_without_head.jpg', 'hands.jpg', 'body_without_hands.jpg';
def disint_hum(inpf):
imgf=cv2.imread(inpf)
#imgs=cv2.imread(inps)
cont_f=encContour(inpf)
#cont_s=encContour(inps)
#getting extreme points
l=tuple(cont_f[cont_f[:,:,0].argmin()][0])
r=tuple(cont_f[cont_f[:,:,0].argmax()][0])
t=tuple(cont_f[cont_f[:,:,1].argmin()][0])
b=tuple(cont_f[cont_f[:,:,1].argmax()][0])
#ls=tuple(cont_s[cont_s[:,:,0].argmin()][0])
#rs=tuple(cont_s[cont_s[:,:,0].argmax()][0])
#ts=tuple(cont_s[cont_s[:,:,1].argmin()][0])
#bs=tuple(cont_s[cont_s[:,:,1].argmax()][0])
hi=b[1]-t[1]
p=t[1]+0.4*hi
x=retval(p,cont_f,0)
ran=np.linspace(t[1],p,200)
for i in range(0,200):
ran[i]=p-ran[i]
ran[i]=ran[i]+t[1]
Y=[]
a=0
b=0
#sorted(ran,reverse=True)
for i in ran:
#i=-i
#i=p+i
if i==0:
continue
yp=retval(i-1,cont_f,0)
y=retval(i,cont_f,0)
if len(yp)==2 and a==0:
p_slope=yp[0]-y[0]
a=1 #comparing slopes
continue
elif len(yp)==2 and len(y)==2 and a==1:
#print len(yp)
slope=yp[0]-y[0]
if p_slope!=0:
b=1
ratio=float(slope)/p_slope
p_slope=slope
else:
continue
if b==1 and ratio>1.5:
Y.append(y[0])
Y.append(y[1])
break
imgf2=cv2.imread(inpf)
height,width,dim=imgf2.shape
img3=np.zeros((height,width,dim), dtype=np.uint8)
img3[:,:]=[255,255,255]
img4=np.zeros((height,width,dim), dtype=np.uint8)
img4[:,:]=[255,255,255]
img4[:,0:int(Y[0])]=imgf2[:,0:int(Y[0])]
img4[:,int(Y[1]):int(width)]=imgf2[:,int(Y[1]):int(width)] #projecting hands in blank image
imgf2[:,0:int(Y[0])]=img3[:,0:int(Y[0])] #removing hands from copy of original image
imgf2[:,int(Y[1]):int(width)]=img3[:,int(Y[1]):int(width)] #also taking the mirror image of one hand
cv2.imwrite('handsi.jpg', img4)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_handsi.jpg', imgf2)
#plt.imshow(imgf2)
#plt.show()
#for cutting head
ran2=np.linspace(l[0],r[0],300)
q=0
Z=[]
for i in range(0,300):
if i==0:
continue
z=retval(ran2[i],cont_f,1)
zp=retval(ran2[i-1],cont_f,1)
if a==0:
p_slope=z[0]-zp[0]
a=1
elif a==1:
slope=z[0]-zp[0]
ratio=p_slope/slope
p_slope=slope
if ratio>1.2:
Z.append(z[0])
w=ran2[i]
break
imgf2=cv2.imread(inpf)
img1=np.zeros((height,width,dim), dtype=np.uint8)
img1[:,:]=[255,255,255]
img2=np.zeros((height,width,dim), dtype=np.uint8)
img2[:,:]=[255,255,255]
img2[0:int(Z[0]),:]=imgf[0:int(Z[0]),:]
imgf[0:int(Z[0]),:]=img1[0:int(Z[0]),:]
cv2.imwrite('headi.jpg', img2)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_headi.jpg', imgf)
#plt.imshow(imgf2)
#plt.show()
return 'handsi.jpg','body_without_handsi.jpg','head.jpg','body_without_headi.jpg'
#from resize_model import resize_model
import cv2
from plot3d import plot3dHuman2
from plot3d import plot3dHuman
from ret2Val import ret2Val
from resize_model import resize_model
from ret_hscale import ret_hscale
from outer import encContour
from cvt_small import cvt_small
'''
This is the test file of ret_hscale
assuming that the real image is girl_front.jpg (as the hands down image of man.jpg was not available :P ) and model image is mfront.jpg
'''
#resize_model('man_front.jpg','man_frontf.jpg','man_side.jpg','girl_front.jpg')
#a=ret2Val('mfront.jpg')
#ret_hscale('girl_front.jpg','mfront.jpg',('mfront.jpg','mfrontf.jpg'))
#plot3dHuman2('body_without_hands.jpg','hands.jpg','man_side.jpg',[])
#from outer import encContour
image = cv2.imread('x1.jpg')
resized_image = cvt_small(image,800)
#y=encContour('P_20160618_001210.jpg')
contour_of_resized_image = encContour(resized_image)
cv2.drawContours(resized_image, contour_of_resized_image, -1 ,(255),2)
cv2.imshow('final',resized_image)
cv2.waitKey(0)
def plot3dHuman2 (front,arms,side,points):
#Extracting enclosing contours
s=encContour(side)
f=encContour(front)
#armsc=encContour(arms)
# arms has both the arms so encContour would give the bigger contour but we want both the contours. Dividing the image into two and then using encContour for both the images
arms1=cv2.imread(arms)
blank=np.zeros(arms1.shape,dtype=np.uint8)
blank[:,:,:]=255
arms2=cv2.imread(arms)
#print arms1.shape[1]
arms1[:,arms1.shape[1]/2:arms1.shape[1]]=blank[:,arms1.shape[1]/2:arms1.shape[1]]
arms2[:,0 : arms1.shape[1]/2]=blank[:,0 : arms1.shape[1]/2]
# Uncomment this
cv2.imwrite('arms1.jpg',arms1)
cv2.imwrite('arms2.jpg',arms2)
armsc1=encContour('arms1.jpg')
armsc2=encContour('arms2.jpg')
extarms1=give_extpoints(armsc1)
extarms2=give_extpoints(armsc2)
trpoint=extarms1["right"]
tlpoint=extarms2["left"]
trindex=give_index_ct(armsc1,trpoint[1])
tlindex=give_index_ct(armsc2,tlpoint[1])
'''
if len(trindex)!=2 or len(tlindex)!=2 :
print "Debug....."
exit(1)
trindex=trindex[1] if armsc1[trindex[1],0,0]>armsc1[trindex[0],0,0] else trindex[0]
tlindex=tlindex[0] if armsc1[tlindex[0],0,0]<armsc1[tlindex[1],0,0] else tlindex[1]
'''
mini=tlindex[0]
maxi=trindex[0]
if len(trindex) >1:
for temp in trindex :
if armsc1[maxi,0,0] < armsc1[temp,0,0] :
maxi=temp
trindex=maxi
if len(tlindex) >1 :
for temp in tlindex :
if armsc2[mini,0,0] > armsc2[temp,0,0] :
mini=temp
tlindex=mini
if armsc1[trindex+1,0,1] < armsc1[trindex,0,1] :
while armsc1[trindex+1,0,0] == armsc1[trindex,0,0] :
trindex+=1
elif armsc1[trindex-1,0,1] < armsc1[trindex,0,1] :
while armsc1[trindex-1,0,0] ==armsc1[trindex,0,0] :
trindex-=1
else :
print "Consider more pixels"
exit(1)
if armsc2[tlindex+1,0,1] < armsc2[tlindex,0,1] :
while armsc2[tlindex+1,0,0] == armsc2[tlindex,0,0] :
tlindex+=1
elif armsc2[tlindex-1,0,1] < armsc2[tlindex,0,1] :
while armsc2[tlindex-1,0,0] ==armsc2[tlindex,0,0] :
tlindex-=1
else :
print "Consider more pixels"
exit(1)
'''
extfront=give_extpoints(f)
x1=extfront["left"]
x2=extfront["right"]
armsVrange=[]
index1=give_index_ct(f,x1[1])
index2=give_index_ct(f,x2[1])
if len(index1) != 2 or len(index2) !=2 :
print "debug...."
exit(1)
indexl=index1[0] if f[index1[0],0,0]<f[index1[1],0,0] else index1[1]
indexr=index2[0] if f[index2[0],0,0]>f[index2[1],0,0] else index2[1]
g=False
indexleft=[]
indexright=[]
for temp in [indexl,indexr] :
t1=temp
t2=temp
i=False
k=False
while i==k :
i=not i
if f[t1+1,0,0]==f[t1,0,0] :
if i is not k :
k=i
t1=t1+1
if f[t2-1,0,0]==f[t2,0,0] :
if i is not k :
k=i
t2=t2 -1
if not g :
if f[t1,0,0] > f[t2,0,0] :
indexleft=[t2,t1]
else :
indexleft=[t1,t2]
else :
if f[t1,0,0] > f[t2,0,0] :
indexright=[t2,t1]
else :
indexright=[t1,t2]
g= not g
'''
# Getting extreme points
fl=tuple(f[f[:,:,0].argmin()][0])
fr=tuple(f[f[:,:,0].argmax()][0])
sl=tuple(s[s[:,:,0].argmin()][0])
sr=tuple(s[s[:,:,0].argmax()][0])
ft=tuple(f[f[:,:,1].argmin()][0])
fb=tuple(f[f[:,:,1].argmax()][0])
st=tuple(s[s[:,:,1].argmin()][0])
sb=tuple(s[s[:,:,1].argmax()][0])
#from height import height
#height('man_front.jpg')
#print ft,fb
#print st,sb
# manipulating the contour to set the lowest point to origin and the body in positive direction
f1=f
f1=f1[:,0]-[fr[0]-(fl[0]+fr[0])/2,fb[1]]
if len(points) != 0 :
print points,fb[1]
points[:,1]=points[:,1]-fb[1]
points[:,1]=-points[:,1]
#print points[:,1]
f1[:,1]=-f1[:,1]
s1=s
s1=s1[:,0]-[sl[0]+(sl[0]+sr[0])/2,sb[1]]
s1[:,1]=-s1[:,1]
matplotlib.rcParams['legend.fontsize']=10
fig=plt.figure()
ax=fig.gca(projection='3d')
s1
#y=np.zeros(len(f1[:,0]),dtype=np.int64)
#ax.plot(f1[:,0],y,f1[:,1],label='front')
#x=np.zeros(len(s1[:,0]),dtype=np.int64)
#ax.plot(x,s1[:,0],s1[:,1],label='side')
ax.set_aspect('equal')
#print retval(800,f1)
# .......................added!...........................
extfront=give_extpoints2(f1)
x1=extfront["left"]
x2=extfront["right"]
index1=give_index_ct2(f1,x1[1])
index2=give_index_ct2(f1,x2[1])
if len(index1) != 2 or len(index2) !=2 :
print "debug...."
exit(1)
indexl=index1[0] if f1[index1[0],0]<f1[index1[1],0] else index1[1]
indexr=index2[0] if f1[index2[0],0]>f1[index2[1],0] else index2[1]
g=False
indexleft=[]
indexright=[]
for temp in [indexl,indexr] :
t1=temp
t2=temp
i=False
k=False
while i==k :
i=not i
if f1[t1+1,0]==f1[t1,0] :
if i is not k :
k=i
t1=t1+1
if f1[t2-1,0]==f1[t2,0] :
if i is not k :
k=i
t2=t2 -1
if not g :
if f1[t1,1] < f1[t2,1] :
indexleft=[t1,t2]
else :
indexleft=[t2,t1]
else :
if f1[t1,1] < f1[t2,1] :
indexright=[t1,t2]
else :
indexright=[t2,t1]
g= not g
z_al=(f1[indexleft[0],1]+f1[indexleft[1],1])/2
temp=retval(z_al,s1)
y_al=(temp[0]+temp[1])/2
z_ar=(f1[indexright[0],1]+f1[indexright[1],1])/2
temp=retval(z_ar,s1)
y_ar=(temp[0]+temp[1])/2
#Matching the topmost shoulder point ( bringing the arms to the main body )
armsc1=armsc1[:,0]-armsc1[trindex,0]+f1[indexleft[1]]
armsc2=armsc2[:,0]-armsc2[tlindex,0]+f1[indexright[1]]
armsVrange=[min(f1[indexleft[0]][1],f1[indexright[0]][1]),max(f1[indexleft[1]][1],f1[indexright[1]][1])]
extarms1=give_extpoints2(armsc1)
extarms2=give_extpoints2(armsc2)
X=[] ;Y=[];Z=[]
armsplotting = True
for p in points :
if p[1] <armsVrange[1] and p[1] > armsVrange[0] :
armsplotting=False
print " Don't click on arms "
if armsplotting :
#Plotting arms
theta=np.linspace(0,2*np.pi,15)
iterator=np.linspace(extarms1["right"][0],extarms1["left"][0],20)
for i in iterator :
x1=retval2(i,armsc1)
#print x1
if len(x1)!=2 :
continue
x_a=(x1[0]+x1[1])/2
radius = abs(x1[1]-x1[0])
for th in theta :
X.append(i)
Y.append(y_al+radius*np.cos(th))
Z.append(x_a+radius*np.sin(th))
iterator=np.linspace(extarms2["right"][0],extarms2["left"][0],20)
for i in iterator :
x1=retval2(i,armsc2)
if len(x1)!=2 :
#print x1
continue
x_a=(x1[0]+x1[1])/2
radius = abs(x1[1]-x1[0])
#print radius
for th in theta :
X.append(i)
Y.append(y_ar+radius*np.cos(th))
Z.append(x_a+radius*np.sin(th))
#Using 30 data points per height
theta=np.linspace(0,2*np.pi,30)
if len(points) != 0 :
t= float( points[1,1]- points[0,1])/(max(max(f1[:,1]),max(s1[:,1]))*0.01)
iterator=np.linspace(points[0,1],points[1,1],abs(t))
#iterator=[ int (k) for k in iterator ]
# print points,points[0,1],points[1,1],iterator
else :
iterator=np.linspace(0,max(max(f1[:,1]),max(s1[:,1])),100)
#For every value in iterator plotting the data points considering ellipses
for fl in iterator:
x1=retval(fl,f1)
y1=retval(fl,s1)
if len(y1) !=2 :
continue
if len(x1)%2 !=0 or len(x1) >5 or len(x1) == 0:
continue
x_a=(x1[0]+x1[1])/2
y_a=(y1[0]+y1[1])/2
a=(x1[1]-x1[0])/2
b=(y1[1]-y1[0])/2
#print a,b
#print y1,b
for temp in x_a+a*np.cos(theta) :
X.append(temp)
for temp in y_a+b*np.sin(theta) :
Y.append(temp)
Z.append(fl)
if len(x1) ==4 :
x_a=(x1[2]+x1[3])/2
y_a=(y1[0]+y1[1])/2
a=(x1[2]-x1[3])/2
b=(y1[1]-y1[0])/2
#print y1,b
for temp in x_a+a*np.cos(theta) :
X.append(temp)
for temp in y_a+b*np.sin(theta) :
Y.append(temp)
Z.append(fl)
fig=plt.figure()
ax=fig.add_subplot(111,projection='3d')
#print len(X),len(Y),len(Z)
#Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots )
ax.plot_wireframe(X,Y,Z,rstride=10,cstride=10)
# Create cubic bounding box to simulate equal aspect ratio
max_range = np.array([max(X)-min(X), max(Y)-min(Y),max(Z)-min(Z)]).max()
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(X)+min(X))
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(Y)+min(Y))
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(Z)+min(Z))
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
plt.show()
def disint_hum(inpf):
imgf = cv2.imread(inpf)
#imgs=cv2.imread(inps)
cont_f = encContour(inpf)
#cont_s=encContour(inps)
#getting extreme points
l = tuple(cont_f[cont_f[:, :, 0].argmin()][0])
r = tuple(cont_f[cont_f[:, :, 0].argmax()][0])
t = tuple(cont_f[cont_f[:, :, 1].argmin()][0])
b = tuple(cont_f[cont_f[:, :, 1].argmax()][0])
#ls=tuple(cont_s[cont_s[:,:,0].argmin()][0])
#rs=tuple(cont_s[cont_s[:,:,0].argmax()][0])
#ts=tuple(cont_s[cont_s[:,:,1].argmin()][0])
#bs=tuple(cont_s[cont_s[:,:,1].argmax()][0])
hi = b[1] - t[1]
p = t[1] + 0.4 * hi
x = retval(p, cont_f, 0)
ran = np.linspace(t[1], p, 200)
for i in range(0, 200):
ran[i] = p - ran[i]
ran[i] = ran[i] + t[1]
Y = []
a = 0
b = 0
#sorted(ran,reverse=True)
for i in ran:
#i=-i
#i=p+i
if i == 0:
continue
yp = retval(i - 1, cont_f, 0)
y = retval(i, cont_f, 0)
if len(yp) == 2 and a == 0:
p_slope = yp[0] - y[0]
a = 1 #comparing slopes
continue
elif len(yp) == 2 and len(y) == 2 and a == 1:
#print len(yp)
slope = yp[0] - y[0]
if p_slope != 0:
b = 1
ratio = float(slope) / p_slope
p_slope = slope
else:
continue
if b == 1 and ratio > 1.5:
Y.append(y[0])
Y.append(y[1])
break
imgf2 = cv2.imread(inpf)
height, width, dim = imgf2.shape
img3 = np.zeros((height, width, dim), dtype=np.uint8)
img3[:, :] = [255, 255, 255]
img4 = np.zeros((height, width, dim), dtype=np.uint8)
img4[:, :] = [255, 255, 255]
img4[:, 0:int(Y[0])] = imgf2[:, 0:int(Y[0])]
img4[:, int(Y[1]):int(width)] = imgf2[:, int(Y[1]):int(
width)] #projecting hands in blank image
imgf2[:, 0:int(Y[0])] = img3[:, 0:int(
Y[0])] #removing hands from copy of original image
imgf2[:, int(Y[1]):int(width)] = img3[:, int(Y[1]):int(
width)] #also taking the mirror image of one hand
cv2.imwrite('handsi.jpg', img4)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_handsi.jpg', imgf2)
#plt.imshow(imgf2)
#plt.show()
#for cutting head
ran2 = np.linspace(l[0], r[0], 300)
q = 0
Z = []
for i in range(0, 300):
if i == 0:
continue
z = retval(ran2[i], cont_f, 1)
zp = retval(ran2[i - 1], cont_f, 1)
if a == 0:
p_slope = z[0] - zp[0]
a = 1
elif a == 1:
slope = z[0] - zp[0]
ratio = p_slope / slope
p_slope = slope
if ratio > 1.2:
Z.append(z[0])
w = ran2[i]
break
imgf2 = cv2.imread(inpf)
img1 = np.zeros((height, width, dim), dtype=np.uint8)
img1[:, :] = [255, 255, 255]
img2 = np.zeros((height, width, dim), dtype=np.uint8)
img2[:, :] = [255, 255, 255]
img2[0:int(Z[0]), :] = imgf[0:int(Z[0]), :]
imgf[0:int(Z[0]), :] = img1[0:int(Z[0]), :]
cv2.imwrite('headi.jpg', img2)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_headi.jpg', imgf)
#plt.imshow(imgf2)
#plt.show()
return 'handsi.jpg', 'body_without_handsi.jpg', 'head.jpg', 'body_without_headi.jpg'
def disint(inpf, inps):
'''
inputs:
inpf: input front image file
inps: input side image file
output:
enclosing contour of:
1. head
2. body without head
3. side view of head
4. body without head side view
5. hands(front view)
6. body without hands(front view)
'''
imgf = cv2.imread(inpf)
imgs = cv2.imread(inps)
cont_f = encContour(inpf)
cont_s = encContour(inps)
#getting extreme points
l = tuple(cont_f[cont_f[:, :, 0].argmin()][0])
r = tuple(cont_f[cont_f[:, :, 0].argmax()][0])
t = tuple(cont_f[cont_f[:, :, 1].argmin()][0])
b = tuple(cont_f[cont_f[:, :, 1].argmax()][0])
ls = tuple(cont_s[cont_s[:, :, 0].argmin()][0])
rs = tuple(cont_s[cont_s[:, :, 0].argmax()][0])
ts = tuple(cont_s[cont_s[:, :, 1].argmin()][0])
bs = tuple(cont_s[cont_s[:, :, 1].argmax()][0])
length = r[0] - l[0]
ran = np.linspace(0, b[1], 200)
for i in range(0, 200):
if i == 0:
continue
ran[i] = b[1] - ran[i]
c = 0
z = 0
X = []
for i in ran:
x11 = retval(i, cont_f, 0) #0 for y-coordinate
if z == 1:
break
if len(x11) == 2 and c == 0:
wl = x11[1] - x11[0]
c = 1
if len(x11) == 2 and c == 1:
nl = x11[1] - x11[0]
if nl < 0.65 * wl:
X.append([x11[0], b[1] - i])
X.append([x11[1], b[1] - i])
z = 1
break
imgf1 = imgf
imgs1 = imgs
h, w, d = imgf.shape
h2, w2, d2 = imgs.shape
img1 = np.zeros((h, w, d), dtype=np.uint8)
img1[:, :] = [255, 255, 255]
img2 = np.zeros((h, w, d), dtype=np.uint8)
img2[:, :] = [255, 255, 255]
img2[0:int(i), :] = imgf1[0:int(i), :] #projecting the head in blank image
imgf1[0:int(i), :] = img1[0:int(
i), :] #removing the head from copy of image
cv2.imwrite('head.jpg', img2)
cv2.imwrite('body_without_head.jpg', imgf1)
dist = int(i - t[1])
img5 = np.zeros((h2, w2, d2), dtype=np.uint8)
img5[:, :] = [255, 255, 255]
img6 = np.zeros((h2, w2, d2), dtype=np.uint8)
img6[:, :] = [255, 255, 255]
img6[0:ts[1] +
dist, :] = imgs1[0:ts[1] +
dist, :] #projecting the head in blank image
imgs1[0:ts[1] + dist, :] = img5[0:ts[1] + dist, :]
cv2.imwrite('head_side_view.jpg', img6)
cv2.imwrite('side_view_without_head.jpg', imgs1)
#cv2.imshow(img2, 'head')
Y = []
height, width, dim = imgf.shape
#for hands
a = 0
b = 0
ran2 = np.linspace(1, r[0], 200)
for i in ran2:
yp = retval(i - 1, cont_f, 1)
y = retval(i, cont_f, 1)
if len(yp) == 2 and a == 0:
p_slope = y[0] - yp[0]
a = 1 #comparing slopes
continue
elif len(yp) == 2 and len(y) == 2 and a == 1:
#print len(yp)
slope = y[1] - yp[1]
if p_slope != 0:
b = 1
ratio = slope / p_slope
p_slope = slope
else:
continue
if b == 1 and ratio > 1.2:
Y.append([i, y[1]])
Y.append([width - i, y[1]])
Y.append([i, y[0]])
Y.append([width - i, y[0]])
break
print Y
imgf2 = cv2.imread(inpf)
img3 = np.zeros((height, width, dim), dtype=np.uint8)
img3[:, :] = [255, 255, 255]
img4 = np.zeros((height, width, dim), dtype=np.uint8)
img4[:, :] = [255, 255, 255]
img4[:, 0:int(i)] = imgf2[:, 0:int(i)]
img4[:, int(width - i):int(width)] = imgf2[:, int(width - i):int(
width)] #projecting hands in blank image
imgf2[:, 0:int(i)] = img3[:, 0:int(
i)] #removing hands from copy of original image
imgf2[:, int(width - i):int(width)] = img3[:, int(width - i):int(
width)] #also taking the mirror image of one hand
cv2.imwrite('hands.jpg', img4)
cv2.imwrite('body_without_hands.jpg', imgf2)
return 'head.jpg', 'body_without_head.jpg', 'head_side_view.jpg', 'side_view_without_head.jpg', 'hands.jpg', 'body_without_hands.jpg'
def plot3dHuman2 (front,arms,side,points): #Extracting enclosing contours s=encContour(side) f=encContour(front) #armsc=encContour(arms) # arms has both the arms so encContour would give the bigger contour but we want both the contours. Dividing the image into two and then using encContour for both the images arms1=cv2.imread(arms) blank=np.zeros(arms1.shape,dtype=np.uint8) blank[:,:,:]=255 arms2=cv2.imread(arms) #print arms1.shape[1] arms1[:,arms1.shape[1]/2:arms1.shape[1]]=blank[:,arms1.shape[1]/2:arms1.shape[1]] arms2[:,0 : arms1.shape[1]/2]=blank[:,0 : arms1.shape[1]/2] armsc1=encContour(arms1) armsc2=encContour(arms2) extarms1=give_extindices(armsc1)["right"] extarms2=give_extindices(armsc2)["left"] index1=index_flatside(armsc1,"right")[0] index2=index_flatside(armsc2,"left")[0] # Getting extreme points ''' fl=tuple(f[f[:,:,0].argmin()][0]) fr=tuple(f[f[:,:,0].argmax()][0]) sl=tuple(s[s[:,:,0].argmin()][0]) sr=tuple(s[s[:,:,0].argmax()][0]) ft=tuple(f[f[:,:,1].argmin()][0]) fb=tuple(f[f[:,:,1].argmax()][0]) st=tuple(s[s[:,:,1].argmin()][0]) sb=tuple(s[s[:,:,1].argmax()][0]) ''' extf=give_extpoints(f) exts=give_extpoints(s) f1=f.copy() s1=s.copy() f1=f1[:,0] s1=s1[:,0] bindex1=index_flatside(f,"left",0.33) bindex2=index_flatside(f,"right",0.33) matplotlib.rcParams['legend.fontsize']=10 fig=plt.figure() ax=fig.gca(projection='3d') # Changing reference f1=f1[:]-[(extf["left"][0]+extf["right"][0])/2,extf["bottom"][1]] f1[:,1]=-f1[:,1] s1=s1[:]-[(exts["left"][0]+exts["right"][0])/2,exts["bottom"][1]] s1[:,1]=-s1[:,1] armsc1[:,0,1]=-armsc1[:,0,1] armsc2[:,0,1]=-armsc2[:,0,1] if len(points) != 0 : #print points,fb[1] points[0][1]=-(points[0][1]-extf["bottom"][1]) points[1][1]=-(points[1][1]-extf["bottom"][1]) #print points #Matching the topmost shoulder point ( bringing the arms to the main body ) armsc1[:,0]=armsc1[:,0]-armsc1[index1,0]+f1[bindex1[0]] armsc2[:,0]=armsc2[:,0]-armsc2[index2,0]+f1[bindex2[0]] armsVrange=[min(f1[bindex1[1]][1],f1[bindex2[1]][1]),max(f1[bindex1[0]][1],f1[bindex2[0]][1])] armsc1=armsc1[:,0] armsc2=armsc2[:,0] extarms1=give_extpoints2(armsc1) extarms2=give_extpoints2(armsc2) temp=retval((f1[bindex1[0],1]+f1[bindex1[1],1])/2,s1) yl,yr=[0,0] if len(temp)==2: yl=(temp[0]+temp[1])/2 temp=retval((f1[bindex2[0],1]+f1[bindex2[1],1])/2,s1) if len(temp)==2 : yr=(temp[0]+temp[1])/2 X=[] ;Y=[];Z=[] armsplotting = False clickrange=[points[0][1],points[0][0]] clickrange=sorted(clickrange,reverse=False) for p in clickrange : if p <armsVrange[1] and p > armsVrange[0] : print " Don't click on arms " exit(1) if clickrange[0]<armsVrange[0] and clickrange[1] > armsVrange[1] : armsplotting=True if armsplotting : #Plotting arms theta=np.linspace(0,2*np.pi,15) iterator=np.linspace(extarms1["right"][0],extarms1["left"][0],50) for i in iterator : x1=retval2(i,armsc1) #print x1 if len(x1)!=2 : continue x_a=(x1[0]+x1[1])/2 radius = (abs(x1[1]-x1[0]))/2 for th in theta : X.append(i) Y.append(yl+radius*np.cos(th)) Z.append(x_a+radius*np.sin(th)) iterator=np.linspace(extarms2["right"][0],extarms2["left"][0],50) for i in iterator : x1=retval2(i,armsc2) if len(x1)!=2 : #print x1 continue x_a=(x1[0]+x1[1])/2 radius = abs(x1[1]-x1[0])/2 for th in theta : X.append(i) Y.append(yr+radius*np.cos(th)) Z.append(x_a+radius*np.sin(th)) points=np.array(points) theta=np.linspace(0,2*np.pi,30) if len(points) != 0 : t= float( points[1,1]- points[0,1])/(max(max(f1[:,1]),max(s1[:,1]))*0.01) iterator=np.linspace(points[0,1],points[1,1],abs(t)) else : iterator=np.linspace(0,max(max(f1[:,1]),max(s1[:,1])),100) #For every value in iterator plotting the data points considering ellipses for fl in iterator: x1=retval(fl,f1) y1=retval(fl,s1) if len(y1) !=2 : continue if len(x1)%2 !=0 or len(x1) >5 or len(x1) == 0: continue x_a=(x1[0]+x1[1])/2 y_a=(y1[0]+y1[1])/2 a=(x1[1]-x1[0])/2 b=(y1[1]-y1[0])/2 for temp in x_a+a*np.cos(theta) : X.append(temp) for temp in y_a+b*np.sin(theta) : Y.append(temp) Z.append(fl) if len(x1) ==4 : x_a=(x1[2]+x1[3])/2 y_a=(y1[0]+y1[1])/2 a=(x1[2]-x1[3])/2 b=(y1[1]-y1[0])/2 #print y1,b for temp in x_a+a*np.cos(theta) : X.append(temp) for temp in y_a+b*np.sin(theta) : Y.append(temp) Z.append(fl) fig=plt.figure() ax=fig.add_subplot(111,projection='3d') #Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots ) ax.plot_wireframe(X,Y,Z,rstride=10,cstride=10) # Create cubic bounding box to simulate equal aspect ratio max_range = np.array([max(X)-min(X), max(Y)-min(Y),max(Z)-min(Z)]).max() Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(X)+min(X)) Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(Y)+min(Y)) Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(Z)+min(Z)) for xb, yb, zb in zip(Xb, Yb, Zb): ax.plot([xb], [yb], [zb], 'w') plt.show()
from outer import encContour
from height import height
from plot import plot_contour
from matplotlib import pyplot as plt
import cv2
import numpy as np
import copy
import math
import sys
cnt1 = encContour('man_front.jpg')
cnt2 = encContour('man_side.jpg')
image = cv2.imread('man_front.jpg')
plot_contour(cnt1=cnt1, cnt2=cnt2)
#cv2.drawContours(image,cnt1,-1,(255),3)
#cv2.imshow('front image',image)
#cv2.waitKey(0)
#print((cnt))
'''
#test for encContour
image = cv2.imread(url)
cnt = encContour(url)
cv2.drawContours(image,cnt,-1,(255),3)
cv2.imshow('image',image)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
def plot3dHuman (front,side,points):
#Extracting enclosing contours
s=encContour(side)
f=encContour(front)
# Function which gives x values for a height t and modified contour f
# Getting extreme points
fl=tuple(f[f[:,:,0].argmin()][0])
fr=tuple(f[f[:,:,0].argmax()][0])
sl=tuple(s[s[:,:,0].argmin()][0])
sr=tuple(s[s[:,:,0].argmax()][0])
ft=tuple(f[f[:,:,1].argmin()][0])
fb=tuple(f[f[:,:,1].argmax()][0])
st=tuple(s[s[:,:,1].argmin()][0])
sb=tuple(s[s[:,:,1].argmax()][0])
#from height import height
#height('man_front.jpg')
#print ft,fb
#print st,sb
# manipulating the contour to set the lowest point to origin and the body in positive direction
f1=f
f1=f1[:,0]-[fr[0]-(fl[0]+fr[0])/2,fb[1]]
if len(points) != 0 :
points[:,1]=points[:,1]-fb[1]
points[:,1]=-points[:,1]
f1[:,1]=-f1[:,1]
s1=s
s1=s1[:,0]-[sl[0]+(sl[0]+sr[0])/2,sb[1]]
s1[:,1]=-s1[:,1]
matplotlib.rcParams['legend.fontsize']=10
fig=plt.figure()
ax=fig.gca(projection='3d')
s1
#y=np.zeros(len(f1[:,0]),dtype=np.int64)
#ax.plot(f1[:,0],y,f1[:,1],label='front')
#x=np.zeros(len(s1[:,0]),dtype=np.int64)
#ax.plot(x,s1[:,0],s1[:,1],label='side')
ax.set_aspect('equal')
#print retval(800,f1)
#Using 30 data points per height
theta=np.linspace(0,2*np.pi,30)
if len(points) != 0 :
t= float( points[1,1]- points[0,1])/(max(max(f1[:,1]),max(s1[:,1]))*0.01)
iterator=np.linspace(points[0,1],points[1,1],t)
else :
iterator=np.linspace(0,max(max(f1[:,1]),max(s1[:,1])),100)
X=[] ;Y=[];Z=[]
#For every value in iterator plotting the data points considering ellipses
for fl in iterator:
x1=retval(fl,f1)
y1=retval(fl,s1)
if len(x1)%2 !=0 :
continue
if len(x1) == 6:
x_a=(x1[0]+x1[1])/2
y_a=(y1[0]+y1[1])/2
x_a1=(x1[4]+x1[5])/2
a=(x1[1]-x1[0])/2
a1=(x1[5]-x1[4])/2
b=a
b1=a1
for temp in (x1[0]+x1[1])/2+a*np.cos(theta) :
X.append(temp)
for temp in (y1[0]+y1[1])/2+b*np.sin(theta) :
Y.append(temp)
Z.append(fl)
for temp in (x1[4]+x1[5])/2+a1*np.cos(theta) :
X.append(temp)
for temp in (y1[0]+y1[1])/2+b1*np.sin(theta) :
Y.append(temp)
Z.append(fl)
xf=[]
if len(x1) == 6 :
xf=[x1[2],x1[3]]
elif len(x1) in [2,4] :
xf=x1
else:
maxdiff=0
d=0
index=-1
while d<len(x1) :
diff=x1[d+1]-x1[d]
if diff> maxdiff :
maxdiff = diff
index=d
d+=2
xf=[x1[index],x1[index+1]]
try :
if x1[index-1]-x1[index-2] < 1.20*(x1[index+1]-x1[index]) and x1[index-1]-x1[index-2] > 0.80*(x1[index+1]-x1[index]) :
xf=x1[index-2:index+2]
x1=xf
elif x1[index+3]-x1[index+2] < 1.20*(x1[index+1]-x1[index]) and x1[index+3]-x1[index+2] > 0.80*(x1[index+1]-x1[index]) :
xf=x1[index:index+4]
x1=xf
except IndexError:
pass
# print len(x1),len(y1)
if xf == [] :
continue
x_a=(xf[1]+xf[0])/2
y_a=(y1[0]+y1[1])/2
a=(xf[1]-xf[0])/2
b=(y1[1]-y1[0])/2
#print y1,b
for temp in x_a+a*np.cos(theta) :
X.append(temp)
for temp in y_a+b*np.sin(theta) :
Y.append(temp)
Z.append(fl)
if(len(x1)==4):
a=(x1[3]-x1[2])/2
x_a=(x1[3]+x1[2])/2
y_a=(y1[0]+y1[1])/2
for temp in x_a+a*np.cos(theta) :
X.append(temp)
for temp in y_a+b*np.sin(theta) :
Y.append(temp)
Z.append(fl)
fig=plt.figure()
ax=fig.add_subplot(111,projection='3d')
#Using wireframe plots ( please improve this : spaces between legs is not clear, may use surface plots )
ax.plot_wireframe(X,Y,Z,rstride=10,cstride=10)
# Create cubic bounding box to simulate equal aspect ratio ( this is just to keep same aspect ratio )
max_range = np.array([max(X)-min(X), max(Y)-min(Y),max(Z)-min(Z)]).max()
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(X)+min(X))
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(Y)+min(Y))
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(Z)+min(Z))
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
plt.show()
from outer import encContour
from height import height
from plot import plot_contour
from matplotlib import pyplot as plt
import cv2
import numpy as np
import copy
import math
import sys
cnt1 = encContour('man_front.jpg')
cnt2 = encContour('man_side.jpg')
image = cv2.imread('man_front.jpg')
plot_contour(cnt1= cnt1, cnt2= cnt2)
#cv2.drawContours(image,cnt1,-1,(255),3)
#cv2.imshow('front image',image)
#cv2.waitKey(0)
#print((cnt))
'''
#test for encContour
image = cv2.imread(url)
cnt = encContour(url)
cv2.drawContours(image,cnt,-1,(255),3)
cv2.imshow('image',image)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
def parts_divide_hum(inpf,inps):
imgf= inpf.copy()
imgs= inps.copy()
cont_f=encContour(imgf)
cont_s=encContour(inps)
#getting extreme points
l=tuple(cont_f[cont_f[:,:,0].argmin()][0])
r=tuple(cont_f[cont_f[:,:,0].argmax()][0])
t=tuple(cont_f[cont_f[:,:,1].argmin()][0])
b=tuple(cont_f[cont_f[:,:,1].argmax()][0])
ls=tuple(cont_s[cont_s[:,:,0].argmin()][0])
rs=tuple(cont_s[cont_s[:,:,0].argmax()][0])
ts=tuple(cont_s[cont_s[:,:,1].argmin()][0])
bs=tuple(cont_s[cont_s[:,:,1].argmax()][0])
hi=b[1]-t[1]
p=t[1]+0.4*hi
x=retval(p,cont_f,0)
ran=np.linspace(t[1],p,200)
for i in range(0,200):
ran[i]=p-ran[i]
ran[i]=ran[i]+t[1]
Y=[]
a=0
b=0
#sorted(ran,reverse=True)
for i in range(1,200):
#i=-i
#i=p+i
yp=retval(ran[i-1],cont_f,0)
y=retval(ran[i],cont_f,0)
if len(yp)==2 and a==0:
p_slope=yp[0]-y[0]
a=1 #comparing slopes
continue
elif len(yp)==2 and len(y)==2 and a==1:
#print len(yp)
slope=yp[0]-y[0]
print p_slope
if p_slope!=0:
b=1
ratio=float(slope)/p_slope
#print ratio
p_slope=slope
else:
continue
if b==1 and ratio>1.2:
Y.append(y[0])
Y.append(y[1])
break
imgf2 = inpf.copy()
#imgf2=cv2.imread(inpf)
height,width,dim=imgf2.shape
img3=np.zeros((height,width,dim), dtype=np.uint8)
img3[:,:]=[255,255,255]
img4=np.zeros((height,width,dim), dtype=np.uint8)
img4[:,:]=[255,255,255]
img4[:,0:int(Y[0])]=imgf2[:,0:int(Y[0])]
img4[:,int(Y[1]):int(width)]=imgf2[:,int(Y[1]):int(width)] #projecting hands in blank image
imgf2[:,0:int(Y[0])]=img3[:,0:int(Y[0])] #removing hands from copy of original image
imgf2[:,int(Y[1]):int(width)]=img3[:,int(Y[1]):int(width)] #also taking the mirror image of one hand
cv2.imwrite('handsi.jpg', img4)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_handsi.jpg', imgf2)
#plt.imshow(imgf2)
#plt.show()
#for cutting head
wid2=(r[0]-l[0])/4
ran2=np.linspace(l[0]+wid2,r[0],300)
q=0
Z=[]
for i in range(0,300):
if i==0:
continue
z=retval(ran2[i],cont_f,1)
zp=retval(ran2[i-1],cont_f,1)
if a==0:
p_slope=float(zp[0]-z[0])/(ran2[i]-ran2[i-1])
a=1
elif a==1:
slope=float(zp[0]-z[0])/(ran2[i]-ran2[i-1])
if p_slope!=0:
ratio=float(slope)/p_slope
#print ratio
p_slope=slope
if ratio>1.5:
Z.append(zp[0])
w=ran2[i]
break
#print len(Z)
#imgf2=cv2.imread(inpf)
imgf2 = inpf.copy()
img1=np.zeros((height,width,dim), dtype=np.uint8)
img1[:,:]=[255,255,255]
img2=np.zeros((height,width,dim), dtype=np.uint8)
img2[:,:]=[255,255,255]
img2[0:int(Z[0]),:]=imgf[0:int(Z[0]),:]
imgf[0:int(Z[0]),:]=img1[0:int(Z[0]),:]
cv2.imwrite('headi.jpg', img2)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_headi.jpg', imgf)
#plt.imshow(imgf2)
#plt.show()
#head side view
cont_h=encContour(img2)
th=tuple(cont_h[cont_h[:,:,1].argmin()][0])
bh=tuple(cont_h[cont_h[:,:,1].argmax()][0])
head_len=bh[1]-th[1]
m=ts[1]+head_len
imgs2=imgs.copy()
hs,ws,ds=imgs2.shape
img5=np.zeros((hs,ws,ds), dtype=np.uint8)
img5[:,:]=[255,255,255]
img6=np.zeros((hs,ws,ds), dtype=np.uint8)
img6[:,:]=[255,255,255]
img6[0:int(m),:]=imgs2[0:int(m),:]
imgs2[0:int(m),:]=img5[0:int(m),:]
cv2.imwrite('head_sidei.jpg', img6)
#plt.imshow(img4)
#plt.show()
cv2.imwrite('body_without_head_sidei.jpg', imgs2)
#plt.imshow(imgf2)
#plt.show()
return 'headi.jpg','body_without_headi.jpg','head_sidei.jpg','body_without_head_sidei.jpg', 'handsi.jpg','body_without_handsi.jpg',
def ret_hscale(rimg,mimg,args):
# getting the enclosing contours of real image and model image
realc=encContour(rimg)
modelc=encContour(mimg)
extreal=give_extpoints(realc)
extmodel=give_extpoints(modelc)
midreal=int( 0.25*extreal["bottom"][1]+0.75*extreal["top"][1])
midmodel=int( 0.25*extmodel["bottom"][1]+0.75*extmodel["top"][1])
realindex=give_index_ct(realc,midreal+extreal["top"][1])
modelindex=give_index_ct(modelc,midmodel+extmodel["top"][1])
min=realc[realindex[0],0,0]
# for real
leftreal=realindex[0]
k=0
for i in realindex :
k+=1
for i in range(1,len(realindex)):
if min> realc[realindex[i],0,0] :
leftreal=realindex[i]
min=realc[realindex[i],0,0]
min=modelc[modelindex[0],0,0]
# for model
leftmodel=modelindex[0]
k=0
for i in modelindex :
k+=1
for i in range(1,len(modelindex)) :
if min>modelc[modelindex[i],0,0] :
leftmodel=modelindex[i]
min=modelc[modelindex[i],0,0]
#figuring out the direction on which to move to reach the shoulder
directionreal='+' if realc[leftreal+1,0,1]+realc[leftreal+2,0,1]+realc[leftreal+3,0,1]<realc[leftreal,0,1]+realc[leftreal-1,0,1]+realc[leftreal-2,0,1] else '-'
directionmodel='+' if modelc[leftmodel+1,0,1]+modelc[leftmodel+2,0,1]+modelc[leftmodel+3,0,1]<modelc[leftmodel,0,1]+modelc[leftmodel-1,0,1]+modelc[leftmodel-2,0,1] else '-'
# Getting the shoulder point for real image
i=leftreal
avr,avm=[0,0]
a=realc[i,0]
while(1) :
if directionreal=='+' :
b=realc[i+1,0]
c=realc[i+2,0]
d=realc[i+3,0]
e=realc[i+4,0]
f=realc[i+5,0]
avr=[float(b[0]+c[0]+d[0]+e[0]+f[0])/5,float(b[1]+c[1]+d[1]+e[1]+f[1])/5]
i+=5
else:
b=realc[i-1,0]
c=realc[i-2,0]
d=realc[i-3,0]
e=realc[i-4,0]
f=realc[i-5,0]
avr=[float(b[0]+c[0]+d[0]+e[0]+f[0])/5,float(b[1]+c[1]+d[1]+e[1]+f[1])/5]
i-=5
if avr[0]!=a[0] and abs(float(avr[1]-a[1])/(avr[0]-a[0]))<1 :
realshoulder=a
break
a=avr
# Getting the shoulder point for model image
i=leftmodel
a=modelc[i,0]
while(1) :
if directionreal=='+' :
b=modelc[i+1,0]
c=modelc[i+2,0]
d=modelc[i+3,0]
e=modelc[i+4,0]
f=modelc[i+5,0]
avm=[float(b[0]+c[0]+d[0]+e[0]+f[0])/5,float(b[1]+c[1]+d[1]+e[1]+f[1])/5]
i+=5
else:
b=modelc[i-1,0]
c=modelc[i-2,0]
d=modelc[i-3,0]
e=modelc[i-4,0]
f=modelc[i-5,0]
avm=[float(b[0]+c[0]+d[0]+e[0]+f[0])/5,float(b[1]+c[1]+d[1]+e[1]+f[1])/5]
i-=5
if avm[0]!=a[0] and abs(float(avm[1]-a[1])/(avm[0]-a[0]))<1 :
modelshoulder=a
break
a=avm
# print realshoulder,modelshoulder
#Calculating the width of model shoulder and real shoulder
a=give_index_ct(realc,realshoulder[1])
print "realshoulder",realc[a[0],0],realc[a[1],0]
b=give_index_ct(modelc,modelshoulder[1])
print "modelshoulder",modelc[b[0],0],modelc[b[1],0]
if len(a)%2!=0 or len(b)%2!=0 :
print "some error message"
exit(1)
scaling=float(realc[a[1],0,0]-realc[a[0],0,0])/(modelc[b[1],0,0]-modelc[b[0],0,0])
print scaling
for i in args :
x=cv2.imread(i)
# print x.shape,int(x.shape[1]*scaling)
x1=cv2.resize(x,(int(float(x.shape[1])*scaling),x.shape[0]),interpolation=cv2.INTER_CUBIC)
cv2.imwrite('scaled_'+i,x1)
return scaling