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Improved logic for iterating over 'pairs.txt' #64

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Improved logic for iterating over 'pairs.txt' #64

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b3db44b
Improved logic for iterating over 'pairs.txt'
sayandipdutta Feb 1, 2022
3a4c230
Change inner loop to use enumerate
sayandipdutta Feb 1, 2022
762c458
Add enumerate in inner loop
sayandipdutta Feb 1, 2022
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62 changes: 32 additions & 30 deletions lfw_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,36 +85,38 @@ def find_best_threshold(thresholds, predicts):
landmark[l[0]] = [int(k) for k in l[1:]]

with open('data/pairs.txt') as f:
pairs_lines = f.readlines()[1:]

for i in range(6000):
p = pairs_lines[i].replace('\n','').split('\t')

if 3==len(p):
sameflag = 1
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
if 4==len(p):
sameflag = 0
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))

img1 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name1),np.uint8),1),landmark[name1])
img2 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name2),np.uint8),1),landmark[name2])

imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
for i in range(len(imglist)):
imglist[i] = imglist[i].transpose(2, 0, 1).reshape((1,3,112,96))
imglist[i] = (imglist[i]-127.5)/128.0

img = np.vstack(imglist)
img = Variable(torch.from_numpy(img).float(),volatile=True).cuda()
output = net(img)
f = output.data
f1,f2 = f[0],f[2]
cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))

_ = next(f, None) # skip header

for line_no, line in enumerate(f, start=1):
p = line.replace('\n','').split('\t')

if 3==len(p):
sameflag = 1
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
if 4==len(p):
sameflag = 0
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))

img1 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name1),np.uint8),1),landmark[name1])
img2 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name2),np.uint8),1),landmark[name2])

imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
for i, image in enumerate(imglist):
image = image.transpose(2, 0, 1).reshape((1,3,112,96))
imglist[i] = (image - 127.5) / 128.0

img = np.vstack(imglist)
img = Variable(torch.from_numpy(img).float(),volatile=True).cuda()
output = net(img)
f = output.data
f1,f2 = f[0],f[2]
cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))

if line_no >= 6000: # break as soon as 6000 lines have been processed.
break

accuracy = []
thd = []
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