def copy_file(old, new):
old_fn = os.path.join('data/lfw_funneled', old)
img = cv.imread(old_fn)
bboxes, landmarks = get_all_face_attributes(old_fn)
draw_bboxes(img, bboxes, landmarks)
cv.resize(img, (224, 224))
new_fn = os.path.join('images', new)
cv.imwrite(new_fn, img)
def compare(model, th, newimgs, srcdir, dstdir, kndatas, crop):
data = []
where = srcdir.split('/')[1]
for newimg in newimgs: #.jpg imgs
newpath = srcdir + newimg #img fullpath
is_valid, bounding_boxes, landmarks = get_all_face_attributes(
newpath) #face detector
if is_valid: #If a face exists in a new image, compare it to the existing data
with torch.no_grad():
for i, box in enumerate(
bounding_boxes
): #bounding_boxes is the coordinates of faces
if crop: # alignment face
new = get_image(newpath, landmarks[i], transformer)
else:
new = get_image(newpath, landmarks[0], transformer)
imgs = torch.zeros([2, 3, 112, 112], dtype=torch.float)
imgs = imgs.to(device) #gpu
imgs[0] = new
for kndata in kndatas:
knimg = kndata['img']
imgs[1] = knimg
output = model(imgs)
feature0 = output[0].cpu().numpy()
feature1 = output[1].cpu().numpy()
x0 = feature0 / np.linalg.norm(feature0)
x1 = feature1 / np.linalg.norm(feature1)
cosine = np.dot(x0, x1)
try:
theta = math.acos(cosine)
except ValueError:
theta = 20
theta = theta * 180 / math.pi
if theta < th:
if crop:
print('known')
break
if kndata == kndatas[-1]: #if unknown
img = Image.open(newpath)
if crop: # new images send to unknown dir
img_trim = img.crop((box[0] - 35, box[1] - 35,
box[2] + 70, box[3] + 70))
img_trim.save('{0}/{1}-{2}'.format(
dstdir, i, newimg))
data.append({
'fullpath':
'{0}/{1}-{2}'.format(dstdir, i, newimg),
'identity':
'unknown',
'img':
imgs[0]
})
else: # learn the images(family or friends)
img.save(dstdir + where + '/' + newimg)
kndatas.append({
'fullpath':
dstdir + where + '/' + newimg,
'identity':
where,
'img':
imgs[0]
})
if not crop: #The learned image is reflected in the existing data.
with open(knpkl, 'wb') as f:
pickle.dump(kndatas, f, pickle.HIGHEST_PROTOCOL)
remove_file(srcdir, newimgs)
return data