def computeMeanMain(args):
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
imgs = list(iterImgs(args.inputDir))
if args.numImages > 0:
imgs = random.sample(imgs, args.numImages)
facePoints = []
for img in imgs:
rgb = img.getRGB()
bb = align.getLargestFaceBoundingBox(rgb)
alignedPoints = align.align(rgb, bb)
if alignedPoints:
facePoints.append(alignedPoints)
facePointsNp = np.array(facePoints)
mean = np.mean(facePointsNp, axis=0)
std = np.std(facePointsNp, axis=0)
write(mean, "{}/mean.csv".format(args.modelDir))
write(std, "{}/std.csv".format(args.modelDir))
# Only import in this mode.
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(mean[:, 0], -mean[:, 1], color='k')
ax.axis('equal')
for i, p in enumerate(mean):
ax.annotate(str(i), (p[0] + 0.005, -p[1] + 0.005), fontsize=8)
plt.savefig("{}/mean.png".format(args.modelDir))
def computeMeanMain(args):
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
imgs = list(iterImgs(args.inputDir))
if args.numImages > 0:
imgs = random.sample(imgs, args.numImages)
facePoints = []
for img in imgs:
rgb = img.getRGB()
bb = align.getLargestFaceBoundingBox(rgb)
alignedPoints = align.align(rgb, bb)
if alignedPoints:
facePoints.append(alignedPoints)
facePointsNp = np.array(facePoints)
mean = np.mean(facePointsNp, axis=0)
std = np.std(facePointsNp, axis=0)
write(mean, "{}/mean.csv".format(args.modelDir))
write(std, "{}/std.csv".format(args.modelDir))
# Only import in this mode.
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(mean[:, 0], -mean[:, 1], color='k')
ax.axis('equal')
for i, p in enumerate(mean):
ax.annotate(str(i), (p[0] + 0.005, -p[1] + 0.005), fontsize=8)
plt.savefig("{}/mean.png".format(args.modelDir))
def main(args):
align = NaiveDlib(args.dlibFacePredictor)
bgrImg = cv2.imread(args.img)
if bgrImg is None:
raise Exception("Unable to load image: {}".format(args.img))
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
bb = align.getLargestFaceBoundingBox(rgbImg)
if bb is None:
raise Exception("Unable to find a face: {}".format(args.img))
landmarks = align.align(rgbImg, bb)
if landmarks is None:
raise Exception("Unable to align image: {}".format(args.img))
# alignedFace = align.alignImg("affine", args.size, rgbImg, bb, landmarks)
bl = (bb.left(), bb.bottom())
tr = (bb.right(), bb.top())
cv2.rectangle(bgrImg, bl, tr, color=(153, 255, 204), thickness=3)
for landmark in landmarks:
cv2.circle(bgrImg,
center=landmark,
radius=3,
color=(102, 204, 255),
thickness=-1)
print("Saving image to 'annotated.png'")
cv2.imwrite("annotated.png", bgrImg)
def process(code, div):
import openface
import openface.helper
import dlib
from openface.alignment import NaiveDlib # Depends on dlib.
code = int(code)
div = int(div)
dlibModelDir = os.path.join(fileDir, "./openface/models/dlib")
dlibFaceMean = os.path.join(dlibModelDir, "mean.csv")
dlibFacePredictor = os.path.join(dlibModelDir,
"shape_predictor_68_face_landmarks.dat")
align = NaiveDlib(dlibFaceMean, dlibFacePredictor)
dataset = data.Dataset()
last = time.time()
count = 0
for model, key, img in dataset.get_images(BUCKET_NAME):
if hash(key) % div == code:
bb = align.getLargestFaceBoundingBox(img)
aligned = align.alignImg("affine", 224, img, bb)
# print time.time() - last
last = time.time()
count += 1
if not aligned is None:
# print model,key,img.shape,bb,aligned.shape
cv2.imwrite(
"output/face_{}".format(
key.replace('/', '_').replace('models', '')), aligned)
# cv2.imshow("test",aligned)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# break
if count % 20 == 0 and code == 0:
local(
'aws s3 mv output/ s3://aub3data/output/ --recursive --storage-class "REDUCED_REDUNDANCY" --region "us-east-1"'
)
def process(code,div):
import openface
import openface.helper
import dlib
from openface.alignment import NaiveDlib # Depends on dlib.
code = int(code)
div = int(div)
dlibModelDir = os.path.join(fileDir, "./openface/models/dlib")
dlibFaceMean = os.path.join(dlibModelDir, "mean.csv")
dlibFacePredictor = os.path.join(dlibModelDir,"shape_predictor_68_face_landmarks.dat")
align = NaiveDlib(dlibFaceMean,dlibFacePredictor)
dataset = data.Dataset()
last = time.time()
count = 0
for model,key,img in dataset.get_images(BUCKET_NAME):
if hash(key) % div == code:
bb = align.getLargestFaceBoundingBox(img)
aligned = align.alignImg("affine", 224, img, bb)
# print time.time() - last
last = time.time()
count += 1
if not aligned is None:
# print model,key,img.shape,bb,aligned.shape
cv2.imwrite("output/face_{}".format(key.replace('/','_').replace('models','')),aligned)
# cv2.imshow("test",aligned)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# break
if count % 20 == 0 and code == 0:
local('aws s3 mv output/ s3://aub3data/output/ --recursive --storage-class "REDUCED_REDUNDANCY" --region "us-east-1"')
def process():
dlibModelDir = os.path.join(fileDir, "./openface/models/dlib")
dlibFaceMean = os.path.join(dlibModelDir, "mean.csv")
dlibFacePredictor = os.path.join(dlibModelDir,"shape_predictor_68_face_landmarks.dat")
align = NaiveDlib(dlibFaceMean,dlibFacePredictor)
dataset = data.Dataset()
for model,key,img in dataset.get_images(BUCKET_NAME):
bb = align.getLargestFaceBoundingBox(img)
aligned = align.alignImg("affine", 224, img, bb)
if not aligned is None:
print model,key,img.shape,bb,aligned.shape
cv2.imwrite("test/face_{}".format(key.replace('/','_')),aligned)
# cv2.imshow("test",aligned)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# break
else:
print "No face found"
def process():
dlibModelDir = os.path.join(fileDir, "./openface/models/dlib")
dlibFaceMean = os.path.join(dlibModelDir, "mean.csv")
dlibFacePredictor = os.path.join(dlibModelDir,
"shape_predictor_68_face_landmarks.dat")
align = NaiveDlib(dlibFaceMean, dlibFacePredictor)
dataset = data.Dataset()
for model, key, img in dataset.get_images(BUCKET_NAME):
bb = align.getLargestFaceBoundingBox(img)
aligned = align.alignImg("affine", 224, img, bb)
if not aligned is None:
print model, key, img.shape, bb, aligned.shape
cv2.imwrite("test/face_{}".format(key.replace('/', '_')), aligned)
# cv2.imshow("test",aligned)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# break
else:
print "No face found"
def main(args):
align = NaiveDlib(args.dlibFacePredictor)
bgrImg = cv2.imread(args.img)
if bgrImg is None:
raise Exception("Unable to load image: {}".format(args.img))
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
bb = align.getLargestFaceBoundingBox(rgbImg)
if bb is None:
raise Exception("Unable to find a face: {}".format(args.img))
landmarks = align.align(rgbImg, bb)
if landmarks is None:
raise Exception("Unable to align image: {}".format(args.img))
# alignedFace = align.alignImg("affine", args.size, rgbImg, bb, landmarks)
bl = (bb.left(), bb.bottom())
tr = (bb.right(), bb.top())
cv2.rectangle(bgrImg, bl, tr, color=(153, 255, 204), thickness=3)
for landmark in landmarks:
cv2.circle(bgrImg, center=landmark, radius=3, color=(102, 204, 255), thickness=-1)
print("Saving image to 'annotated.png'")
cv2.imwrite("annotated.png", bgrImg)
