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 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)
