def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
outDir = os.path.join(args.outputDir, imgObject.cls)
imgName = "{}/{}.png".format(outDir, imgObject.name)
openface.helper.mkdirP(outDir)
if not os.path.isfile(imgName):
rgb = imgObject.getRGB(cache=False)
out = align.alignImg(args.method, args.size, rgb,
outputPrefix=outDir,
outputDebug=args.outputDebugImages)
if args.fallbackLfw and out is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(os.path.join(args.fallbackLfw,
imgObject.cls),
imgObject.name)
shutil.copy(deepFunneled, "{}/{}.jpg".format(os.path.join(args.outputDir,
imgObject.cls),
imgObject.name))
if out is not None:
io.imsave(imgName, out)
print('nFallbacks:', nFallbacks)
def alignMain(self):
""" ***** """
warnings.filterwarnings(action='ignore', category=DeprecationWarning)
openface.helper.mkdirP(self.__aligned_images_dir)
imgs = list(iterImgs(self.__traininig_images_dir))
random.shuffle(imgs)
for imgObject in imgs:
out_dir = os.path.join(self.__aligned_images_dir, imgObject.cls)
openface.helper.mkdirP(out_dir)
outputPrefix = os.path.join(out_dir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" {} Already found, skipping.".format(imgName))
else:
rgb = imgObject.getRGB()
if rgb is None:
print(" {} Unable to load.".format(imgName))
outRgb = None
else:
outRgb = self.__align.align(AlignAndXformFaces.__Image_dim,
rgb,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE,
skipMulti=True)
if outRgb is None:
print(" {} Unable to align.".format(imgName))
if outRgb is not None:
print(" {} Writing aligned file to disk.".format(imgName))
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
return
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
if args.landmarks == 'outerEyesAndNose':
landmarkIndices = openface.AlignDlib.OUTER_EYES_AND_NOSE
elif args.landmarks == 'innerEyesAndBottomLip':
landmarkIndices = openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
else:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
align = openface.AlignDlib(args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(args.outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
if args.verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
if args.verbose:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(args.size,
rgb,
landmarkIndices=landmarkIndices,
skipMulti=args.skipMulti)
if outRgb is None and args.verbose:
print(" + Unable to align.")
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(
os.path.join(args.fallbackLfw, imgObject.cls),
imgObject.name)
shutil.copy(
deepFunneled, "{}/{}.jpg".format(
os.path.join(args.outputDir, imgObject.cls),
imgObject.name))
if outRgb is not None:
if args.verbose:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
def computeMeanMain(args):
align = openface.AlignDlib(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 align_faces_dlib():
openface.helper.mkdirP(img_train_aligned_path)
images = list(iterImgs(img_train_raw_path))
align_dlib = openface.AlignDlib(dlib_face_predictor)
for image in images:
aligned_person_face_path = os.path.join(img_train_aligned_path,
image.cls)
openface.helper.mkdirP(aligned_person_face_path)
aligned_numbered_person_face_path = os.path.join(
aligned_person_face_path, image.name)
aligned_image_name = aligned_numbered_person_face_path + ".png"
image_rgb = image.getRGB()
if image_rgb is None:
print("Unable to load image {}").format(image.name)
aligned_image_rgb = None
else:
aligned_image_rgb = align_dlib.align(
aligned_image_size,
image_rgb,
landmarkIndices=align_landmark_indices,
skipMulti=align_images_with_multiple_faces)
if aligned_image_rgb is None:
print("Unable to align image {}").format(image.name)
if aligned_image_rgb is not None:
aligned_image_bgr = cv2.cvtColor(aligned_image_rgb,
cv2.COLOR_RGB2BGR)
cv2.imwrite(aligned_image_name, aligned_image_bgr)
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if args.landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
landmarkIndices = landmarkMap[args.landmarks]
align = openface.AlignDlib(args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(args.outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
if args.verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
if args.verbose:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(args.size, rgb,
landmarkIndices=landmarkIndices,
skipMulti=args.skipMulti)
if outRgb is None and args.verbose:
print(" + Unable to align.")
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(os.path.join(args.fallbackLfw,
imgObject.cls),
imgObject.name)
shutil.copy(deepFunneled, "{}/{}.jpg".format(os.path.join(args.outputDir,
imgObject.cls),
imgObject.name))
if outRgb is not None:
if args.verbose:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
def align_faces_dlib():
openface.helper.mkdirP(img_train_aligned_path)
images = list(iterImgs(img_train_raw_path))
align_dlib = openface.AlignDlib(dlib_face_predictor)
for image in images:
aligned_person_face_path = os.path.join(img_train_aligned_path, image.cls)
openface.helper.mkdirP(aligned_person_face_path)
aligned_numbered_person_face_path = os.path.join(aligned_person_face_path, image.name)
aligned_image_name = aligned_numbered_person_face_path + ".png"
image_rgb = image.getRGB()
if image_rgb is None:
print("Unable to load image {}").format(image.name)
aligned_image_rgb = None
else:
aligned_image_rgb = align_dlib.align(aligned_image_size, image_rgb, landmarkIndices=align_landmark_indices, skipMulti = align_images_with_multiple_faces)
if aligned_image_rgb is None:
print("Unable to align image {}").format(image.name)
if aligned_image_rgb is not None:
aligned_image_bgr = cv2.cvtColor(aligned_image_rgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(aligned_image_name, aligned_image_bgr)
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 align_images(self, input_dir):
imgs = list(iterImgs(input_dir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
for imgObject in imgs:
outDir = os.path.join(self.aligned_dir, imgObject.cls)
if not os.path.isdir(outDir):
os.makedirs(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if not os.path.isfile(imgName):
rgb = imgObject.getRGB()
if rgb is None:
outRgb = None
else:
outRgb = self.align.align(
self.imgDim, rgb, landmarkIndices=self.landmarkIndices)
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
logger.info("Write image {}".format(imgName))
else:
os.remove(imgObject.path)
logger.warn("No face was detected in {}. Removed.".format(
imgObject.path))
def align_images(self, input_dir):
imgs = list(iterImgs(input_dir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
for imgObject in imgs:
outDir = os.path.join(self.aligned_dir, imgObject.cls)
if not os.path.isdir(outDir):
os.makedirs(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if not os.path.isfile(imgName):
rgb = imgObject.getRGB()
if rgb is None:
outRgb = None
else:
outRgb = self.align.align(
self.imgDim, rgb,
landmarkIndices=self.landmarkIndices)
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
logger.info("Write image {}".format(imgName))
else:
os.remove(imgObject.path)
logger.warn("No face was detected in {}. Removed.".format(imgObject.path))
else:
logger.debug("Skip image {}".format(imgName))
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
outDir = os.path.join(args.outputDir, imgObject.cls)
imgName = "{}/{}.png".format(outDir, imgObject.name)
openface.helper.mkdirP(outDir)
if not os.path.isfile(imgName):
rgb = imgObject.getRGB(cache=False)
out = align.alignImg(args.method, args.size, rgb)
if args.fallbackLfw and out is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(
os.path.join(args.fallbackLfw, imgObject.cls),
imgObject.name)
shutil.copy(
deepFunneled, "{}/{}.jpg".format(
os.path.join(args.outputDir, imgObject.cls),
imgObject.name))
if out is not None:
io.imsave(imgName, out)
print('nFallbacks:', nFallbacks)
def detectAlignImages(args):
pool = ThreadPool(5)
imgs = list(iterImgs(args.input))
imgs.sort(key=lambda x: x.path)
print("All images is listed")
aligner = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
bundles = []
current_class = None
class_num = -1
for imgObject in imgs:
if imgObject.cls != current_class:
class_num += 1
current_class = imgObject.cls
#descr.write(os.path.join(imgObject.cls, imgObject.name) + " " + str(class_num) + "\n")
bundles.append((args, imgObject, aligner, class_num))
#print("Description is generated")
exists = pool.map(alignImage, bundles)
pool.close()
pool.join()
with open(os.path.join(args.output, 'description.txt'), 'w') as descr:
for (exist, obj) in zip(exists, bundles):
if exist:
descr.write(
os.path.join(obj[1].cls, obj[1].name) + " " + str(obj[3]) +
"\n")
def alignMain(outputDir,inputDir,landmarks,dlibFacePredictor,verbose,size,skipMulti):
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix
if os.path.isfile(imgName):
if verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
if verbose:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(size, rgb,
landmarkIndices=landmarkIndices,
skipMulti=skipMulti)
if outRgb is None and verbose:
print(" + Unable to align.")
# if args.fallbackLfw and outRgb is None:
# nFallbacks += 1
# deepFunneled = "{}/{}.jpg".format(os.path.join(args.fallbackLfw,
# imgObject.cls),
# imgObject.name)
# shutil.copy(deepFunneled, "{}/{}.jpg".format(os.path.join(outputDir,
# imgObject.cls),
# imgObject.name))
if outRgb is not None:
if verbose:
print(" + Writing aligned file to disk.")
for i,rgb in enumerate(outRgb):
outBgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName+str(i)+ ".png", outBgr)
def alignMain(outputDir, inputDir, landmark="outerEyesAndNose"):
"""function that performs the alignment of faces in the input directory using the provided landmarks and writes the aligned faces to disk in the provided outputDir
paramters: outputDir, inputDir, landmark
type parameter: string, string, string
return: aligned files are written to disk"""
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmark not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmark))
landmarkIndices = landmarkMap[landmark]
align = openface.AlignDlib("shape_predictor_68_face_landmarks.dat")
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(96, rgb,
landmarkIndices=landmarkIndices,
skipMulti=True)
if outRgb is None:
print(" + Unable to align.")
if outRgb is not None:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def alignMain(inputDir, output, landmarks, dlibFacePredictor, size):
# input_save("input/")
imgs = list(iterImgs(inputDir))
name = inputDir.split("/")[1]
output = os.path.join(output, name)
# print name
# print output
# exit(-1)
openface.helper.mkdirP(output)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
# innerEyesAndBottomLip daha duzgun aliyor.
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
path = "face/hepsi/" + str(imgObject) + ".png"
# print("=== {} ===".format(imgObject.path))
outDir = os.path.join(output, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
# print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(size,
rgb,
landmarkIndices=landmarkIndices)
if outRgb is None:
print(" + Unable to align.")
if outRgb is not None:
# print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
if args.landmarks == 'outerEyesAndNose':
landmarkIndices = NaiveDlib.OUTER_EYES_AND_NOSE
elif args.landmarks == 'innerEyesAndBottomLip':
landmarkIndices = NaiveDlib.INNER_EYES_AND_BOTTOM_LIP
else:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
align = NaiveDlib(args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
outDir = os.path.join(args.outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if not os.path.isfile(imgName):
rgb = imgObject.getRGB()
if rgb is not None:
print(imgName, type(rgb), rgb.shape)
outRgb = align.alignImg('affine',
args.size,
rgb,
landmarkIndices=landmarkIndices)
else:
outRgb = None
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(
os.path.join(args.fallbackLfw, imgObject.cls),
imgObject.name)
shutil.copy(
deepFunneled, "{}/{}.jpg".format(
os.path.join(args.outputDir, imgObject.cls),
imgObject.name))
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
def align_images(input_path, output_path, processes=3, size=96):
# not available in all opencv 2.4.x versions
try:
# since 2.4.13 opencv supports multithreading out of the box
# this does not play well with multiprocessing of python
# we need multiprocessing to spread the load of image alignment to all the cores
# as python threading is limited to one core only
cv2.setNumThreads(0)
except AttributeError:
pass
openface.helper.mkdirP(output_path)
imgs = list(iterImgs(input_path))
m = multiprocessing.Manager()
workers = m.Queue()
results = m.Queue()
# fill the worker queue before starting the processes as the workers
# will exit in case the queue is empty
logger.info('preparing images')
for imgObject in imgs:
outDir = os.path.join(output_path, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
rgb = imgObject.getRGB()
workers.put((imgName, rgb))
threads = []
logger.info('running workers')
for i in range(processes):
t = AlignWorker(workers, results, size)
t.start()
threads.append(t)
workers_done = 0
while not workers_done == processes:
logger.info("{0} images left...".format(workers.qsize()))
while not results.empty():
imgName, outRgb = results.get(block=True)
# the last message a worker will send is a 'None' image to signal it is done
# as the worker runs in another process you cannot access ressources/properties
# of the worker directly
if imgName is None:
workers_done += 1
elif outRgb is not None:
# dlib works with RGB but opencv requires BGR order for writing images
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
time.sleep(0.5)
def gen_data(self):
face_reps = []
labels = []
reps_fname = "{}/reps.csv".format(self.aligned_dir)
label_fname = "{}/labels.csv".format(self.aligned_dir)
for imgObject in iterImgs(self.aligned_dir):
reps = self.net.forward(imgObject.getRGB())
face_reps.append(reps)
labels.append((imgObject.cls, imgObject.name))
if face_reps and labels and not self.stop_training.is_set():
pd.DataFrame(face_reps).to_csv(reps_fname, header=False, index=False)
pd.DataFrame(labels).to_csv(label_fname, header=False, index=False)
logger.info("Generated label file {}".format(label_fname))
logger.info("Generated representation file {}".format(reps_fname))
def align(args, multi):
# Aligning now
start = time.time()
mkdirP(alignedDir)
imgs = list(iterImgs(trainingDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmarks not in landmarkMap:
# TODO: Avoid exceptions, find way to silently fail and skip image
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(alignedDir, imgObject.cls)
mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
# TODO: output is still PNG?
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
if args.verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
if args.verbose:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(imgSize, rgb,
landmarkIndices=landmarkIndices,
skipMulti=not multi)
if outRgb is None and args.verbose:
print(" + Unable to align.")
if outRgb is not None:
if args.verbose:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def gen_data(self):
face_reps = []
labels = []
reps_fname = "{}/reps.csv".format(self.aligned_dir)
label_fname = "{}/labels.csv".format(self.aligned_dir)
for imgObject in iterImgs(self.aligned_dir):
reps = self.net.forward(imgObject.getRGB())
face_reps.append(reps)
labels.append((imgObject.cls, imgObject.name))
if face_reps and labels and not self.stop_training.is_set():
pd.DataFrame(face_reps).to_csv(reps_fname,
header=False,
index=False)
pd.DataFrame(labels).to_csv(label_fname, header=False, index=False)
logger.info("Generated label file {}".format(label_fname))
logger.info("Generated representation file {}".format(reps_fname))
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
if args.landmarks == 'outerEyesAndNose':
landmarkIndices = NaiveDlib.OUTER_EYES_AND_NOSE
elif args.landmarks == 'innerEyesAndBottomLip':
landmarkIndices = NaiveDlib.INNER_EYES_AND_BOTTOM_LIP
else:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
align = NaiveDlib(args.dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
outDir = os.path.join(args.outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if not os.path.isfile(imgName):
rgb = imgObject.getRGB()
if rgb is not None:
print(imgName, type(rgb), rgb.shape)
outRgb = align.alignImg('affine', args.size, rgb,
landmarkIndices=landmarkIndices)
else:
outRgb = None
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(os.path.join(args.fallbackLfw,
imgObject.cls),
imgObject.name)
shutil.copy(deepFunneled, "{}/{}.jpg".format(os.path.join(args.outputDir,
imgObject.cls),
imgObject.name))
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
def alignMain(inputDir, outputDir, landmarks, dlibFacePredictor, size):
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(size,
rgb,
landmarkIndices=landmarkIndices)
if outRgb is None:
print(" + Unable to align.")
if outRgb is not None:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def detectAlignImages(args):#input_dir, output_dir):
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
imgs = list(iterImgs(args.input))
aligner = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
futures = []
for imgObject in imgs:
future = executor.submit(alignImage, args, imgObject, aligner)
futures.append(future)
count_success = 0
n = len(futures)
#for future in concurrent.futures.as_completed(futures):
for future in futures:
future.result()
count_success += 1
if count_success % 1000 == 0:
print("{} photo resized from {}".format(count_success, n))
def generate(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
s = ''
i = 0
j = 0
label = []
rep = []
for imgObject in imgs:
#print("=== {} ===".format(imgObject.path))
y = imgObject.path.split('/')
img = cv2.imread(imgObject.path)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = cv2.resize(img, (96, 96), interpolation=cv2.INTER_LINEAR)
img = np.transpose(img, (2, 0, 1))
img = img.astype(np.float32) / 255.0
input = torch.from_numpy(img).unsqueeze(0)
output = model(Variable(input))
output = output.data.numpy()
rep.append(output[0, :])
if s != y[2]:
i += 1
s = y[2]
g = [i, imgObject.path]
label.append(g)
j += 1
if j % 50 == 0:
print("represented " + str(j) + "/" + str(len(imgs)))
label = np.array(label)
d = rep[1] - rep[0]
print(np.dot(d, d))
rep = np.array(rep)
print(np.shape(rep))
la = pd.DataFrame(label)
la.to_csv(args.outputDir + "/labels1.csv",
mode='w',
index=False,
header=False)
re = pd.DataFrame(rep)
re.to_csv(args.outputDir + "/reps1.csv",
mode='w',
index=False,
header=False)
def alignMain(inputDir,outputDir,landmarks,dlibFacePredictor,size):
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
nFallbacks = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
print(" + Unable to load.")
outRgb = None
else:
outRgb = align.align(size, rgb, landmarkIndices=landmarkIndices)
if outRgb is None:
print(" + Unable to align.")
if outRgb is not None:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def align_images(self, input_dir):
imgs = list(iterImgs(input_dir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
for imgObject in imgs:
outDir = os.path.join(self.aligned_dir, imgObject.cls)
if not os.path.isdir(outDir):
os.makedirs(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if not os.path.isfile(imgName):
logger.info("Aligning image %s", imgName)
try:
self.align_image(imgObject, imgName)
except Exception as ex:
logger.error(ex)
else:
logger.info("Skip existing aligned image %s", imgName)
def gen_data(self):
face_reps = []
labels = []
reps_fname = "{}/reps.csv".format(self.classifier_dir)
label_fname = "{}/labels.csv".format(self.classifier_dir)
imgs = list(iterImgs(self.aligned_dir))
n_total = len(imgs)
for i, imgObject in enumerate(imgs, 1):
reps = self.net.forward(imgObject.getRGB())
face_reps.append(reps)
labels.append((imgObject.cls, imgObject.name))
logger.info("{}/{}".format(i, n_total))
if face_reps and labels:
pd.DataFrame(face_reps).to_csv(reps_fname, header=False, index=False)
pd.DataFrame(labels).to_csv(label_fname, header=False, index=False)
logger.info("Generated label file {}".format(label_fname))
logger.info("Generated representation file {}".format(reps_fname))
def align_face(im_size, inputDir, outputDir='data/aligned_dataset'):
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarks = 'innerEyesAndBottomLip'
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor)
for imgObject in imgs:
print "Aligned images: {}".format(imgObject.path)
ext = imgObject.path.split('.')[-1]
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + "." + ext
rgb = imgObject.getRGB()
if rgb is None:
outRgb = None
else:
outRgb = align.align(im_size,
rgb,
landmarkIndices=landmarkIndices,
skipMulti=True)
if outRgb is None:
shutil.copyfile(imgObject.path, imgName)
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
def generate(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
sock.sendall((str(len(imgs)) + '\n').encode())
j = 0
label = []
rep = []
for imgObject in imgs:
#print("=== {} ===".format(imgObject.path))
#y = imgObject.path.split('/')
#print(imgObject.path)
#print(y)
img = cv2.imread(imgObject.path)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = cv2.resize(img, (96, 96), interpolation=cv2.INTER_LINEAR)
img = np.transpose(img, (2, 0, 1))
img = img.astype(np.float32) / 255.0
input = torch.from_numpy(img).unsqueeze(0)
output = model(Variable(input))
output = output.data.numpy()
rep.append(output[0, :])
sock.sendall('added\n'.encode())
g = [args.name, imgObject.path]
label.append(g)
j += 1
if j % 50 == 0:
print("represented " + str(j) + "/" + str(len(imgs)))
sock.sendall(
("represented " + str(j) + "/" + str(len(imgs))).encode())
label = np.array(label)
rep = np.array(rep)
#print(np.shape(rep))
la = pd.DataFrame(label)
outfile = open(args.outputDir + "/labels.csv", mode='a')
la.to_csv(outfile, index=False, header=False)
outfile.close()
re = pd.DataFrame(rep)
outfile = open(args.outputDir + "/reps.csv", mode='a')
re.to_csv(outfile, index=False, header=False)
outfile.close()
parser = argparse.ArgumentParser()
parser.add_argument('imgDir', type=str, help="Input image directory.")
parser.add_argument('--numImages', type=int, default=1000)
parser.add_argument('--model',
type=str,
help="TODO",
default="./models/openface/nn4.v1.t7")
parser.add_argument('--outputFile',
type=str,
help="Output file, stored in numpy serialized format.",
default="./unknown.npy")
parser.add_argument('--imgDim',
type=int,
help="Default image size.",
default=96)
args = parser.parse_args()
align = NaiveDlib("models/dlib/", "shape_predictor_68_face_landmarks.dat")
openface = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
allImgs = list(iterImgs(args.imgDir))
imgObjs = random.sample(allImgs, args.numImages)
reps = []
for imgObj in imgObjs:
rep = openface.forward(imgObj.path)
rep = np.array(rep)
reps.append(rep)
np.save(args.outputFile, np.row_stack(reps))
def generate(inputDir):
imgs = []
for i in range(0, len(inputDir)):
img = list(iterImgs(inputDir[i]))
imgs += img
sock.sendall('length\n'.encode())
sock.sendall((str(len(imgs)) + '\n').encode())
print(len(imgs))
i = 0
j = 0
label = []
rep = []
for imgObject in imgs:
#print("=== {} ===".format(imgObject.path))
img = cv2.imread(imgObject.path)
if img is None:
sock.sendall('unable to load\n'.encode())
continue
width = img.shape[1]
height = img.shape[0]
check = False
if width > 600:
width = 600
check = True
if height > 600:
height = 600
check = True
if check:
img = cv2.resize(img, (width, height),
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = cv2.resize(img, (96, 96), interpolation=cv2.INTER_LINEAR)
img = np.transpose(img, (2, 0, 1))
img = img.astype(np.float32) / 255.0
input = torch.from_numpy(img).unsqueeze(0)
output = model(Variable(input))
output = output.data.numpy()
rep.append(output[0, :])
sock.sendall('one complete\n'.encode())
label.append(imgObject.path)
j += 1
if j % 50 == 0:
print("represented " + str(j) + "/" + str(len(imgs)))
label = np.array(label)
images = []
for i in range(0, len(rep)):
temp = [label[i]]
for j in range(i + 1, len(rep)):
d = rep[j] - rep[i]
d = np.dot(d, d)
if d <= 0.02:
temp.append(label[j])
if len(temp) > 1:
images.append(temp)
sock.sendall('one complete\n'.encode())
'''d = rep[1]-rep[0]
print(np.shape(rep[1]))
print(np.dot(d,d))
rep = np.array(rep)
print(np.shape(rep))
la = pd.DataFrame(label)
la.to_csv(args.outputDir+"/labels1.csv", mode='w', index=False, header=False)
re = pd.DataFrame(rep)
re.to_csv(args.outputDir+"/reps1.csv", mode='w', index=False, header=False)
'''
images = pd.DataFrame(images)
images.to_csv("tmp.csv", mode='w', index=False, header=False)
import openface
import openface.helper
from openface.data import iterImgs
openface.helper.mkdirP(
'./aligned-images/'
) #'/aligned-images/ --> Out Directory for storing aligned images
# Model used to predict the 68 face landmarks
dlibFacePredictor = './models/shape_predictor_68_face_landmarks.dat'
#Defining an object of AlignDlib class
align = openface.AlignDlib(dlibFacePredictor)
#Iterating among all the images
imgs = list(iterImgs('./training-images'))
#Shuffling the Images
random.shuffle(imgs)
nFallbacks = 0
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
landmarks = 'outerEyesAndNose' # Default, can also take 'innerEyesAndBottomLip'
landmarkIndices = landmarkMap[landmarks]
#Iterating through images one-by-one
for imgObject in imgs:
def processFaces(args, imgDirPath):
imgs = list(iterImgs(imgDirPath))
return imgs[0].getBGR()
def alignMain(args):
openface.helper.mkdirP(args.outputDir)
imgs = list(iterImgs(args.inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if args.landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
landmarkIndices = landmarkMap[args.landmarks]
align = openface.AlignDlib(args.dlibFacePredictor)
nFallbacks = 0
failed_images = []
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(args.outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
if args.verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None:
if args.verbose:
print(" + Unable to load.")
outRgb = None
else:
if args.aligned:
outRgb = align.align(args.size,
rgb,
landmarkIndices=landmarkIndices,
skipMulti=args.skipMulti)
else:
outRgb = align.align(args.size,
rgb,
skipMulti=args.skipMulti)
if outRgb is None and args.verbose:
print(" + Unable to align.")
try:
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
res = cv2.resize(rgb, (args.size, args.size),
interpolation=cv2.INTER_CUBIC)
if args.rgb:
outBgr = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
else:
outBgr = cv2.cvtColor(res, cv2.COLOR_RGB2GRAY)
cv2.imwrite(imgName, outBgr)
except:
failed_images.append(imgName)
if outRgb is not None:
if args.verbose:
print(" + Writing aligned file to disk.")
if args.rgb:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
else:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2GRAY)
cv2.imwrite(imgName, outBgr)
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
print(failed_images)
def find_landmarks(inputDir, outputDir, landmarks='outerEyesAndNose', size=96, useCNN=True, skipMulti=False, verbose=True):
''' Align all faces chips from folder inputDir and save output to outputDir '''
fileDir = '/media/tunguyen/Devs/DeepLearning/FaceReg/openface'
modelDir = os.path.join(fileDir, 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')
# openfaceModelDir = os.path.join(modelDir, 'openface')
dlibFacePredictor = os.path.join(
dlibModelDir, "shape_predictor_68_face_landmarks.dat")
# dlibFacePredictor = os.path.join(
# dlibModelDir, "shape_predictor_5_face_landmarks.dat")
cnnDetectModel = os.path.join(dlibModelDir, "mmod_human_face_detector.dat")
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP,
'5_landmarks': openface.AlignDlib.BASIC_5_POINTS
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
if "predictor_5" in dlibFacePredictor:
landmarks = '5_landmarks'
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor, cnnDetectModel)
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
#outDir = os.path.join(outputDir, imgObject.cls)
# openface.helper.mkdirP(outDir)
outDir = outputDir
outputPrefix = os.path.join(outDir, imgObject.name)
imgPath = outputPrefix + ".png"
print('\n'+outDir)
if os.path.isfile(imgPath):
# if 1 == 0:
if verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
#bgr_img = cv2.imread(imgObject.path)
#rgb = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
if rgb is None:
if verbose:
print(" + Unable to load.")
outRgb = None
else:
if "predictor_68" in dlibFacePredictor:
outRgb, landmarks = align.align(size, rgb,
landmarkIndices=landmarkIndices,
skipMulti=skipMulti,
useCNN=useCNN)
else:
outRgb, landmarks = align.align(size, rgb,
useCNN=useCNN,
landmarksNum=5)
if outRgb is None and verbose:
print(" + Unable to align.")
# print(outRgb)
if outRgb is not None and landmarks is not None:
if verbose:
print(" + Writing aligned file to disk.")
print(landmarks)
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
# cv2.imwrite(imgPath, outBgr)
topy = landmarks[51][1] - 10
rightx = landmarks[54][0] + 8
leftx = landmarks[48][0] - 8
bottomy = landmarks[58][1]
cv2.rectangle(outBgr,(leftx, topy),(rightx,bottomy),(0,0,0),-1)
# for i in range(0, 68):
# if i in [48, 58, 54, 51]:
# cv2.putText(outBgr,
# text=str(i),
# org=(landmarks[i][0], landmarks[i][1]),
# fontFace=cv2.FONT_HERSHEY_SIMPLEX,
# fontScale=0.2,
# color=(255, 0, 0),
# thickness=1,
# lineType=2)
cv2.imwrite(imgPath, outBgr)
def alignMain(args):
openface.helper.mkdirP(args.outDir)
check = os.listdir(os.path.normpath(args.outDir)).__contains__(args.name)
print(check)
sock.sendall((str(check) + "\n").encode())
if not check:
os.mkdir(os.path.join(args.outDir, args.name))
else:
shutil.rmtree(os.path.join(args.outDir, args.name))
os.mkdir(os.path.join(args.outDir, args.name))
print('directory created')
sock.sendall('directory created\n'.encode())
imgs = list(iterImgs(args.inputDir))
sock.sendall("length\n".encode())
sock.sendall((str(len(imgs) * 3) + "\n").encode())
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP
}
if args.landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(args.landmarks))
landmarkIndices = landmarkMap[args.landmarks]
align = openface.AlignDlib(args.dlibFacePredictor)
nFallbacks = 0
cnt = 0
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
s = os.path.normpath(args.inputDir) + '/' + imgObject.name
img = cv2.imread(s)
if img is None:
sock.sendall(" + Unable to load.\n".encode())
sock.send("processed\n".encode())
width = img.shape[1]
height = img.shape[0]
check = False
if width > 600:
width = 600
check = True
if height > 600:
height = 600
check = True
if check:
img = cv2.resize(img, (width, height),
interpolation=cv2.INTER_LINEAR)
cv2.imwrite(
os.path.normpath(args.inputDir) + '/1' + imgObject.name, img)
im = Image.open(
os.path.normpath(args.inputDir) + '/1' + imgObject.name)
os.remove(os.path.normpath(args.inputDir) + '/1' + imgObject.name)
contrast = ImageEnhance.Contrast(im)
bright = ImageEnhance.Brightness(im)
sock.sendall(("=== {} ===\n".format(imgObject.path)).encode())
outDir = os.path.join(args.outDir, args.name)
#outDir = os.path.join(args.outDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir,
os.path.splitext(imgObject.name)[0])
j = 0
i = 0.7
t = False
for k in range(1, 3, 1):
for l in range(1, 4):
if k != 1 and l == 1:
continue
elif t:
im = Image.open(s)
contrast = ImageEnhance.Contrast(im)
bright = ImageEnhance.Brightness(im)
if k == 1:
imgName = outputPrefix + ".png"
elif l == 2:
imgObject = contrast.enhance(i)
imgObject = np.array(imgObject)
imgName = outputPrefix + "_" + str(j) + ".png"
elif l == 3:
imgObject = bright.enhance(i)
imgObject = np.array(imgObject)
imgName = outputPrefix + "_" + str(j + 1) + ".png"
if k == 1:
rgb = imgObject.getRGB()
print(type(rgb))
else:
rgb = imgObject
cnt = cnt + 1
sock.sendall(("cnt " + str(cnt) + "\n").encode())
if rgb is None:
if args.verbose:
print(" + Unable to load.")
sock.sendall(" + Unable to load.\n".encode())
sock.send("processed\n".encode())
outRgb = None
else:
outRgb = align.align(args.size,
rgb,
landmarkIndices=landmarkIndices,
skipMulti=args.skipMulti)
if outRgb is None and args.verbose:
print(" + Unable to align." + " " + str(k) + " " +
str(l))
sock.sendall(" + Unable to align.\n".encode())
sock.send("processed\n".encode())
if args.fallbackLfw and outRgb is None:
nFallbacks += 1
deepFunneled = "{}/{}.jpg".format(
os.path.join(args.fallbackLfw, imgObject.cls),
imgObject.name)
shutil.copy(
deepFunneled, "{}/{}.jpg".format(
os.path.join(args.outDir, imgObject.cls),
imgObject.name))
if outRgb is not None:
if args.verbose:
print(" + Writing aligned file to disk.")
sock.sendall(
" + Writing aligned file to disk.\n".encode())
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgName, outBgr)
sock.sendall("processed\n".encode())
if k == 1:
x = outputPrefix + ".png"
s = x
t = True
sock.sendall("write to index\n".encode())
sock.sendall((x + '\n').encode())
break
if k != 1:
i += 0.3
j += 2
if args.fallbackLfw:
print('nFallbacks:', nFallbacks)
def prebatchAlign(num_processes, args, multi):
mkdirP(alignedDir)
imgs = list(iterImgs(trainingDir))
np.random.shuffle(imgs)
div_arrays = divideArray(imgs, num_processes)
return div_arrays
def align_face(inputDir, outputDir, landmarks='outerEyesAndNose', size=96, useCNN=True, skipMulti=False, verbose=True):
''' Align all faces chips from folder inputDir and save output to outputDir '''
fileDir = '/media/tunguyen/Devs/DeepLearning/FaceReg/openface'
modelDir = os.path.join(fileDir, 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')
# openfaceModelDir = os.path.join(modelDir, 'openface')
# dlibFacePredictor = os.path.join(
# dlibModelDir, "shape_predictor_68_face_landmarks.dat")
dlibFacePredictor = os.path.join(
dlibModelDir, "shape_predictor_5_face_landmarks.dat")
cnnDetectModel = os.path.join(dlibModelDir, "mmod_human_face_detector.dat")
openface.helper.mkdirP(outputDir)
imgs = list(iterImgs(inputDir))
# Shuffle so multiple versions can be run at once.
random.shuffle(imgs)
landmarkMap = {
'outerEyesAndNose': openface.AlignDlib.OUTER_EYES_AND_NOSE,
'innerEyesAndBottomLip': openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP,
'5_landmarks': openface.AlignDlib.BASIC_5_POINTS
}
if landmarks not in landmarkMap:
raise Exception("Landmarks unrecognized: {}".format(landmarks))
if "predictor_5" in dlibFacePredictor:
landmarks = '5_landmarks'
landmarkIndices = landmarkMap[landmarks]
align = openface.AlignDlib(dlibFacePredictor, cnnDetectModel)
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
#outDir = os.path.join(outputDir, imgObject.cls)
# openface.helper.mkdirP(outDir)
outDir = outputDir
outputPrefix = os.path.join(outDir, imgObject.name)
imgPath = outputPrefix + ".png"
print('\n'+outDir)
if os.path.isfile(imgPath):
if verbose:
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
#bgr_img = cv2.imread(imgObject.path)
#rgb = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
if rgb is None:
if verbose:
print(" + Unable to load.")
outRgb = None
else:
if "predictor_68" in dlibFacePredictor:
outRgb, _ = align.align(size, rgb,
landmarkIndices=landmarkIndices,
skipMulti=skipMulti,
useCNN=useCNN)
else:
outRgb, _ = align.align(size, rgb,
useCNN=useCNN,
landmarksNum=5)
if outRgb is None and verbose:
print(" + Unable to align.")
# print(outRgb)
if outRgb is not None:
if verbose:
print(" + Writing aligned file to disk.")
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(imgPath, outBgr)
from openface.alignment import NaiveDlib
from openface.data import iterImgs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('imgDir', type=str, help="Input image directory.")
parser.add_argument('--numImages', type=int, default=1000)
parser.add_argument('--model', type=str, help="TODO",
default="./models/openface/nn4.v1.t7")
parser.add_argument('--outputFile', type=str,
help="Output file, stored in numpy serialized format.",
default="./unknown.npy")
parser.add_argument('--imgDim', type=int, help="Default image size.",
default=96)
args = parser.parse_args()
align = NaiveDlib("models/dlib/",
"shape_predictor_68_face_landmarks.dat")
openface = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
allImgs = list(iterImgs(args.imgDir))
imgObjs = random.sample(allImgs, args.numImages)
reps = []
for imgObj in imgObjs:
rep = openface.forward(imgObj.path)
rep = np.array(rep)
reps.append(rep)
np.save(args.outputFile, np.row_stack(reps))
cv2.waitKey(50) & 0xFF
if box:
if num > 0:
cv2.imwrite(output_path, frame)
num = num + 1
else:
num = num + 1
continue
capture1.release()
cv2.destroyAllWindows()
os.path.split(output_path)[0]
imgs = list(iterImgs(inputDir))
random.shuffle(imgs)
for imgObject in imgs:
print("=== {} ===".format(imgObject.path))
outDir = os.path.join(outputDir, imgObject.cls)
openface.helper.mkdirP(outDir)
outputPrefix = os.path.join(outDir, imgObject.name)
imgName = outputPrefix + ".png"
if os.path.isfile(imgName):
print(" + Already found, skipping.")
else:
rgb = imgObject.getRGB()
if rgb is None: