def __init__(self,
model_path,
extractor: BaseExtractor = DLibExtractor(),
threshold: float = 0.9,
align=True):
super().__init__()
self.pipeline = Pipeline([
DLibDetector(scale=1),
# OneMillisecondAligner(extractor.resize.size) if align else NoAligner(),
extractor
])
self.model_path = model_path
# for vgg face, 0.7 seems to be the best
self.threshold = threshold
if os.path.exists(model_path):
self.reload()
return
classes = os.listdir(FilePathManager.resolve("faces"))
self.classes = {}
for clz in classes:
path = FilePathManager.resolve(f"faces/{clz}")
temp = os.listdir(path)
images = []
for t in temp:
fi = f"{path}/{t}"
images.append(cv2.imread(fi))
self.add_person(clz, images)
class EvmPredictor:
pipeline = Pipeline([
DLibDetector(scale=1),
OneMillisecondAligner(224),
VggExtractor()
])
def __init__(self, evm_model_path: str):
self.model_path = evm_model_path
self.evm: EVM = joblib.load(self.model_path)
def reload(self):
self.evm = joblib.load(self.model_path)
def save(self):
joblib.dump(self.evm, self.model_path)
@staticmethod
def extract_from_images(images):
result = []
for image in images:
temp = EvmPredictor.pipeline(image)[0].reshape(-1)
if temp[0] == 0:
continue
result.append(temp)
return np.asarray(result)
def add_person(self, person_name, images):
X = EvmPredictor.extract_from_images(images)
y = np.full((len(images), 1), person_name)
self.evm.fit(X, y)
self.save()
def remove_person(self, person_name):
self.evm.remove(person_name)
self.save()
def predict_from_image(self, image):
faces = EvmPredictor.pipeline(image)[0]
return self.evm.predict_with_prop(faces)
def predict_from_path(self, path):
return self.predict_from_image(cv2.imread(path))
import glob
import cv2
from aligners.base_aligner import BaseAligner
from bases.pipeline import Pipeline
from detectors.dlib_detector import DLibDetector
from file_path_manager import FilePathManager
class PassAligner(BaseAligner):
def forward(self, inputs):
return inputs
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
pipeline = Pipeline([DLibDetector(), PassAligner()])
for i, face in enumerate(faces):
face = cv2.imread(face)
cropped_output, image = pipeline(face, True)
for j, cropped_image in enumerate(cropped_output):
cv2.imwrite(path + "/test{}-{}.jpeg".format(i, j), cropped_image)
return Variable(faces), image
def forward(self, inputs):
faces, image = inputs
_, features = self.light_cnn(faces)
return features, image
def postprocess(self, inputs):
features, image = inputs
return torch2cv(features), image
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
# pipeline = Pipeline([DLibDetector(), OneMillisecondAligner(), VggExtractor()])
pipeline = Pipeline(
[DLibDetector(),
NoAligner(scale=3),
LightCNNExtractor()])
# pipeline = Pipeline([DLibDetector(), Crop(), Resize(224), VggExtractor()])
for i, face in enumerate(faces):
face = cv2.imread(face)
features, _ = pipeline(face, True)
print("{} image: #{} Features.".format(i, features.shape))
faces = faces.unsqueeze(0)
if self.use_cuda:
faces = faces.cuda()
return Variable(faces), image
def forward(self, inputs):
faces, image = inputs
return self.vgg_face(faces), image
def postprocess(self, inputs):
features, image = inputs
return torch2cv(features), image
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
# pipeline = Pipeline([DLibDetector(), OneMillisecondAligner(), VggExtractor()])
pipeline = Pipeline([DLibDetector(), NoAligner(scale=3), VggExtractor()])
# pipeline = Pipeline([DLibDetector(), Crop(), Resize(224), VggExtractor()])
for i, face in enumerate(faces):
face = cv2.imread(face)
features, _ = pipeline(face, True)
print("{} image: #{} Features.".format(i, features.shape))
import numpy as np
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import train_test_split, GridSearchCV
from aligners.one_millisecond_aligner import OneMillisecondAligner
from bases.pipeline import Pipeline
from classifiers.evm import EVM
from detectors.dlib_detector import DLibDetector
from extractors.dlib_extractor import DLibExtractor
# from extractors.vgg_extractor import VggExtractor
from file_path_manager import FilePathManager
root_path = FilePathManager.resolve("faces")
classes = os.listdir(root_path)
pipeline = Pipeline([
DLibDetector(scale=1),
OneMillisecondAligner(224),
# VggExtractor()
DLibExtractor()
])
X, y = [], []
for clz in classes:
files = os.listdir(f"{root_path}/{clz}")
for file in files:
path = f"{root_path}/{clz}/{file}"
image = cv2.imread(path)
temp = pipeline(image)[0].reshape(-1)
if temp.shape[0] == 0:
print(f"wrong : {path}")
continue
print(f"correct : {path}")
return self.aligner.align(
self.size,
self.image,
rect,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
def forward(self, inputs):
rects, self.image = inputs
rects = Utils.points2rects(rects)
with Pool(cpu_count()) as pool:
result = pool.map(self.align, rects)
return result, self.image
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
pipeline = Pipeline([DLibDetector(), OneMillisecondAligner()])
for i, face in enumerate(faces):
face = cv2.imread(face)
cropped_output, image = pipeline(face)
for j, cropped_image in enumerate(cropped_output):
cv2.imwrite(path + "/test{}-{}.jpeg".format(i, j), cropped_image)
super().__init__()
def preprocess(self, inputs):
return inputs
def forward(self, inputs):
faces, image = inputs
rects = dlib.rectangles(Utils.points2rects(faces))
csses = []
for rect in rects:
csses.append(_rect_to_css(rect))
result = face_recognition.face_encodings(image, csses)
return np.array(result), rects, image
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
# pipeline = Pipeline([DLibDetector(), OneMillisecondAligner(), VggExtractor()])
pipeline = Pipeline([DLibDetector(), NoAligner(scale=0), DLibExtractor()])
# pipeline = Pipeline([DLibDetector(), Crop(), Resize(224), VggExtractor()])
for i, face in enumerate(faces):
face = cv2.imread(face)
features, _ = pipeline(face, True)
print("{} image: #{} Features.".format(i, features.shape))
from transforms.crop import Crop
from transforms.scale import Scale
class NoAligner(BaseAligner):
def __init__(self, scale: float = 0.0):
"""
:param scale: between 0 (0%) and 1 (100%)
"""
self.scale = scale
self.pipeline = Pipeline([Scale(scale), Crop()])
def forward(self, inputs):
return self.pipeline(inputs)
if __name__ == '__main__':
FilePathManager.clear_dir("output")
path = FilePathManager.resolve("output")
faces = sorted(glob.glob(FilePathManager.resolve("images/*")))
pipeline = Pipeline([DLibDetector(), NoAligner(scale=1.5)])
for i, face in enumerate(faces):
face = cv2.imread(face)
cropped_output, image = pipeline(face, True)
for j, cropped_image in enumerate(cropped_output):
cv2.imwrite(path + "/test{}-{}.jpeg".format(i, j), cropped_image)