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from ultralyticsplus import YOLO
from PIL import Image
import numpy as np
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import (
Convolution2D,
LocallyConnected2D,
MaxPooling2D,
Flatten,
Dense,
Dropout,
)
import os
import zipfile
import gdown
import tensorflow as tf
def load_detector():
# load model
model = YOLO('https://github.com/akanametov/yolov8-face/releases/download/v0.0.0/yolov8n-face.pt')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 50 # maximum number of detections per image
return model
def extract_faces(model, image):
# perform inference
results = model.predict(image)
ids = np.array(results[0].boxes.xyxy).astype(np.int32)
img = Image.open(image)
crops = []
for id in ids:
crops.append(Image.fromarray(np.array(img)[id[1] : id[3], id[0]: id[2]]))
return crops
def load_model(
url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
):
base_model = Sequential()
base_model.add(
Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))
)
base_model.add(MaxPooling2D(pool_size=3, strides=2, padding="same", name="M2"))
base_model.add(Convolution2D(16, (9, 9), activation="relu", name="C3"))
base_model.add(LocallyConnected2D(16, (9, 9), activation="relu", name="L4"))
base_model.add(LocallyConnected2D(16, (7, 7), strides=2, activation="relu", name="L5"))
base_model.add(LocallyConnected2D(16, (5, 5), activation="relu", name="L6"))
base_model.add(Flatten(name="F0"))
base_model.add(Dense(4096, activation="relu", name="F7"))
base_model.add(Dropout(rate=0.5, name="D0"))
base_model.add(Dense(8631, activation="softmax", name="F8"))
# ---------------------------------
home = os.getcwd()
if os.path.isfile(home + "/VGGFace2_DeepFace_weights_val-0.9034.h5") != True:
print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
output = home + "/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
gdown.download(url, output, quiet=False)
# unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
with zipfile.ZipFile(output, "r") as zip_ref:
zip_ref.extractall(home)
base_model.load_weights(home + "/VGGFace2_DeepFace_weights_val-0.9034.h5")
# drop F8 and D0. F7 is the representation layer.
deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
return deepface_model
def findCosineDistance(source_representation, test_representation):
a = np.matmul(np.transpose(source_representation), test_representation)
b = np.sum(np.multiply(source_representation, source_representation))
c = np.sum(np.multiply(test_representation, test_representation))
return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
def get_embeddings(model, imgs):
embeddings = []
for i, img in enumerate(imgs):
try:
img = np.expand_dims(np.array(img.resize((152,152))), axis = 0)
embedding = model.predict(img, verbose=0)[0]
embeddings.append(embedding)
except:
print(f"Error at {i}, skipping")
return embeddings |