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"""=============
Example : extract_record.py
Author : Saifeddine ALOUI (ParisNeo)
Description :
Make sure you install deepface
pip install deepface
<================"""
import numpy as np
from pathlib import Path
import cv2
import time
from FaceAnalyzer import FaceAnalyzer
from pathlib import Path
import pickle
from tqdm import tqdm # used to draw a progress bar pip install tqdm
from deepface import DeepFace
# Number of images to use to build the embedding
nb_images=50
# If faces path is empty then make it
faces_path = Path(__file__).parent/"faces"
if not faces_path.exists():
faces_path.mkdir(parents=True, exist_ok=True)
# Build face analyzer while specifying that we want to extract just a single face
fa = FaceAnalyzer(max_nb_faces=1)
box_colors=[
(255,0,0),
(0,255,0),
(0,0,255),
(255,255,0),
(255,0,255),
]
import gradio as gr
import numpy as np
class UI():
def __init__(self) -> None:
self.i=0
self.embeddings_cloud = []
self.is_recording=False
self.face_name=None
self.nb_images = 20
# Important to set. If higher than this distance, the face is considered unknown
self.threshold = 4e-1
self.faces_db_preprocessed_path = Path(__file__).parent/"faces_db_preprocessed"
self.current_name = None
self.current_face_files = []
self.draw_landmarks = True
with gr.Blocks() as demo:
gr.Markdown("## FaceAnalyzer face recognition test")
with gr.Tabs():
with gr.TabItem('Realtime Recognize'):
with gr.Blocks():
with gr.Row():
with gr.Column():
self.rt_webcam = gr.Image(label="Input Image", source="webcam", streaming=True)
with gr.Column():
self.rt_rec_img = gr.Image(label="Output Image")
self.rt_webcam.change(self.recognize, inputs=self.rt_webcam, outputs=self.rt_rec_img, show_progress=False)
with gr.TabItem('Image Recognize'):
with gr.Blocks():
with gr.Row():
with gr.Column():
self.rt_inp_img = gr.Image(label="Input Image")
with gr.Column():
self.rt_rec_img = gr.Image(label="Output Image")
self.rt_inp_img.change(self.recognize2, inputs=self.rt_inp_img, outputs=self.rt_rec_img, show_progress=True)
with gr.TabItem('Add face from webcam'):
with gr.Blocks():
with gr.Row():
with gr.Column():
self.img = gr.Image(label="Input Image", source="webcam", streaming=True)
self.txtFace_name = gr.Textbox(label="face_name")
self.txtFace_name.change(self.set_face_name, inputs=self.txtFace_name, show_progress=False)
self.status = gr.Label(label="face_name")
self.img.change(self.record, inputs=self.img, outputs=self.status, show_progress=False)
with gr.Column():
self.btn_start = gr.Button("Start Recording face")
self.btn_start.click(self.start_stop)
with gr.TabItem('Add face from files'):
with gr.Blocks():
with gr.Row():
with gr.Column():
self.gallery = gr.Gallery(
label="Uploaded Images", show_label=False, elem_id="gallery"
).style(grid=[2], height="auto")
self.add_file = gr.Files(label="Files",file_types=["image"])
self.add_file.change(self.add_files, self.add_file, self.gallery)
self.txtFace_name2 = gr.Textbox(label="face_name")
self.txtFace_name2.change(self.set_face_name, inputs=self.txtFace_name2, show_progress=False)
self.status = gr.Label(label="face_name")
self.img.change(self.record, inputs=self.img, outputs=self.status, show_progress=False)
with gr.Column():
self.btn_start = gr.Button("Build face embeddings")
self.btn_start.click(self.start_stop)
with gr.TabItem('Known Faces List'):
with gr.Blocks():
with gr.Row():
with gr.Column():
self.faces_list = gr.Dataframe(
headers=["Face Name"],
datatype=["str"],
label="Faces",
)
with gr.Row():
with gr.Accordion(label="Options", open=False):
self.sld_threshold = gr.Slider(1e-2,10,4e-1,step=1e-2,label="Recognition threshold")
self.sld_threshold.change(self.set_th,inputs=self.sld_threshold)
self.sld_nb_images = gr.Slider(2,50,20,label="Number of images")
self.sld_nb_images.change(self.set_nb_images, self.sld_nb_images)
self.cb_draw_landmarks = gr.Checkbox(label="Draw landmarks", value=True)
self.cb_draw_landmarks.change(self.set_draw_landmarks, self.cb_draw_landmarks)
self.upgrade_faces()
demo.queue().launch()
def add_files(self, files):
for file in files:
img = cv2.cvtColor(cv2.imread(file.name), cv2.COLOR_BGR2RGB)
self.current_face_files.append(img)
return self.current_face_files
def set_th(self, value):
self.threshold=value
def set_nb_images(self, value):
self.nb_images=value
def set_draw_landmarks(self, value):
self.draw_landmarks=value
def cosine_distance(self, u, v):
"""
Computes the cosine distance between two vectors.
Parameters:
u (numpy array): A 1-dimensional numpy array representing the first vector.
v (numpy array): A 1-dimensional numpy array representing the second vector.
Returns:
float: The cosine distance between the two vectors.
"""
dot_product = np.dot(u, v)
norm_u = np.linalg.norm(u)
norm_v = np.linalg.norm(v)
return 1 - (dot_product / (norm_u * norm_v))
def upgrade_faces(self):
# Load faces
self.known_faces=[]
self.known_faces_names=[]
face_files = [f for f in faces_path.iterdir() if f.name.endswith("pkl")]
for file in face_files:
with open(str(file),"rb") as f:
finger_print = pickle.load(f)
self.known_faces.append(finger_print)
self.known_faces_names.append(file.stem)
self.faces_list.update(self.known_faces_names)
def set_face_name(self, face_name):
self.face_name=face_name
def start_stop(self):
self.is_recording=True
def process_db(self, images):
for i,image in enumerate(images):
# Opencv uses BGR format while mediapipe uses RGB format. So we need to convert it to RGB before processing the image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (640, 480))
# Process the image to extract faces and draw the masks on the face in the image
fa.process(image)
if fa.nb_faces>0:
if fa.nb_faces>1:
print("Found too many faces!!")
face = fa.faces[0]
try:
# Get a realigned version of the landmarksx
vertices = face.get_face_outer_vertices()
image = face.getFaceBox(image, vertices,margins=(30,30,30,30))
embedding = DeepFace.represent(image)[0]["embedding"]
embeddings_cloud.append(embedding)
cv2.imwrite(str(self.faces_db_preprocessed_path/f"im_{i}.png"), cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
except Exception as ex:
print(ex)
embeddings_cloud = np.array(embeddings_cloud)
embeddings_cloud_mean = embeddings_cloud.mean(axis=0)
embeddings_cloud_inv_cov = np.linalg.inv(np.cov(embeddings_cloud.T))
# Now we save it.
# create a dialog box to ask for the subject name
name = self.face_name
with open(str(faces_path/f"{name}.pkl"),"wb") as f:
pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
print(f"Saved {name}")
def record(self, image):
if self.face_name is None:
return "Please input a face name"
if self.is_recording and image is not None:
if self.i < self.nb_images:
# Process the image to extract faces and draw the masks on the face in the image
fa.process(image)
if fa.nb_faces>0:
try:
face = fa.faces[0]
vertices = face.get_face_outer_vertices()
image = face.getFaceBox(image, vertices, margins=(40,40,40,40))
embedding = DeepFace.represent(image)[0]["embedding"]
self.embeddings_cloud.append(embedding)
self.i+=1
cv2.imshow('Face Mesh', cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
except Exception as ex:
print(ex)
return f"Processing frame {self.i}/{self.nb_images}..."
else:
# Now let's find out where the face lives inside the latent space (128 dimensions space)
embeddings_cloud = np.array(self.embeddings_cloud)
embeddings_cloud_mean = embeddings_cloud.mean(axis=0)
embeddings_cloud_inv_cov = embeddings_cloud.std(axis=0)
# Now we save it.
# create a dialog box to ask for the subject name
name = self.face_name
with open(str(faces_path/f"{name}.pkl"),"wb") as f:
pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
print(f"Saved {name} embeddings")
self.i=0
self.embeddings_cloud=[]
self.is_recording=False
self.upgrade_faces()
return f"Saved {name} embeddings"
else:
return "Waiting"
def recognize(self, image):
# Process the image to extract faces and draw the masks on the face in the image
fa.process(image)
if fa.nb_faces>0:
for i in range(fa.nb_faces):
try:
face = fa.faces[i]
vertices = face.get_face_outer_vertices()
face_image = face.getFaceBox(image, vertices, margins=(40,40,40,40))
embedding = DeepFace.represent(face_image)[0]["embedding"]
if self.draw_landmarks:
face.draw_landmarks(image, color=(0,0,0))
nearest_distance = 1e100
nearest = 0
for i, known_face in enumerate(self.known_faces):
# absolute distance
distance = np.abs(known_face["mean"]-embedding).sum()
# euclidian distance
#diff = known_face["mean"]-embedding
#distance = np.sqrt([email protected])
# Cosine distance
distance = self.cosine_distance(known_face["mean"], embedding)
if distance<nearest_distance:
nearest_distance = distance
nearest = i
if nearest_distance>self.threshold:
face.draw_bounding_box(image, thickness=1,text=f"Unknown:{nearest_distance:.3e}")
else:
face.draw_bounding_box(image, thickness=1,text=f"{self.known_faces_names[nearest]}:{nearest_distance:.3e}")
except Exception as ex:
pass
# Return the resulting frame
return image
def recognize2(self, image):
if image is None:
return None
image = cv2.resize(image, fa.image_size)
# Process the image to extract faces and draw the masks on the face in the image
fa.process(image)
if fa.nb_faces>0:
for i in range(fa.nb_faces):
try:
face = fa.faces[i]
vertices = face.get_face_outer_vertices()
face_image = face.getFaceBox(image, vertices, margins=(40,40,40,40))
embedding = DeepFace.represent(face_image)[0]["embedding"]
if self.draw_landmarks:
face.draw_landmarks(image, color=(0,0,0))
nearest_distance = 1e100
nearest = 0
for i, known_face in enumerate(self.known_faces):
# absolute distance
distance = np.abs(known_face["mean"]-embedding).sum()
# euclidian distance
#diff = known_face["mean"]-embedding
#distance = np.sqrt([email protected])
# Cosine distance
distance = self.cosine_distance(known_face["mean"], embedding)
if distance<nearest_distance:
nearest_distance = distance
nearest = i
if nearest_distance>self.threshold:
face.draw_bounding_box(image, thickness=1,text=f"Unknown:{nearest_distance:.3e}")
else:
face.draw_bounding_box(image, thickness=1,text=f"{self.known_faces_names[nearest]}:{nearest_distance:.3e}")
except Exception as ex:
pass
# Return the resulting frame
return image
ui = UI()
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