Introduction:
DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.
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Code:
import os
import cv2
import numpy as np
from deepface import DeepFace
# Directory to store the dataset
data_dir = "face_dataset"
os.makedirs(data_dir, exist_ok=True)
# Step 1: Create Face Dataset
def create_face_dataset(name):
person_dir = os.path.join(data_dir, name)
os.makedirs(person_dir, exist_ok=True)
cap = cv2.VideoCapture(0)
print("Capturing images. Press 'q' to quit.")
count = 0
while True:
ret, frame = cap.read()
if not ret:
print("Failed to grab frame.")
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml").detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
count += 1
face_img = frame[y:y + h, x:x + w]
face_path = os.path.join(person_dir, f"{name}_{count}.jpg")
cv2.imwrite(face_path, face_img)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow("Capture Faces", frame)
if cv2.waitKey(1) & 0xFF == ord('q') or count >= 20:
break
cap.release()
cv2.destroyAllWindows()
print(f"Saved {count} images for {name}.")
# Step 2: Train Face Dataset
def train_face_dataset():
embeddings = {}
for person in os.listdir(data_dir):
person_dir = os.path.join(data_dir, person)
if os.path.isdir(person_dir):
embeddings[person] = []
for img_name in os.listdir(person_dir):
img_path = os.path.join(person_dir, img_name)
try:
embedding = DeepFace.represent(img_path, model_name="Facenet", enforce_detection=False)[0]["embedding"]
embeddings[person].append(embedding)
except Exception as e:
print(f"Failed to process {img_path}: {e}")
return embeddings
# Step 3: Recognize Faces
def recognize_faces(embeddings):
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
print("Failed to grab frame.")
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml").detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
face_img = frame[y:y + h, x:x + w]
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
try:
analysis = DeepFace.analyze(face_img, actions=["age", "gender", "emotion"], enforce_detection=False)
# Handle cases where analysis is returned as a list
if isinstance(analysis, list):
analysis = analysis[0]
# Display age, gender, and emotion
age = analysis["age"]
gender = analysis["gender"]
gender = gender if isinstance(gender, str) else max(gender, key=gender.get)
emotion = max(analysis["emotion"], key=analysis["emotion"].get)
# Recognize person
face_embedding = DeepFace.represent(face_img, model_name="Facenet", enforce_detection=False)[0]["embedding"]
# Calculate cosine similarity
match = None
max_similarity = -1
for person, person_embeddings in embeddings.items():
for embed in person_embeddings:
similarity = np.dot(face_embedding, embed) / (np.linalg.norm(face_embedding) * np.linalg.norm(embed))
if similarity > max_similarity:
max_similarity = similarity
match = person
if max_similarity > 0.7:
label = f"{match} ({max_similarity:.2f})"
else:
label = "Unknown"
display_text = f"{label}, Age: {int(age)}, Gender: {gender}, Emotion: {emotion}"
cv2.putText(frame, display_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
except Exception as e:
print(f"Error recognizing face: {e}")
cv2.imshow("Recognize Faces", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
print("1. Create Face Dataset\n2. Train Face Dataset\n3. Recognize Faces")
choice = input("Enter your choice: ")
if choice == "1":
name = input("Enter the name of the person: ")
create_face_dataset(name)
elif choice == "2":
embeddings = train_face_dataset()
np.save("embeddings.npy", embeddings)
print("Embeddings saved.")
elif choice == "3":
if os.path.exists("embeddings.npy"):
embeddings = np.load("embeddings.npy", allow_pickle=True).item()
recognize_faces(embeddings)
else:
print("No embeddings found. Train the dataset first.")
else:
print("Invalid choice.")
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