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#!/usr/bin/env python3
import cv2
import numpy as np
import os
import argparse
import face_recognition
import tempfile
import shutil
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def capture_image():
# Initialize webcam
video_capture = cv2.VideoCapture(0)
if not video_capture.isOpened():
logger.error("Could not open webcam")
return None
# Capture a single frame
ret, frame = video_capture.read()
# Release the webcam
video_capture.release()
if not ret:
logger.error("Failed to capture image")
return None
return frame
def save_image_with_increment(directory, base_filename, image):
base, ext = os.path.splitext(base_filename)
filename = os.path.join(directory, base + ext)
i = 0
while os.path.exists(filename):
filename = os.path.join(directory, f"{base}_{i}{ext}")
i += 1
cv2.imwrite(filename, image)
logger.info(f"Image saved as '{filename}'")
return filename
def filter_background(image, padding=40):
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Load the pre-trained face detection model from OpenCV
face_cascade_path = '/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml' # Adjust this path as needed
if not os.path.exists(face_cascade_path):
logger.error("Face cascade file not found at: %s", face_cascade_path)
return None, None
face_cascade = cv2.CascadeClassifier(face_cascade_path)
if face_cascade.empty():
logger.error("Failed to load face cascade")
return None, None
# Detect faces in the image
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) == 0:
logger.warning("No face detected")
return None, None
# Create a mask with the same dimensions as the image, initialized to zeros (black)
mask = np.zeros_like(image)
# Draw white filled rectangles on the mask where faces are detected
for (x, y, w, h) in faces:
# Add padding to the detected face coordinates
x_start = max(x - padding, 0)
y_start = max(y - padding, 0)
x_end = min(x + w + padding, image.shape[1])
y_end = min(y + h + padding, image.shape[0])
mask[y_start:y_end, x_start:x_end] = image[y_start:y_end, x_start:x_end]
# Apply the mask to the original image
result = cv2.bitwise_and(image, mask)
# Crop the image to the bounding box of the face with padding
for (x, y, w, h) in faces:
x_start = max(x - padding, 0)
y_start = max(y - padding, 0)
x_end = min(x + w + padding, image.shape[1])
y_end = min(y + h + padding, image.shape[0])
cropped_result = result[y_start:y_end, x_start:x_end]
break # Assuming we only process the first detected face
return cropped_result, faces[0] # Return the cropped result and the face bounding box
def compare_faces(face_encodings):
if len(face_encodings) < 2:
logger.warning("Not enough faces to compare")
return True
for i in range(1, len(face_encodings)):
match = face_recognition.compare_faces([face_encodings[0]], face_encodings[i])[0]
if not match:
return False
return True
def main(num_captures):
face_encodings = []
with tempfile.TemporaryDirectory() as temp_dir:
for _ in range(num_captures):
image = capture_image()
if image is not None:
filtered_image, face_box = filter_background(image)
if filtered_image is not None:
filename = save_image_with_increment(temp_dir, 'filtered_image.jpg', filtered_image)
top, right, bottom, left = face_box
face_image = image[top:bottom, left:right]
face_locations = [(0, right-left, bottom-top, 0)]
encodings = face_recognition.face_encodings(face_image, face_locations)
if encodings:
face_encodings.append(encodings[0])
else:
logger.warning("No face detected in %s", filename)
return
else:
logger.error("Filtering failed")
return
else:
logger.error("No image captured")
return
if compare_faces(face_encodings):
logger.info("All captured faces match.")
known_faces_dir = '/usr/local/share/known_faces'
if not os.path.exists(known_faces_dir):
os.makedirs(known_faces_dir, mode=0o755, exist_ok=True)
logger.info("Created directory: %s", known_faces_dir)
for filename in os.listdir(temp_dir):
src_file = os.path.join(temp_dir, filename)
dst_file = os.path.join(known_faces_dir, filename)
shutil.move(src_file, dst_file)
logger.info("Moved '%s' to '%s'", src_file, dst_file)
else:
logger.warning("Captured faces do not match.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Capture and filter images, then compare faces.")
parser.add_argument('--num_captures', type=int, default=10, help='Number of images to capture and compare')
args = parser.parse_args()
main(args.num_captures)
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