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Zero
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# Standard library imports
import os
# Third-party imports
import cv2
import numpy as np
from PIL import Image
# Local imports
from utils.image_utils import load_image, preprocess_image, get_image_from_input
from utils.face_detector import load_face_detector # Assuming this is the dlib detector loader
# Define constants
HAAR_CASCADE_FILENAME = "haarcascade_frontalface_default.xml"
def face_detection(input_type, uploaded_image, image_url, base64_string, face_detection_method):
"""
Performs face detection on the image from various input types using the selected method.
Args:
input_type (str): The selected input method ("Upload File", "Enter URL", "Enter Base64").
uploaded_image (PIL.Image.Image): The uploaded image (if input_type is "Upload File").
image_url (str): The image URL (if input_type is "Enter URL").
base64_string (str): The image base64 string (if input_type is "Enter Base64").
face_detection_method (str): The selected face detection method ("OpenCV" or "dlib").
Returns:
numpy.ndarray: The image with detected faces, or None if an error occurred.
"""
# Use the centralized function to get the image
image = get_image_from_input(input_type, uploaded_image, image_url, base64_string)
if image is None:
print("Image is None after loading/selection.")
return None # No valid input or loading failed
try:
# Preprocess the image (convert PIL to numpy, ensure RGB)
# preprocess_image expects a PIL Image or something convertible by Image.fromarray
processed_image = preprocess_image(image)
if processed_image is not None:
gray = cv2.cvtColor(processed_image, cv2.COLOR_BGR2GRAY)
if face_detection_method == "OpenCV":
print("Using OpenCV for face detection.")
# Ensure the haarcascade file is accessible.
# This path might need adjustment depending on the environment.
# Construct the full path to the Haar cascade file
cascade_path = os.path.join(cv2.data.haarcascades, HAAR_CASCADE_FILENAME)
# Check if the cascade file exists
if not os.path.exists(cascade_path):
error_message = f"Error: Haar cascade file not found at {cascade_path}. Please ensure OpenCV is installed correctly and the file exists."
print(error_message)
return None
face_cascade = cv2.CascadeClassifier(cascade_path)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for x, y, w, h in faces:
cv2.rectangle(processed_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
elif face_detection_method == "dlib":
print("Using dlib for face detection.")
face_detector = load_face_detector()
# dlib works on RGB images, but the detector can take grayscale
# However, the rectangles are relative to the original image size
# Let's use the original processed_image (RGB numpy array) for drawing
faces = face_detector(processed_image, 1) # 1 is the upsample level
for face in faces:
x, y, w, h = face.left(), face.top(), face.width(), face.height()
cv2.rectangle(processed_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
return processed_image
else:
return None
except Exception as e:
print(f"Error in face detection processing: {e}")
return None |