5 Best Ways to Crop and Save the Detected Faces in OpenCV Python

💡 Problem Formulation: When working with facial recognition systems or preprocessing images for machine learning models, it’s common to need to detect faces in images and then crop and save them as separate files. The input is typically a photograph or video frame and the desired output is a set of images, each containing one detected face.

Method 1: Use Haar Cascades for Face Detection and Cropping

OpenCV provides pre-trained Haar cascades that can be used for detecting faces. After detecting the faces, the coordinates can be used to crop the faces from the original image. Faces are saved using OpenCV’s imwrite function.

Here’s an example:

import cv2

# Load the Haar cascade file
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Read the image and convert to grayscale
image = cv2.imread('input.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces
faces = face_cascade.detectMultiScale(gray_image, 1.1, 4)

# Crop and Save faces
for index, (x, y, w, h) in enumerate(faces):
    face = image[y:y+h, x:x+w]
    cv2.imwrite(f'face{index}.jpg', face)

Output: Images named face0.jpg, face1.jpg, etc. each containing a single cropped face from the original image.

This method involves loading a pre-trained Haar cascade model, passing a grayscale version of the original image to the detectMultiScale function, which returns the coordinates of the detected faces within the image, then cropping the faces using these coordinates and saving them as separate images.

Method 2: Utilize DNN Module for Accurate Face Detection

OpenCV’s DNN module offers a deep learning-based approach for more accurate face detection. After the detection step, similar cropping and saving operations are performed.

Here’s an example:

import cv2

# Load the model
net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'weights.caffemodel')

# Read the image
image = cv2.imread('input.jpg')
(h, w) = image.shape[:2]

# Preprocess the image for DNN
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))

# Forward pass through the network
net.setInput(blob)
detections = net.forward()

# Crop and Save detected faces
for i in range(0, detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.5:
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        face = image[startY:endY, startX:endX]
        cv2.imwrite(f'face{index}.jpg', face)

Output: Images with higher accuracy named face0.jpg, face1.jpg, etc., each containing a single cropped face.

This method makes use of a pre-trained Caffe model to perform face detection. The image is preprocessed and input to the deep learning model which returns a list of detected faces with a confidence score. Faces are then cropped and saved based on this information.

Method 3: Apply MTCNN for Detection and Cropping

MTCNN (Multi-task Cascaded Convolutional Networks) is a powerful and precise neural network method for detecting faces and facial landmarks. It works well for both frontal and profile faces.

Here’s an example:

from mtcnn.mtcnn import MTCNN
import cv2

detector = MTCNN()

image = cv2.imread('input.jpg')
result = detector.detect_faces(image)

for index, face in enumerate(result):
    x, y, width, height = face['box']
    face_image = image[y:y+height, x:x+width]
    cv2.imwrite(f'face{index}.jpg', face_image)

Output: Images named face0.jpg, face1.jpg, etc., with precise detection of faces at various angles.

In this method, the MTCNN library provides high-precision face detection. The detected face areas are then used to crop the original image and the individual faces are saved as separate images.

Method 4: Employ Dlib for Face Detection and Landmark-Based Cropping

Dlib’s facial landmark detector is not only capable of detecting faces but also identifying key landmark points on faces. This allows for more refined cropping that can include adjustments based on facial features.

Here’s an example:

import dlib
import cv2

# Load the detector
detector = dlib.get_frontal_face_detector()

# Read the image
image = cv2.imread('input.jpg')

# Detect faces
faces = detector(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), 1)

# Crop and Save faces
for index, d in enumerate(faces):
    face = image[d.top():d.bottom(), d.left():d.right()]
    cv2.imwrite(f'face{index}.jpg', face)

Output: Images named face0.jpg, face1.jpg, etc., with the possibility of using facial landmarks to adjust the crop area.

This method involves using Dlib’s face detector to find faces in the image, then cropping the detected faces based on the coordinates provided by the detector, and saving them as new images.

Bonus One-Liner Method 5: Minimalistic Approach Using a Single Function Call

For those who prefer minimalistic code, OpenCV and image slicing can be used to detect, crop, and save faces with just one line inside the loop that processes each detected face.

Here’s an example:

[(cv2.imwrite(f'face{index}.jpg', image[y:y+h, x:x+w]),) for index, (x, y, w, h) in enumerate(cv2.CascadeClassifier('haarcascade_frontalface_default.xml').detectMultiScale(cv2.cvtColor(cv2.imread('input.jpg'), cv2.COLOR_BGR2GRAY), 1.1, 4))]

Output: A collection of images saved with minimal code effort.

This is a compact approach that combines loading the image, detecting faces, and saving each cropped face in a single line of Python. It’s a succinct and quick way to perform the task with less code.

Summary/Discussion

• Method 1: Haar Cascades. Easy to implement. May not be as accurate with varied or low-quality images.
• Method 2: DNN Module. Offers high accuracy and good performance on a range of images. Requires more computational resources.
• Method 3: MTCNN. Provides high precision in detecting faces of different orientations. Potentially slower and more resource-intensive than some other methods.
• Method 4: Dlib. Allows for detailed face detection and the use of landmarks. However, the installation process can be more complex and requires Python bindings for Dlib.
• Bonus Method 5: Minimalistic One-Liner. Quick and easy. Not suitable for complex scenarios and lacks customization for the detection and cropping process.

💡 Problem Formulation: When working with facial recognition systems or preprocessing images for machine learning models, it’s common to need to detect faces in images and then crop and save them as separate files. The input is typically a photograph or video frame and the desired output is a set of images, each containing one detected face.

Method 1: Use Haar Cascades for Face Detection and Cropping

OpenCV provides pre-trained Haar cascades that can be used for detecting faces. After detecting the faces, the coordinates can be used to crop the faces from the original image. Faces are saved using OpenCV’s imwrite function.

Here’s an example:

import cv2

# Load the Haar cascade file
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Read the image and convert to grayscale
image = cv2.imread('input.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces
faces = face_cascade.detectMultiScale(gray_image, 1.1, 4)

# Crop and Save faces
for index, (x, y, w, h) in enumerate(faces):
    face = image[y:y+h, x:x+w]
    cv2.imwrite(f'face{index}.jpg', face)

Output: Images named face0.jpg, face1.jpg, etc. each containing a single cropped face from the original image.

This method involves loading a pre-trained Haar cascade model, passing a grayscale version of the original image to the detectMultiScale function, which returns the coordinates of the detected faces within the image, then cropping the faces using these coordinates and saving them as separate images.

Method 2: Utilize DNN Module for Accurate Face Detection

OpenCV’s DNN module offers a deep learning-based approach for more accurate face detection. After the detection step, similar cropping and saving operations are performed.

Here’s an example:

import cv2

# Load the model
net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'weights.caffemodel')

# Read the image
image = cv2.imread('input.jpg')
(h, w) = image.shape[:2]

# Preprocess the image for DNN
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))

# Forward pass through the network
net.setInput(blob)
detections = net.forward()

# Crop and Save detected faces
for i in range(0, detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.5:
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        face = image[startY:endY, startX:endX]
        cv2.imwrite(f'face{index}.jpg', face)

Output: Images with higher accuracy named face0.jpg, face1.jpg, etc., each containing a single cropped face.

This method makes use of a pre-trained Caffe model to perform face detection. The image is preprocessed and input to the deep learning model which returns a list of detected faces with a confidence score. Faces are then cropped and saved based on this information.

Method 3: Apply MTCNN for Detection and Cropping

MTCNN (Multi-task Cascaded Convolutional Networks) is a powerful and precise neural network method for detecting faces and facial landmarks. It works well for both frontal and profile faces.

Here’s an example:

from mtcnn.mtcnn import MTCNN
import cv2

detector = MTCNN()

image = cv2.imread('input.jpg')
result = detector.detect_faces(image)

for index, face in enumerate(result):
    x, y, width, height = face['box']
    face_image = image[y:y+height, x:x+width]
    cv2.imwrite(f'face{index}.jpg', face_image)

Output: Images named face0.jpg, face1.jpg, etc., with precise detection of faces at various angles.

In this method, the MTCNN library provides high-precision face detection. The detected face areas are then used to crop the original image and the individual faces are saved as separate images.

Method 4: Employ Dlib for Face Detection and Landmark-Based Cropping

Dlib’s facial landmark detector is not only capable of detecting faces but also identifying key landmark points on faces. This allows for more refined cropping that can include adjustments based on facial features.

Here’s an example:

import dlib
import cv2

# Load the detector
detector = dlib.get_frontal_face_detector()

# Read the image
image = cv2.imread('input.jpg')

# Detect faces
faces = detector(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), 1)

# Crop and Save faces
for index, d in enumerate(faces):
    face = image[d.top():d.bottom(), d.left():d.right()]
    cv2.imwrite(f'face{index}.jpg', face)

Output: Images named face0.jpg, face1.jpg, etc., with the possibility of using facial landmarks to adjust the crop area.

This method involves using Dlib’s face detector to find faces in the image, then cropping the detected faces based on the coordinates provided by the detector, and saving them as new images.

Bonus One-Liner Method 5: Minimalistic Approach Using a Single Function Call

For those who prefer minimalistic code, OpenCV and image slicing can be used to detect, crop, and save faces with just one line inside the loop that processes each detected face.

Here’s an example:

[(cv2.imwrite(f'face{index}.jpg', image[y:y+h, x:x+w]),) for index, (x, y, w, h) in enumerate(cv2.CascadeClassifier('haarcascade_frontalface_default.xml').detectMultiScale(cv2.cvtColor(cv2.imread('input.jpg'), cv2.COLOR_BGR2GRAY), 1.1, 4))]

Output: A collection of images saved with minimal code effort.

This is a compact approach that combines loading the image, detecting faces, and saving each cropped face in a single line of Python. It’s a succinct and quick way to perform the task with less code.

Summary/Discussion

• Method 1: Haar Cascades. Easy to implement. May not be as accurate with varied or low-quality images.
• Method 2: DNN Module. Offers high accuracy and good performance on a range of images. Requires more computational resources.
• Method 3: MTCNN. Provides high precision in detecting faces of different orientations. Potentially slower and more resource-intensive than some other methods.
• Method 4: Dlib. Allows for detailed face detection and the use of landmarks. However, the installation process can be more complex and requires Python bindings for Dlib.
• Bonus Method 5: Minimalistic One-Liner. Quick and easy. Not suitable for complex scenarios and lacks customization for the detection and cropping process.