• 1 year, 2 months ago

Learn How to Create a Snapchat Filter Using Python

Mradul Mishra
Table of Contents

In this tutorial, you'll learn how to use Python to create a Snapchat filter by manipulating pixel values of an input image to create various effects, such as adding dog nose and ears or changing the color palette. Get started today!

Creating a Snapchat filter using Python involves several steps. Here's an overview of the process:

  1. Install the required packages: You'll need to install a few packages in order to build a Snapchat filter. Some of the important packages are:
  • OpenCV for computer vision tasks
  • NumPy for numerical computations
  • dlib for facial landmark detection
  1. Load the face detection and landmark detection models: You'll need a pre-trained face detection model and a landmark detection model in order to detect faces and facial landmarks in an image. You can download the models from the internet and load them into your program.

  2. Read input image: You'll need to read the image that you want to apply the filter on. You can use the OpenCV "cv2.imread()" function to read the image.

  3. Detect faces and landmarks: Once you have loaded the face detection and landmark detection models and read the input image, you can detect the faces and facial landmarks in the image. You can use the "cv2.CascadeClassifier.detectMultiScale()" function to detect faces and the "dlib.shape_predictor()" function to detect landmarks.

  4. Apply the filter: Once the facial landmarks are detected, you can apply the filter to the image. You can use NumPy to manipulate the image and create the filter effect.

  5. Display the output: Finally, you can display the output image with the applied filter using the OpenCV "cv2.imshow()" function.

Here's some sample code to get you started:


import cv2
import dlib
import numpy as np

# Load face detection model
detector = dlib.get_frontal_face_detector()

# Load landmark detection model
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# Load dog nose and ears images
nose_img = cv2.imread("dog_nose.png", -1)
ears_img = cv2.imread("dog_ears.png", -1)

# Read input image
img = cv2.imread("input.jpg")

# Convert to RGBA format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)

# Detect faces
gray = cv2.cvtColor(img, cv2.COLOR_RGBA2GRAY)
rects = detector(gray, 0)

# Loop through each face
for rect in rects:
    # Detect landmarks
    landmarks = predictor(gray, rect)
    # Extract nose and ears landmarks
    nose_landmarks = np.array([(landmarks.part(30).x, landmarks.part(30).y)])
    ears_landmarks = np.array([(landmarks.part(17).x, landmarks.part(17).y), (landmarks.part(26).x, landmarks.part(26).y)])
    # Resize dog nose and ears images to fit face
    nose_width = int(np.linalg.norm(landmarks.part(35) - landmarks.part(31)))
    nose_height = int(nose_width * nose_img.shape[0] / nose_img.shape[1])
    nose_img_resized = cv2.resize(nose_img, (nose_width, nose_height))
    ears_width = int(np.linalg.norm(landmarks.part(17) - landmarks.part(26)))
    ears_height = int(ears_width * ears_img.shape[0] / ears_img.shape[1])
    ears_img_resized = cv2.resize(ears_img, (ears_width, ears_height))
    # Translate dog nose and ears images to face
    nose_center = (landmarks.part(30).x, landmarks.part(30).y)
    nose_top_left = (nose_center[0] - int(nose_width/2), nose_center[1] - int(nose_height/2))
    ears_top_left = (landmarks.part(17).x - int(ears_width/2), landmarks.part(17).y - int(ears_height/2))
    # Blend dog nose and ears images with input image
    for i in range(nose_img_resized.shape[0]):
        for j in range(nose_img_resized.shape[1]):
            if nose_img_resized[i,j,3] != 0:
                img[nose_top_left[1]+i, nose_top_left[0]+j,:] = nose_img_resized[i,j,:3]
    for i in range(ears_img_resized.shape[0]):
        for j in range(ears_img_resized.shape[1]):
            if ears_img_resized[i,j,3] != 0:
                img[ears_top_left[1]+i, ears_top_left[0]+j,:] = ears_img_resized[i,j,:3]
# Convert back to BGR format
img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)

# Display the output
cv2.imshow("Snapchat Filter", img)


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