CODEWITHSUNDEEP
pythonopencvpython-imaging-libraryopencv3.0
mattwelter
mattwelter

Reputation: 39

Why can't I place my image on eye detection?

I want to place a transparent image on the location of where cv2 detects my eyes. I've accomplished the main two steps, and now I need to combine them.

For instance, here is the output with the image transparency working, and here is the output with the eye detection working. The script and images are below, I'm not sure what to do.

Images

dot_transparent.png

image.jpg

app.py

import os
import numpy
import cv2
from PIL import Image
from os.path import join, dirname, realpath

def upload_files():
    #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml
    face_cascade = cv2.CascadeClassifier('/Users/matt/Python/LazerEyes/haarcascade_eye.xml')

    #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml
    eye_cascade = cv2.CascadeClassifier('/Users/matt/Python/LazerEyes/haarcascade_eye.xml')

    img = cv2.imread('new.png')
    dot = cv2.imread('dot_transparent.png', cv2.IMREAD_UNCHANGED)

    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    gray_to_place = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    
    img_h, img_w = gray.shape
    img_to_place_h, img_to_place_w = gray_to_place.shape

    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[y:y+h, x:x+w]
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex,ey,ew,eh) in eyes:
            dot = cv2.resize(dot, (eh, ew))
            # Prepare pixel-wise alpha blending
            dot_alpha = dot[..., :3] / 255.0
            dot_alpha = numpy.repeat(dot_alpha[..., numpy.newaxis], 3, axis=2)
            dot = dot[..., :3]

            resized_img = cv2.resize(dot, (eh, ew), interpolation = cv2.INTER_AREA)
            resized_img_h, resized_img_w, _ = resized_img.shape

            #pointsOnFace = []
            #integersToAppend = eh
            #pointsOnFace.append(integersToAppend)
            #print(pointsOnFace)

            roi_color[ey:ey+resized_img_h, ex:ex+resized_img_w, :] = resized_img

    cv2.imwrite('out.png', img)

Upvotes: 1

Views: 113

Answers (1)

HansHirse
HansHirse

Reputation: 18925

Incorporating my earlier answer into the given code (and minimizing the resulting code), the solution might look like this:

import cv2
import numpy as np

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

img = cv2.imread('new.jpg')
dot = cv2.imread('dot_transparent.png', cv2.IMREAD_UNCHANGED)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

for (x, y, w, h) in faces:

    eyes = eye_cascade.detectMultiScale(gray[y:y+h, x:x+w])

    for (ex, ey, ew, eh) in eyes:

        # Filter out small detections, if you're only want to have the eyes
        if ew < 100 or eh < 100:
            continue

        d = cv2.resize(dot.copy(), (eh, ew))
        d_alpha = d[..., 3] / 255.0
        d_alpha = np.repeat(d_alpha[..., np.newaxis], 3, axis=2)
        d = d[..., :3]

        img[y+ey:y+ey+eh, x+ex:x+ex+ew, :] = \
            img[y+ey:y+ey+eh, x+ex:x+ex+ew, :] * (1 - d_alpha) + d * d_alpha

cv2.imwrite('out.png', img)

That's the output (I filtered out small detections, such that only the actual eyes are overlayed):

Finetuning the exact locations might still be needed, but I think that's an issue coming from the Haar cascade classifier itself.

----------------------------------------
System information
----------------------------------------
Platform:      Windows-10-10.0.16299-SP0
Python:        3.9.1
NumPy:         1.20.1
OpenCV:        4.5.1
----------------------------------------

Upvotes: 1

Related Questions