Image processing for blur detection

Question

I am trying to detect blurred images. Thanks to this post, I managed to create a script using Fast Fourier Transform and so far it worked quite well. But for some photos, I am not able to get correct results.

When the background is almost as the same color than the objects from the front, I think my script is not able to give good result. Do you have any leads to correct this ?

import cv2
import imutils
from PIL import Image as pilImg
from IPython.display import display
import numpy as np
from matplotlib import pyplot as plt

def detect_blur_fft(image, size=60, thresh=17, vis=False):
    """
    Detects blur by comparing the image to a blurred version of the image
    
    :param image: The image to detect blur in
    :param size: the dimension of the smaller square extracted from the image, defaults to 60 (optional)
    :param thresh: the lower this value, the more blur is acceptable, defaults to 17 (optional)
    :param vis: Whether or not to return a visualization of the detected blur points, defaults to False
    (optional)
    """
    # grab the dimensions of the image and use the dimensions to
    # derive the center (x, y)-coordinates
    (h, w) = image.shape
    (cX, cY) = (int(w / 2.0), int(h / 2.0))
    # compute the FFT to find the frequency transform, then shift
    # the zero frequency component (i.e., DC component located at
    # the top-left corner) to the center where it will be more
    # easy to analyze
    fft = np.fft.fft2(image)
    fftShift = np.fft.fftshift(fft)
    # check to see if we are visualizing our output
    if vis:
        # compute the magnitude spectrum of the transform
        magnitude = 20 * np.log(np.abs(fftShift))
        # display the original input image
        (fig, ax) = plt.subplots(1, 2, )
        ax[0].imshow(image, cmap="gray")
        ax[0].set_title("Input")
        ax[0].set_xticks([])
        ax[0].set_yticks([])
        # display the magnitude image
        ax[1].imshow(magnitude, cmap="gray")
        ax[1].set_title("Magnitude Spectrum")
        ax[1].set_xticks([])
        ax[1].set_yticks([])
        # show our plots
        plt.show()
    # zero-out the center of the FFT shift (i.e., remove low
    # frequencies), apply the inverse shift such that the DC
    # component once again becomes the top-left, and then apply
    # the inverse FFT
    fftShift[cY - size:cY + size, cX - size:cX + size] = 0
    fftShift = np.fft.ifftshift(fftShift)
    recon = np.fft.ifft2(fftShift)
    # compute the magnitude spectrum of the reconstructed image,
    # then compute the mean of the magnitude values
    magnitude = 20 * np.log(np.abs(recon))
    mean = np.mean(magnitude)
    # the image will be considered "blurry" if the mean value of the
    # magnitudes is less than the threshold value
    return (mean, mean <= thresh)

pathImg = "path to the image"
image = cv2.imread(pathImg)
# Resizing the image to 500 pixels in width.
image = imutils.resize(image, width= 500)
# Converting the image to gray scale.
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Using the FFT to detect blur.
(mean, blurry) = detect_blur_fft(gray, size=60)
image = np.dstack([gray] * 3)
# This is a conditional statement that will set the color to red if the image is blurry or green
color = (0, 0, 255) if blurry else (0, 255, 0)
text = "Blurry ({:.4f})" if blurry else "Not Blurry ({:.4f})"
text = text.format(mean)
# Adding text to the image.
cv2.putText(image, text, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
print("[INFO] {}".format(text))
# show the output image
display(pilImg.fromarray(image))