Compare two screenshots for differences and highlight the differences

screenshot_compare.py

import base64
from dataclasses import dataclass
from typing import Any

import cv2
import imutils
import numpy as np
from skimage.metrics import structural_similarity


@dataclass
class ImageDifference:
    score: float
    original: Any
    compare: Any

    def write_images(self, original_file_path="original", difference_file_path="difference"):
        cv2.imwrite(str(original_file_path), self.original)
        cv2.imwrite(str(difference_file_path), self.compare)


class Image(object):
    def __init__(self, cv_image):
        if not isinstance(cv_image, np.ndarray):
            raise TypeError("parameter must be a cv2 image")
        self.image = cv_image

    @classmethod
    def from_path(cls, file_path):
        return cls(cv2.imread(str(file_path)))

    @classmethod
    def from_base64(cls, base64_image):
        im_bytes = base64.b64decode(base64_image)
        im_arr = np.frombuffer(im_bytes, dtype=np.uint8)  # im_arr is one-dim Numpy array
        img = cv2.imdecode(im_arr, flags=cv2.IMREAD_COLOR)
        return cls(img)


class ImageCompare:
    @staticmethod
    def compare_images(lhs: Image, rhs: Image, threshold: int = 0.95) -> ImageDifference:
        # https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/
        # convert the images to grayscale
        lhs_gray = cv2.cvtColor(lhs.image, cv2.COLOR_BGR2GRAY)
        rhs_gray = cv2.cvtColor(rhs.image, cv2.COLOR_BGR2GRAY)

        # compute the Structural Similarity Index (SSIM) between the two
        # images, ensuring that the difference image is returned

        (score, diff) = structural_similarity(
            lhs_gray, rhs_gray, gaussian_weights=True, full=True, sigma=1.5, use_sample_covariance=False
        )
        if score >= threshold:
            return None

        diff = (diff * 255).astype("uint8")

        # threshold the difference image, followed by finding contours to
        # obtain the regions of the two input images that differ
        thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)

        # loop over the contours
        for c in cnts:
            # compute the bounding box of the contour and then draw the
            # bounding box on both input images to represent where the two
            # images differ
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(lhs.image, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.rectangle(rhs.image, (x, y), (x + w, y + h), (0, 0, 255), 2)

        return ImageDifference(score=score, original=lhs.image, compare=rhs.image)