wasNodeSuitesComfyui_midasSuiteV2/fDOF_WAS.py

# By WASasquatch (Discord: WAS#0263)

import torch
import numpy as np
from PIL import Image, ImageFilter

class fDOF:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "depth": ("IMAGE",),
                "mode": (["mock","gaussian","box"],),
                "radius": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}),
                "samples": ("INT", {"default": 1, "min": 1, "max": 3, "step": 1}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "fdof_composite"

    CATEGORY = "WAS"

    def fdof_composite(self, image, depth, radius, samples, mode):
    
        if 'opencv-python' not in self.packages():
            print("Installing CV2...")
            subprocess.check_call([sys.executable, '-m', 'pip', '-q', 'install', 'opencv-python'])
        
        import cv2 as cv

        #Convert tensor to a PIL Image
        i = 255. * image.cpu().numpy().squeeze()
        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
        d = 255. * depth.cpu().numpy().squeeze()
        depth_img = Image.fromarray(np.clip(d, 0, 255).astype(np.uint8))

        #Apply Fake Depth of Field
        fdof_image = self.portraitBlur(img, depth_img, radius, samples, mode)

        return ( torch.from_numpy(np.array(fdof_image).astype(np.float32) / 255.0).unsqueeze(0), )

    def medianFilter(self, img, diameter, sigmaColor, sigmaSpace):
        import cv2 as cv
        diameter = int(diameter); sigmaColor = int(sigmaColor); sigmaSpace = int(sigmaSpace)
        img = img.convert('RGB')
        img = cv.cvtColor(np.array(img), cv.COLOR_RGB2BGR)
        img = cv.bilateralFilter(img, diameter, sigmaColor, sigmaSpace)
        img = cv.cvtColor(np.array(img), cv.COLOR_BGR2RGB)
        return Image.fromarray(img).convert('RGB')

    def portraitBlur(self, img, mask, radius=5, samples=1, mode = 'mock'):
        mask = mask.resize(img.size).convert('L')
        if mode == 'mock':
            bimg = self.medianFilter(img, radius, (radius * 1500), 75)
        elif mode == 'gaussian':
            bimg = img.filter(ImageFilter.GaussianBlur(radius = radius))
        elif mode == 'box':
            bimg = img.filter(ImageFilter.BoxBlur(radius))
        bimg.convert(img.mode)
        rimg = None
        if samples > 1:
            for i in range(samples):
                if i == 0:
                    rimg = Image.composite(img, bimg, mask)
                else:
                    rimg = Image.composite(rimg, bimg, mask)
        else:
            rimg = Image.composite(img, bimg, mask).convert('RGB')
        
        return rimg
        
    def packages(self):
        import sys, subprocess
        return [r.decode().split('==')[0] for r in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']).split()]

NODE_CLASS_MAPPINGS = {
    "fDOF": fDOF
}