app.py

import cv2
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as splin
from numba import jit
import gradio as gr

@jit(nopython=True)
def build_poisson_sparse_matrix(ys, xs, im2var, img_s, img_t, mask):
    nnz = len(ys)
    img_s_h, img_s_w = img_s.shape
    A_data = np.zeros(16 * nnz, dtype=np.float64)
    A_rows = np.zeros(16 * nnz, dtype=np.int32)
    A_cols = np.zeros(16 * nnz, dtype=np.int32)
    b = np.zeros(4 * nnz, dtype=np.float64)
    
    offsets = np.array([(0, 1), (0, -1), (1, 0), (-1, 0)])
    
    idx = 0
    for n in range(nnz):
        y, x = ys[n], xs[n]
        for i in range(4):
            dy, dx = offsets[i]
            n_y, n_x = y + dy, x + dx
            e = 4 * n + i
            
            if 0 <= n_y < img_s_h and 0 <= n_x < img_s_w:
                A_data[idx] = 1
                A_rows[idx] = e
                A_cols[idx] = im2var[y, x]
                idx += 1
                
                b[e] = img_s[y, x] - img_s[n_y, n_x]
                
                if im2var[n_y, n_x] != -1:
                    A_data[idx] = -1
                    A_rows[idx] = e
                    A_cols[idx] = im2var[n_y, n_x]
                    idx += 1
                else:
                    b[e] += img_t[n_y, n_x]
    
    return A_data[:idx], A_rows[:idx], A_cols[:idx], b

def poisson_blend_fast_jit(img_s: np.ndarray, mask: np.ndarray, img_t: np.ndarray) -> np.ndarray:
    nnz = np.sum(mask > 0)
    im2var = np.full(mask.shape, -1, dtype=np.int32)
    im2var[mask > 0] = np.arange(nnz)
    
    ys, xs = np.nonzero(mask)
    
    A_data, A_rows, A_cols, b = build_poisson_sparse_matrix(ys, xs, im2var, img_s, img_t, mask)
    
    A = sp.csr_matrix((A_data, (A_rows, A_cols)), shape=(4*nnz, nnz))
    v = splin.lsqr(A, b)[0]
    
    img_t_out = img_t.copy()
    img_t_out[mask > 0] = v[im2var[mask > 0]]
    
    return np.clip(img_t_out, 0, 1)

@jit(nopython=True)
def neighbours(i: int, j: int, max_i: int, max_j: int):
    pairs = []
    for n in (-1, 1):
        if 0 <= i+n <= max_i:
            pairs.append((i+n, j))
        if 0 <= j+n <= max_j:
            pairs.append((i, j+n))
    return pairs

@jit(nopython=True)
def build_mixed_blend_sparse_matrix(ys, xs, im2var, img_s, img_t, mask):
    nnz = len(ys)
    img_s_h, img_s_w = img_s.shape
    A_data = np.zeros(8 * nnz, dtype=np.float64)
    A_rows = np.zeros(8 * nnz, dtype=np.int32)
    A_cols = np.zeros(8 * nnz, dtype=np.int32)
    b = np.zeros(4 * nnz, dtype=np.float64)
    
    idx = 0
    e = 0
    for n in range(nnz):
        y, x = ys[n], xs[n]
        for n_y, n_x in neighbours(y, x, img_s_h-1, img_s_w-1):
            ds = img_s[y, x] - img_s[n_y, n_x]
            dt = img_t[y, x] - img_t[n_y, n_x]
            d = ds if abs(ds) > abs(dt) else dt
            
            A_data[idx] = 1
            A_rows[idx] = e
            A_cols[idx] = im2var[y, x]
            idx += 1
            
            b[e] = d
            
            if im2var[n_y, n_x] != -1:
                A_data[idx] = -1
                A_rows[idx] = e
                A_cols[idx] = im2var[n_y, n_x]
                idx += 1
            else:
                b[e] += img_t[n_y, n_x]
            e += 1
    
    return A_data[:idx], A_rows[:idx], A_cols[:idx], b[:e]

def mixed_blend_fast_jit(img_s: np.ndarray, mask: np.ndarray, img_t: np.ndarray) -> np.ndarray:
    nnz = np.sum(mask > 0)
    im2var = np.full(mask.shape, -1, dtype=np.int32)
    im2var[mask > 0] = np.arange(nnz)
    
    ys, xs = np.nonzero(mask)
    
    A_data, A_rows, A_cols, b = build_mixed_blend_sparse_matrix(ys, xs, im2var, img_s, img_t, mask)
    
    A = sp.csr_matrix((A_data, (A_rows, A_cols)), shape=(len(b), nnz))
    
    v = splin.spsolve(A.T @ A, A.T @ b)
    
    img_t_out = img_t.copy()
    img_t_out[mask > 0] = v[im2var[mask > 0]]
    
    return np.clip(img_t_out, 0, 1)

def _2d_gaussian(sigma: float) -> np.ndarray:
    ksize = np.int64(np.ceil(sigma)*6+1)
    gaussian_1d = cv2.getGaussianKernel(ksize, sigma)
    return gaussian_1d * np.transpose(gaussian_1d)

def _low_pass_filter(img: np.ndarray, sigma: float) -> np.ndarray:
    return cv2.filter2D(img, -1, _2d_gaussian(sigma))

def _high_pass_filter(img: np.ndarray, sigma: float) -> np.ndarray:
    return img - _low_pass_filter(img, sigma)

def _gaus_pyramid(img: np.ndarray, depth: int, sigma: int):
    _im = img.copy()
    
    pyramid = []
    for d in range(depth-1):
        _im = _low_pass_filter(_im.copy(), sigma)
        pyramid.append(_im)
        _im = cv2.pyrDown(_im)
        
    return pyramid 

def _lap_pyramid(img: np.ndarray, depth: int, sigma: int):
    _im = img.copy()
    
    pyramid = []
    for d in range(depth-1):
        lap = _high_pass_filter(_im.copy(), sigma)
        pyramid.append(lap)
        _im = cv2.pyrDown(_im)
        
    return pyramid 

def _blend(img1: np.ndarray, img2: np.ndarray, mask: np.ndarray) -> np.ndarray:
    return img1 * mask + img2 * (1.0 - mask)

def laplacian_blend(img1: np.ndarray, img2: np.ndarray, mask: np.ndarray, depth: int, sigma: int) -> np.ndarray:
    mask_gaus_pyramid = _gaus_pyramid(mask, depth, sigma)
    img1_lap_pyramid, img2_lap_pyramid = _lap_pyramid(img1, depth, sigma), _lap_pyramid(img2, depth, sigma)

    blended = [_blend(obj, bg, mask) for obj, bg, mask in zip(img1_lap_pyramid, img2_lap_pyramid, mask_gaus_pyramid)][::-1]
    
    h, w = blended[0].shape[:2]
    
    img1 = cv2.resize(img1, (w, h))
    img2 = cv2.resize(img2, (w, h))
    mask = cv2.resize(mask, (w, h))

    blanded_img = _blend(img1, img2, mask)
    blanded_img = cv2.resize(blanded_img, blended[0].shape[:2])
    
    imgs = []
    for d in range(0, depth-1):
        gaussian_img = _low_pass_filter(blanded_img.copy(), sigma)
        reconstructed_img = cv2.add(blended[d], gaussian_img)
        
        imgs.append(reconstructed_img)
        blanded_img = cv2.pyrUp(reconstructed_img)
        
    return np.clip(imgs[-1], 0, 1)

def get_image(img_path: str, mask: bool=False, scale: bool=True) -> np.array:
    """
    Gets image in appropriate format
    """
    if isinstance(img_path, np.ndarray):
        img = img_path
    else:
        img = cv2.imread(img_path)
    
    if mask:
        if len(img.shape) == 3:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        _, binary_mask = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
        return np.where(binary_mask == 255, 1, 0)
    
    if img.shape[-1] == 3:  # Check if the image has 3 channels
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    if scale:
        return img.astype('double') / 255.0
    
    return img

def blend_images(bg_img, obj_img, mask_img, method):
    bg_img = get_image(bg_img)
    obj_img = get_image(obj_img)
    mask_img = get_image(mask_img, mask=True)

    if method == "Poisson":
        blend_img = np.zeros_like(bg_img)
        for b in range(3):
            blend_img[:,:,b] = poisson_blend_fast_jit(obj_img[:,:,b], mask_img, bg_img[:,:,b].copy())
    elif method == "Mixed Gradient":
        blend_img = np.zeros_like(bg_img)
        for b in range(3):
            blend_img[:,:,b] = mixed_blend_fast_jit(obj_img[:,:,b], mask_img, bg_img[:,:,b].copy())
    elif method == "Laplacian":
        mask_stack = np.stack((mask_img.astype(float),) * 3, axis=-1)
        blend_img = laplacian_blend(obj_img, bg_img, mask_stack, 5, 25.0)
    
    return (blend_img * 255).astype(np.uint8)

examples = [
    ["img1.jpg", "img2.jpg", "mask1.jpg", "Poisson"],
    ["img3.jpg", "img4.jpg", "mask2.jpg", "Mixed Gradient"],
    ["img6.jpg", "img9.jpg", "mask3.jpg", "Laplacian"]
]

iface = gr.Interface(
    fn=blend_images,
    inputs=[
        gr.Image(label="Background Image", type="numpy"),
        gr.Image(label="Object Image", type="numpy"),
        gr.Image(label="Mask Image", type="numpy"),
        gr.Radio(["Poisson", "Mixed Gradient", "Laplacian"], label="Blending Method")
    ],
    outputs=gr.Image(label="Blended Image"),
    title="Image Blending with Examples",
    description="Choose from example images or upload your own to blend using different methods.",
    examples=examples,
    cache_examples=True
)

iface.launch()