Question

我有投资回报率和图像。我必须用我拥有的图像填充ROI。图像应根据ROI的形状和大小进行缩放，并且应该在不重复图像的情况下填充整个ROI。如何使用opencv实现这一目标？ opencv中是否有任何方法可以实现这一目标？

假设此白色部分是我的投资回报率和

这是我的输入图片

有没有使用imageMagick ???

的解决方案

Answer 1

找到一个形状在另一个形状内的最佳拟合并非易事，但如果您能够找到不理想的结果，您可以执行以下操作：

import cv2
import numpy as np
from matplotlib import pyplot as plt

bg_contours, bg_hierarchy = cv2.findContours(bg_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
bg_contour = bg_contours[0]
bg_ellipse = cv2.fitEllipse(bg_contour)

p_contours, p_hierarchy = cv2.findContours(fruit_alpha, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

pear_hull = cv2.convexHull(p_contours[0])
pear_ellipse = cv2.fitEllipse(pear_hull)

min_ratio = min(bg_ellipse[1][0] / pear_ellipse[1][0], bg_ellipse[1][1] / pear_ellipse[1][1])

x_shift = bg_ellipse[0][0] - pear_ellipse[0][0] * min_ratio
y_shift = bg_ellipse[0][1] - pear_ellipse[0][1] * min_ratio

（启发式）调整水果轮廓的大小，从基于椭圆的初始猜测开始，使用轮廓进行细化（这可以改进，但这是一个非平凡的优化问题，你可以看得更多here）：

r_contour = np.array([[[int(j) for j in i[0]]] for i in min_ratio * p_contours[max_c_ix]])

min_dist, bad_pt = GetMinDist(outer_contour=bg_contour, inner_contour=r_contour, offset=(int(x_shift), int(y_shift)))
mask_size = max(bg_ellipse[1][0], bg_ellipse[1][1])
scale = min_ratio * (mask_size + min_dist) / mask_size

r_contour = np.array([[[int(j) for j in i[0]]] for i in scale * p_contours[max_c_ix]])

使用Alpha通道合并图像：

combined = CombineImages(bg, fruit_rgb, fruit_alpha, scale, (int(x_shift), int(y_shift)))

效用函数：

def GetMinDist(outer_contour, inner_contour, offset):
    min_dist = 10000
    bad_pt = (0,0)
    for i_pt in inner_contour:
        #pt = (float(i_pt[0][0]), float(i_pt[0][1]))
        pt = (i_pt[0][0] + int(offset[0]), i_pt[0][1] + int(offset[1]))
        dst = cv2.pointPolygonTest(outer_contour, pt, True)
        if dst < min_dist:
            min_dist = dst
            bad_pt = pt
    return min_dist, bad_pt

def CombineImages(mask_img, fruit_img, fruit_alpha, scale, offset):
    mask_height, mask_width, mask_dim = mask_img.shape
    combined_img = np.copy(mask_img)
    resized_fruit = np.copy(mask_img)
    resized_fruit[:] = 0
    resized_alpha = np.zeros( (mask_height, mask_width), fruit_alpha.dtype)
    f_height, f_width, f_dim = fruit_img.shape
    r_fruit = cv2.resize(fruit_img, (int(f_width*scale), int(f_height*scale)) )
    r_alpha = cv2.resize(fruit_alpha, (int(f_width*scale), int(f_height*scale)) )
    height, width, channels = r_fruit.shape
    roi_x_from = offset[0]
    roi_x_to   = offset[0] + width
    roi_y_from = offset[1]
    roi_y_to   = offset[1] + height
    resized_fruit[roi_y_from:roi_y_to, roi_x_from:roi_x_to, :] = r_fruit
    resized_alpha[roi_y_from:roi_y_to, roi_x_from:roi_x_to] = r_alpha
    for y in range(0,mask_height):
        for x in range(0, mask_width):
            if resized_alpha[y,x] > 0:
                combined_img[y,x,:] = resized_fruit[y,x,:]

    return combined_img

我希望有所帮助。

（我省略了无助于理解流程的部分代码）

将图像拟合到ROI

1 个答案: