如何使用 OpenCV 从图像中删除特定标签/贴纸/对象？

Question

我有数百张珠宝产品的图片。其中一些带有“畅销书”标签。标签的位置因图像而异。我想遍历所有图像，如果图像具有此标签，则将其删除。结果图像将在移除对象的像素上渲染背景。

带有标签/贴纸/对象的图像示例：

要移除的标签/贴纸/对象：

import numpy as np
import cv2 as cv

img = plt.imread('./images/001.jpg')
sticker = plt.imread('./images/tag.png',1)
diff_im = cv2.absdiff(img, sticker)

我希望得到的图像是这样的：

Answer 1

这是使用修改后的模板匹配方法的方法。这是总体策略：

• 加载模板，转换为灰度，进行canny边缘检测
• 加载原始图像，转换为灰度
• 不断重新缩放图像，使用边缘应用模板匹配，并跟踪相关系数（值越高表示匹配越好）
• 找到最适合边界框的坐标，然后删除不需要的 ROI

首先，我们加载模板并执行 Canny 边缘检测。应用带有边缘的模板匹配而不是原始图像可消除颜色变化差异并提供更稳健的结果。从模板图像中提取边缘：

接下来，我们不断缩小图像并在调整后的图像上应用模板匹配。我使用旧答案在每次调整大小时保持纵横比。这是策略的可视化

我们调整图像大小的原因是因为使用的标准模板匹配cv2.matchTemplate不会健壮，如果模板和图像的尺寸不匹配，可能会产生误报。为了克服这个维度问题，我们使用了这种修改后的方法：

• 以各种较小的比例连续调整输入图像的大小
• 使用模板匹配cv2.matchTemplate并跟踪最大相关系数
• 具有最大相关系数的比率/尺度将具有最佳匹配的 ROI

获得 ROI 后，我们可以通过使用白色填充矩形来“删除”徽标

cv2.rectangle(final, (start_x, start_y), (end_x, end_y), (255,255,255), -1)

import cv2
import numpy as np

# Resizes a image and maintains aspect ratio
def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    # Grab the image size and initialize dimensions
    dim = None
    (h, w) = image.shape[:2]

    # Return original image if no need to resize
    if width is None and height is None:
        return image

    # We are resizing height if width is none
    if width is None:
        # Calculate the ratio of the height and construct the dimensions
        r = height / float(h)
        dim = (int(w * r), height)
    # We are resizing width if height is none
    else:
        # Calculate the ratio of the 0idth and construct the dimensions
        r = width / float(w)
        dim = (width, int(h * r))

    # Return the resized image
    return cv2.resize(image, dim, interpolation=inter)

# Load template, convert to grayscale, perform canny edge detection
template = cv2.imread('template.PNG')
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
cv2.imshow("template", template)

# Load original image, convert to grayscale
original_image = cv2.imread('1.jpg')
final = original_image.copy()
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
found = None

# Dynamically rescale image for better template matching
for scale in np.linspace(0.2, 1.0, 20)[::-1]:

    # Resize image to scale and keep track of ratio
    resized = maintain_aspect_ratio_resize(gray, width=int(gray.shape[1] * scale))
    r = gray.shape[1] / float(resized.shape[1])

    # Stop if template image size is larger than resized image
    if resized.shape[0] < tH or resized.shape[1] < tW:
        break

    # Detect edges in resized image and apply template matching
    canny = cv2.Canny(resized, 50, 200)
    detected = cv2.matchTemplate(canny, template, cv2.TM_CCOEFF)
    (_, max_val, _, max_loc) = cv2.minMaxLoc(detected)

    # Uncomment this section for visualization
    '''
    clone = np.dstack([canny, canny, canny])
    cv2.rectangle(clone, (max_loc[0], max_loc[1]), (max_loc[0] + tW, max_loc[1] + tH), (0,255,0), 2)
    cv2.imshow('visualize', clone)
    cv2.waitKey(0)
    '''

    # Keep track of correlation value
    # Higher correlation means better match
    if found is None or max_val > found[0]:
        found = (max_val, max_loc, r)

# Compute coordinates of bounding box
(_, max_loc, r) = found
(start_x, start_y) = (int(max_loc[0] * r), int(max_loc[1] * r))
(end_x, end_y) = (int((max_loc[0] + tW) * r), int((max_loc[1] + tH) * r))

# Draw bounding box on ROI to remove
cv2.rectangle(original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 2)
cv2.imshow('detected', original_image)

# Erase unwanted ROI (Fill ROI with white)
cv2.rectangle(final, (start_x, start_y), (end_x, end_y), (255,255,255), -1)
cv2.imshow('final', final)
cv2.waitKey(0)