如何使用OpenCV从图像中删除特定的标签/贴纸/对象?

问题描述：

我有数百幅珠宝产品图片.其中一些带有畅销书"标签.标签的位置因图像而异.我要遍历所有图像，如果图像具有此标签，则将其删除.生成的图像将在移除的对象的像素上渲染背景.

I have hundreds of images of jewelry products. Some of them have "best-seller" tag on them. The position of the tag is different from image to image. I want iterate over all images, and if an image has this tag then remove it. The resulted image will render the background over the removed object's pixels.

带有标签/贴纸/对象的图像示例:

Example of an image with Tag/sticker/object:

要删除的标签/贴纸/对象:

Tag/sticker/object to remove:

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)

我希望结果图像如下所示:

I want the resulted image to be like this:

答

这是使用修改后的

Here's an method using a modified Template Matching approach. Here's the overall strategy:

加载模板，转换为灰度，执行Canny边缘检测
加载原始图像，转换为灰度
连续缩放图像，使用边缘应用模板匹配，并跟踪相关系数(值越大，匹配越好)
找到最合适边界框的坐标，然后删除不想要的ROI

首先，我们加载模板并执行Canny边缘检测.应用与边缘匹配的模板而不是原始图像，可以消除颜色变化的差异，并提供更可靠的结果.从模板图像中提取边缘:

To begin, we load in the template and perform Canny edge detection. Applying template matching with edges instead of the raw image removes color variation differences and gives a more robust result. Extracting edges from template image:

接下来，我们继续按比例缩小图像并将模板匹配应用于调整大小后的图像.我使用旧答案来保持每次调整尺寸的长宽比.这是该策略的可视化图

Next we continuously scale down the image and apply template matching on our resized image. I maintain aspect ratio with each resize using a old answer. Here's a visualization of the strategy

我们调整图像大小的原因是因为使用cv2.matchTemplate进行标准模板匹配将不可靠，并且如果模板和图像的尺寸不匹配，则可能会给出误报.为了克服这个尺寸问题，我们使用以下修改的方法:

The reason we resize the image is because standard template matching using cv2.matchTemplate will not be robust and may give false positives if the dimensions of the template and the image do not match. To overcome this dimension issue, we use this modified approach:

以各种较小的比例连续调整输入图像的大小
使用cv2.matchTemplate应用模板匹配并跟踪最大相关系数
相关系数最大的比率/比例将具有最佳的投资回报率

Continuously resize the input image at various smaller scales
Apply template matching using cv2.matchTemplate and keep track of the largest correlation coefficient
The ratio/scale with the largest correlation coefficient will have the best matched ROI

获得ROI后，我们可以使用

Once the ROI is obtained, we can "delete" the logo by filling in the rectangle with white using

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)

如何使用OpenCV从图像中删除特定的标签/贴纸/对象?

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