問(wèn)題描述

我有數(shù)百?gòu)堉閷毊a(chǎn)品的圖片.其中一些帶有暢銷書(shū)"標(biāo)簽.標(biāo)簽的位置因圖像而異.我想遍歷所有圖像，如果圖像具有此標(biāo)簽，則將其刪除.生成的圖像將在移除對(duì)象的像素上渲染背景.

帶有標(biāo)簽/貼紙/對(duì)象的圖像示例:

要移除的標(biāo)簽/貼紙/對(duì)象:

將 numpy 導(dǎo)入為 np將 cv2 導(dǎo)入為 cvimg = plt.imread('./images/001.jpg')貼紙 = plt.imread('./images/tag.png',1)diff_im = cv2.absdiff(img，貼紙)

我希望結(jié)果圖像是這樣的:

這是一個(gè)使用修改后的方法

接下來(lái)，我們不斷縮小圖像并在調(diào)整后的圖像上應(yīng)用模板匹配.我使用

我們調(diào)整圖像大小的原因是因?yàn)槭褂?cv2.matchTemplate 的標(biāo)準(zhǔn)模板匹配不可靠，如果模板和圖像的尺寸不匹配，可能會(huì)出現(xiàn)誤報(bào).為了克服這個(gè)維度問(wèn)題，我們使用了這種修改后的方法:

• 以各種更小的比例不斷調(diào)整輸入圖像的大小
• 使用 cv2.matchTemplate 應(yīng)用模板匹配并跟蹤最大相關(guān)系數(shù)
• 具有最大相關(guān)系數(shù)的比率/比例將具有最佳匹配的 ROI

獲得 ROI 后，我們可以通過(guò)使用白色填充矩形來(lái)刪除"徽標(biāo)

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

導(dǎo)入 cv2將 numpy 導(dǎo)入為 np# 調(diào)整圖像大小并保持縱橫比def maintain_aspect_ratio_resize(圖像，寬度=無(wú)，高度=無(wú)，inter=cv2.INTER_AREA):# 獲取圖像大小并初始化維度暗淡 = 無(wú)(h, w) = image.shape[:2]# 如果不需要調(diào)整大小，則返回原始圖像如果寬度為無(wú)且高度為無(wú):返回圖片# 如果寬度沒(méi)有，我們正在調(diào)整高度如果寬度為無(wú):# 計(jì)算高度的比例并構(gòu)造尺寸r = 高度/浮動(dòng)(h)暗淡=(int(w * r)，高度)# 如果沒(méi)有高度，我們正在調(diào)整寬度別的:# 計(jì)算0idth的比例并構(gòu)造維度r = 寬度/浮動(dòng)(w)暗淡=(寬度，int(h * r))# 返回調(diào)整大小的圖像return cv2.resize(圖像，暗淡，插值=inter)# 加載模板，轉(zhuǎn)換為灰度，進(jìn)行canny邊緣檢測(cè)模板 = cv2.imread('template.PNG')模板 = cv2.cvtColor(模板，cv2.COLOR_BGR2GRAY)模板 = cv2.Canny(模板, 50, 200)(tH, tW) = 模板.shape[:2]cv2.imshow("模板", 模板)# 加載原圖，轉(zhuǎn)灰度original_image = cv2.imread('1.jpg')final = original_image.copy()灰色 = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)找到 = 無(wú)# 動(dòng)態(tài)重新縮放圖像以獲得更好的模板匹配對(duì)于 np.linspace(0.2, 1.0, 20)[::-1] 中的比例:# 調(diào)整圖像大小并跟蹤比例調(diào)整大小=維護(hù)縱橫比調(diào)整大小(灰色，寬度=int(gray.shape[1] * scale))r = gray.shape[1]/float(resized.shape[1])# 如果模板圖像大小大于調(diào)整后的圖像，則停止如果調(diào)整大小.shape[0] <tH 或 resized.shape[1] 找到[0]:找到 = (max_val, max_loc, r)# 計(jì)算邊界框的坐標(biāo)(_, max_loc, r) = 找到(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))# 在 ROI 上繪制邊界框以移除cv2.rectangle(original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 2)cv2.imshow('檢測(cè)到', original_image)# 擦除不需要的 ROI(用白色填充 ROI)cv2.rectangle(最終，(start_x，start_y)，(end_x，end_y)，(255,255,255)，-1)cv2.imshow('final', final)cv2.waitKey(0)

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.

Example of an image with Tag/sticker/object:

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)

I want the resulted image to be like this:

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

• Load template, convert to grayscale, perform canny edge detection
• Load original image, convert to grayscale
• Continuously rescale image, apply template matching using edges, and keep track of the correlation coefficient (higher value means better match)
• Find coordinates of best fit bounding box then erase unwanted ROI

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:

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

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:

• 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

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)

這篇關(guān)于如何使用 OpenCV 從圖像中刪除特定的標(biāo)簽/貼紙/對(duì)象?的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！