如何隔离轮廓中的所有内容，缩放比例并测试与图像的相似性？

Question

我正在做一个项目，只是为了娱乐，我的目标是玩在线扑克，并让程序识别桌子上的纸牌。我正在将OpenCV与python一起使用来隔离卡片将要放置的区域。我已经能够拍摄该区域的图像，对其进行灰度处理并对其进行阈值处理，并在卡的边缘周围绘制轮廓。我现在停留在前进的方向。

到目前为止，这是我的代码：

import cv2
from PIL import ImageGrab
import numpy as np

def processed(image):
    grayscaled = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresholded = cv2.Canny(grayscaled, threshold1 = 200, threshold2 = 200)

    return thresholded

def drawcard1():
    screen = ImageGrab.grab(bbox = (770,300,850,400))

    processed_img = processed(np.array(screen))  

    outside_contour, dummy = cv2.findContours(processed_img.copy(), 0,2)

    colored = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2BGR)

    cv2.drawContours(colored, outside_contour, 0, (0,255,0),2)     

    cv2.imshow('resized_card', colored)

while True:
    drawcard1()



    if cv2.waitKey(25) & 0xFF == ord('w'):
        cv2.destroyAllWindows()
        break

这是我到目前为止的结果：

我需要能够吸收轮廓的内部，并去除轮廓之外的任何东西。然后生成的图像应该只是卡，我需要将其缩放到49x68像素。一旦使它可行，我的计划就是获取等级和西装的轮廓，并用白色像素填充，然后将其与一组图像进行比较，以确定最佳拟合。

我对OpenCV和图像处理非常陌生，但是我发现这些东西令人着迷！我已经可以通过Google达到这一目标，但是这次我什么也找不到。

这是我目前用来替换游戏的图像：

这是我用来比较桌卡的图像之一：

Answer 1

这种情况非常适合template matching。这个想法是在较大图像中搜索并找到模板图像的位置。为了执行此方法，模板在输入图像上滑动（类似于2D卷积），在此执行比较方法以确定像素相似度。这是模板匹配的基本思想。不幸的是，这种基本方法有缺陷，因为它仅在模板图像大小与输入图像中所需的项目相同时才起作用。因此，如果您的模板图像小于要在输入图像中找到的所需区域，则此方法将无效。

要解决此限制，我们可以使用np.linspace()通过动态缩放图像来实现缩放比例模板匹配。每次迭代时，我们都会调整输入图像的大小并跟踪比率。我们继续调整大小，直到模板图像的大小大于调整大小的图像，同时跟踪最高的相关值。相关值越高，匹配越好。遍历各种尺度后，我们找到匹配度最大的比率，然后计算边界框的坐标以确定ROI。

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使用模板图片：

此处检测到的卡以绿色突出显示。要可视化动态模板匹配的过程，请取消注释代码中的部分。

代码

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 and convert to grayscale
template = cv2.imread('template.png')
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
(tH, tW) = template.shape[:2]
cv2.imshow("template", template)

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

# Dynamically rescale image for better template matching
for scale in np.linspace(0.1, 3.0, 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

    # Threshold resized image and apply template matching
    thresh = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
    detected = cv2.matchTemplate(thresh, template, cv2.TM_CCOEFF)
    (_, max_val, _, max_loc) = cv2.minMaxLoc(detected)

    # Uncomment this section for visualization
    '''
    clone = np.dstack([thresh, thresh, thresh])
    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(50)
    '''

    # 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
cv2.rectangle(original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 5)
cv2.imshow('detected', original_image)
cv2.imwrite('detected.png', original_image)
cv2.waitKey(0)