在嘈雜的二值影像中檢測不同的形狀

我想檢測此影像中的圓圈和五個正方形：

這是我目前使用的代碼的相關部分：

# detect shapes in black-white RGB formatted cv2 image
def detect_shapes(img, approx_poly_accuracy=APPROX_POLY_ACCURACY):

    res_dict = {
        "rectangles": [],
        "squares": []
    }

    vis = img.copy()

    shape = img.shape

    height, width = shape[0], shape[1]

    total_area = height * width

    # Morphological closing: get rid of holes
    # img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))

    # Morphological opening: get rid of extensions at the border of the objects
    # img = cv2.morphologyEx(img, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (121, 121)))

    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # cv2.imshow('intermediate', img)
    # cv2.waitKey(0)

    contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    logging.info("Number of found contours for shape detection: {0}".format(len(contours)))

    # vis = img.copy()
    # cv2.drawContours(vis, contours, -1, (0, 255, 0), 2)
    cv2.imshow('vis', vis)
    cv2.waitKey(0)

    for contour in contours:

        area = cv2.contourArea(contour)

        if area < MIN_SHAPE_AREA:
            logging.warning("Area too small: {0}. Skipping.".format(area))
            continue

        if area > MAX_SHAPE_AREA_RATIO * total_area:
            logging.warning("Area ratio too big: {0}. Skipping.".format(area / total_area))
            continue

        approx = cv2.approxPolyDP(contour, approx_poly_accuracy * cv2.arcLength(contour, True), True)

        cv2.drawContours(vis, [approx], -1, (0, 0, 255), 2)

        la = len(approx)

        # find the center of the shape

        M = cv2.moments(contour)

        if M['m00'] == 0.0:
            logging.warning("Unable to compute shape center! Skipping.")
            continue

        x = int(M['m10'] / M['m00'])
        y = int(M['m01'] / M['m00'])

        if la < 3:
            logging.warning("Invalid shape detected! Skipping.")
            continue

        if la == 3:
            logging.info("Triangle detected at position {0}".format((x, y)))

        elif la == 4:
            logging.info("Quadrilateral detected at position {0}".format((x, y)))

            if approx.shape != (4, 1, 2):
                raise ValueError("Invalid shape before reshape to (4, 2): {0}".format(approx.shape))

            approx = approx.reshape(4, 2)

            r_check, data = check_rect_or_square(approx)

            blob_data = {"position": (x, y), "approx": approx}

            blob_data.update(data)

            if r_check == 2:
                res_dict["squares"].append(blob_data)

            elif r_check == 1:
                res_dict["rectangles"].append(blob_data)

        elif la == 5:
            logging.info("Pentagon detected at position {0}".format((x, y)))

        elif la == 6:
            logging.info("Hexagon detected at position {0}".format((x, y)))

        else:
            logging.info("Circle, ellipse or arbitrary shape detected at position {0}".format((x, y)))

        cv2.drawContours(vis, [contour], -1, (0, 255, 0), 2)
        cv2.imshow('vis', vis)
        cv2.waitKey(0)

    logging.info("res_dict: {0}".format(res_dict))

    return res_dict

問題是：如果我將approx_poly_accuracy引數設定得太高，則圓被檢測為多邊形（例如，六邊形或八邊形）。如果我將其設定得太低，則正方形不會被檢測為正方形，而是五角形，例如：

紅線是近似輪廓，綠線是原始輪廓。文本被檢測為一個完全錯誤的輪廓，它不應該被逼近到這個水平（我不太關心文本，但如果它被檢測為一個少于 5 個頂點的多邊形，它將是一個誤報）。

For a human, it is obvious that the left object is a circle and that the five objects on the right are squares, so there should be a way to make the computer realize that with high accuracy too. How do I modify this code to properly detect all objects?

What I already tried:

• Apply filters like MedianFilter. It made things worse, because the rounded edges of the squares promoted them being detected as a polygon with more than four vertices.
• Variate the approx_poly_accuracy parameter. There is no value that fits my purposes, considering that some other images might even have a some more noise.
• 找到 RDP 演算法的實作，它允許我指定精確數量的輸出點。這將允許我為一定數量的點（例如在 3..10 范圍內）計算建議的多邊形，然后計算(A_1 A_2) / A_common - 1使用面積而不是弧長作為精度，這可能會導致更好的結果。我還沒有找到一個很好的實作。我現在將嘗試使用數值求解器方法來動態計算 RDP 的正確 epsilon 引數。但是，這種方法并不是真正干凈和有效。我會盡快在此處發布結果。如果有人有更好的方法，請告訴我。

uj5u.com熱心網友回復：

一種可能的方法是根據這些屬性計算一些 blob 描述符和過濾 blob。例如，您可以計算 blob 的縱橫比、（近似）頂點數和面積。步驟非常簡單：

1. 加載影像并將其轉換為灰度。
2. （反轉）影像的閾值。讓我們確保斑點是白色的。
3. 獲取二值影像的輪廓。
4. 計算兩個特征：縱橫比和頂點數
5. 根據這些特征過濾斑點

讓我們看看代碼：

# Imports:
import cv2
import numpy as np
# Load the image:
fileName = "yh6Uz.png"
path = "D://opencvImages//"

# Reading an image in default mode:
inputImage = cv2.imread(path   fileName)
# Prepare a deep copy of the input for results:
inputImageCopy = inputImage.copy()

# Grayscale conversion:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Threshold via Otsu:
_, binaryImage = cv2.threshold(grayscaleImage, 0, 255, cv2.THRESH_BINARY_INV   cv2.THRESH_OTSU)

# Find the blobs on the binary image:
contours, hierarchy = cv2.findContours(binaryImage, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Store the bounding rectangles here:
circleData = []
squaresData = []

好吧。到目前為止，我已經在輸入影像上加載、閾值化和計算輪廓。此外，我準備了兩個串列來存盤正方形和圓形的邊界框。讓我們創建特征過濾器：

for i, c in enumerate(contours):

    # Get blob perimeter:
    currentPerimeter = cv2.arcLength(c, True)
    # Approximate the contour to a polygon:
    approx = cv2.approxPolyDP(c, 0.04 * currentPerimeter, True)
    # Get polygon's number of vertices:
    vertices = len(approx)
    # Get the polygon's bounding rectangle:
    (x, y, w, h) = cv2.boundingRect(approx)

    # Compute bounding box area:
    rectArea = w * h

    # Compute blob aspect ratio:
    aspectRatio = w / h

    # Set default color for bounding box:
    color = (0, 0, 255)

我遍歷每個輪廓并計算當前 blob 的perimeter和polygon approximation。此資訊用于近似計算 blob vertices。該aspect ratio計算是很容易的。我第一次拿到BLOB的邊框，并得到其尺寸：左上角(x, y)，width和height。縱橫比就是寬度除以高度。

正方形和圓形非常緊湊。這意味著它們的縱橫比應該接近于1.0。然而，正方形有精確的4頂點，而（近似的）圓有更多的頂點。我使用此資訊來構建一個非常基本的功能過濾器。它首先檢查aspect ratio，area然后檢查數量vertices。我使用了理想特征和真實特征之間的差異。該引數delta調整過濾器容差。一定還要過濾微小的斑點，為此使用該區域：

    # Set minimum tolerable difference between ideal
    # feature and actual feature:
    delta = 0.15

    # Set the minimum area:
    minArea = 400

    # Check features, get blobs with aspect ratio
    # close to 1.0 and area > min area:
    if (abs(1.0 - aspectRatio) < delta) and (rectArea > minArea):
        print("Got target blob.")

        # If the blob has 4 vertices, it is a square:
        if vertices == 4:
            print("Target is square")

            # Save bounding box info:
            tempTuple = (x, y, w, h)
            squaresData.append(tempTuple)

            # Set green color:
            color = (0, 255, 0)

        # If the blob has more than 6 vertices, it is a circle:
        elif vertices > 6:
            print("Target is circle")

            # Save bounding box info:
            tempTuple = (x, y, w, h)
            circleData.append(tempTuple)

            # Set blue color:
            color = (255, 0, 0)

        # Draw bounding rect:
        cv2.rectangle(inputImageCopy, (int(x), int(y)), (int(x   w), int(y   h)), color, 2)

        cv2.imshow("Rectangles", inputImageCopy)
        cv2.waitKey(0)

這是結果。正方形用綠色矩形標識，圓形用藍色矩形標識。此外，邊界框分別存盤在squaresData和 中circleData：

標籤：Python opencv 形状 轮廓

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