Question

因此，此代码能够将其标识的各种房间划分为不同的颜色，如下所示。问题是，我如何获得彩色房间的面积（就像那些蓝色房间一样）。客房的比例为1m：150m。

第一幅图像是我需要测量的输出，第二幅空间是我用来运行代码的图像，第三幅图像是供参考的原始图像。预先感谢。

import numpy as np

def find_rooms(img, noise_reduction=10, corners_threshold=0.0000001,
               room_close=2, gap_in_wall_threshold=0.000001):

    # :param img: grey scale image of rooms, already eroded and doors removed etc.
    # :param noise_reduction: Amount of noise removed.
    # :param corners_threshold: Corners to retained, higher value = more of house removed.
    # :param room_close: Maximum line length to add to close off open doors.
    # :param gap_in_wall_threshold: Minimum number of pixels to identify component as room instead of hole in the wall.
    # :return: rooms: list of numpy arrays containing boolean masks for each detected room
    # colored_house: Give room a color.

    assert 0 <= corners_threshold <= 1
    # Remove noise left from door removal

    img[img < 128] = 0
    img[img > 128] = 255
    contours, _ = cv2.findContours(~img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    mask = np.zeros_like(img)
    for contour in contours:
        area = cv2.contourArea(contour)
        if area > noise_reduction:
            cv2.fillPoly(mask, [contour], 255)

    img = ~mask

    # Detect corners (you can play with the parameters here)
    #harris corner detection
    dst = cv2.cornerHarris(img, 4,3,0.000001)
    dst = cv2.dilate(dst,None)
    corners = dst > corners_threshold * dst.max()

    # Draw lines to close the rooms off by adding a line between corners on the same x or y coordinate
    # This gets some false positives.
    # Can try disallowing drawing through other existing lines, need to test.
    for y,row in enumerate(corners):
        x_same_y = np.argwhere(row)
        for x1, x2 in zip(x_same_y[:-1], x_same_y[1:]):

            if x2[0] - x1[0] < room_close:
                color = 0
                cv2.line(img, (x1, y), (x2, y), color, 1)

    for x,col in enumerate(corners.T):
        y_same_x = np.argwhere(col)
        for y1, y2 in zip(y_same_x[:-1], y_same_x[1:]):
            if y2[0] - y1[0] < room_close:
                color = 0
                cv2.line(img, (x, y1), (x, y2), color, 1)


    # Mark the outside of the house as black
    contours, _ = cv2.findContours(~img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]
    biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]
    mask = np.zeros_like(mask)
    cv2.fillPoly(mask, [biggest_contour], 255)
    img[mask == 0] = 0

    # Find the connected components in the house
    ret, labels = cv2.connectedComponents(img)
    img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
    unique = np.unique(labels)
    rooms = []
    for label in unique:
        component = labels == label
        if img[component].sum() == 0 or np.count_nonzero(component) < gap_in_wall_threshold:
            color = 0
        else:
            rooms.append(component)
            color = np.random.randint(0, 255, size=3)
        img[component] = color

    return rooms, img

#Read gray image
img = cv2.imread('output16.png', 0)
rooms, colored_house = find_rooms(img.copy())
cv2.imshow('result', colored_house)
cv2.waitKey()
cv2.destroyAllWindows()

Answer 1

好的，假设您使用OpenCV读取了分割后的图片：

import cv2
import numpy as np

# reading the segmented picture in coloured mode
image = cv2.imread("path/to/segmented/coloured/picture.jpg", cv2.IMREAD_COLOR)

现在，假设您知道整张图片的大小（以平方米为单位），那么例如，如果某张图片的总尺寸为150m x 70m，则总尺寸为150x70 =10500m²。让我们将其声明为变量：

total_size = 10500

您还想知道图片中的像素总数。例如，如果您的图片为750 * 350像素，则您有：262500像素。您可以执行以下操作：

total_number_of_pixels = image.shape[0]*image.shape[1]

现在，正如我在评论中说的那样，您还想知道分割后的图片中每种唯一颜色的像素数，您可以使用以下方法完成

：

# count all occurrences of unique colours in your picture
unique, counts = np.unique(image.reshape(-1, image.shape[2]), axis=0, return_counts=True)

coloured_pixel_counts = sorted(zip(unique, counts), key=lambda x: x[1]))

现在，您所要做的只是交叉乘法，可以使用以下方法完成此操作：

rooms = []
for colour, pixel_count in coloured_pixel_counts:
     rooms.append((colour, (pixel_count/total_number_of_pixels)*total_size))

您现在应该拥有所有颜色的列表，以及每种颜色的房间的近似大小（以平方米为单位）。

现在，请注意，但是，您可能必须将此列表子集化为您感兴趣的颜色，因为某些颜色似乎并没有真正链接到分段图片中的房间...

再次，请问是否有任何不清楚的地方！

Answer 2

因此，测量将基于像素，并且您将需要知道要“测量”的颜色的RGB值的最大和最小范围。我在您的图片上运行此代码，以查找绿色区域占房子整个区域的百分比，并且得到以下结果：

过滤后的像素数为：331213，占房屋的％5

import cv2
import numpy as np
import math

img = cv2.imread('22I7X.png')
#Defining wanted color range
filteredColorMin = np.array([36,0,0], np.uint8) #Min range
filteredColorMax = np.array([70, 255,255], np.uint8) #High range
#Find all the pixels in the wanted color range
dst = cv2.inRange(img, filteredColorMin, filteredColorMax)
#count non-zero values from filtered range
numFilteredColor = cv2.countNonZero(dst)
#Getting total number of pixels in image to get the percentage of the filtered pixels from the total pixels
numTotalPixels=img.shape[0] *img.shape[1]

print('The number of filtered pixels is: ' + str(numFilteredColor) + " Which counts for %" + str(math.ceil((numFilteredColor/numTotalPixels)*100)) + " of the house")
cv2.imshow("original image",img)
cv2.waitKey(0)

如何根据颜色测量房间的大小

2 个答案: