图像区域的直方图

Question

我想获取 python 中 numpy 图像中某个区域的直方图。我找到了一个关于如何使用面具的解决方案 here.

此解决方案对我没有帮助，因为如果我使用它，我将丢失实际数量的黑色像素。另外，我想要得到的区域不一定是矩形的。

Answer 1

要计算直方图，请使用 np.histogram 函数。它 returns 一个直方图和分箱。因此您可以存储结果并使用它：

hist, bins = np.histogram(arr, bins=bins, range=range)

如果要绘制结果，可以在应用 np.histogram 后使用 plt.bar，只需传递 bins 和 hist:

plt.bar(bins, hist)

另一个选项是使用 matplotlib plt.hist 它计算直方图并根据原始数据绘制它：

plt.hist(arr, bins=bins)

以下是任意形状图像区域直方图的完整示例：

代码：

import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import face
from PIL import Image, ImageDraw

# Let's create test image with different colors
img = np.zeros((300, 300, 3), dtype=np.uint8)
img[0:150, 0:150] = [255, 0, 0]
img[0:150, 150:] = [0, 255, 0]
img[150:, :150] = [0, 0, 255]
img[150:, 150:] = [255, 255, 255]

# define our function for preparing mask
def prepare_mask(polygon, image):
    """Returns binary mask based on input polygon presented as list of coordinates of vertices
    Params:
        polygon (list) - coordinates of polygon's vertices. Ex: [(x1,y1),(x2,y2),...] or [x1,y1,x2,y2,...]
        image (numpy array) - original image. Will be used to create mask of the same size. Shape (H, W, C).
    Output:
        mask (numpy array) - boolean mask. Shape (H, W).
    """
    # create an "empty" pre-mask with the same size as original image
    width = image.shape[1]
    height = image.shape[0]
    mask = Image.new('L', (width, height), 0)
    # Draw your mask based on polygon
    ImageDraw.Draw(mask).polygon(polygon, outline=1, fill=1)
    # Covert to np array
    mask = np.array(mask).astype(bool)
    return mask


def compute_histogram(mask, image):
    """Returns histogram for image region defined by mask for each channel
    Params:
        image (numpy array) - original image. Shape (H, W, C).
        mask (numpy array) - boolean mask. Shape (H, W).
    Output:
        list of tuples, each tuple (each channel) contains 2 arrays: first - computed histogram, the second - bins.

    """
    # Apply binary mask to your array, you will get array with shape (N, C)
    region = image[mask]

    red = np.histogram(region[..., 0].ravel(), bins=256, range=[0, 256])
    green = np.histogram(region[..., 1].ravel(), bins=256, range=[0, 256])
    blue = np.histogram(region[..., 2].ravel(), bins=256, range=[0, 256])

    return [red, green, blue]


def plot_histogram(histograms):
    """Plots histogram computed for each channel.
    Params:
        histogram (list of tuples) - [(red_ch_hist, bins), (green_ch_hist, bins), (green_ch_hist, bins)]
    """

    colors = ['r', 'g', 'b']
    for hist, ch in zip(histograms, colors):
        plt.bar(hist[1][:256], hist[0], color=ch)

# Create some test masks
red_polygon = [(50, 100), (50, 50), (100, 75)]
green_polygon = [(200, 100), (200, 50), (250, 75)]
blue_polygon = [(50, 250), (50, 200), (100, 225)]
white_polygon = [(200, 250), (200, 200), (250, 225)]
polygons = [red_polygon, green_polygon, blue_polygon, white_polygon]

for polygon in polygons:
    mask = prepare_mask(polygon, img)
    histograms = compute_histogram(mask, img)

    # Let's plot our test results
    plt.figure(figsize=(10, 10))

    plt.subplot(221)
    plt.imshow(img)
    plt.title('Image')

    plt.subplot(222)
    plt.imshow(mask, cmap='gray')
    plt.title('Mask')


    plt.subplot(223)
    plot_histogram(histograms)
    plt.title('Histogram')

    plt.show()

输出：

浣熊的最终测试：

代码：

raccoon = face()
polygon = [(200, 700), (150, 600), (300, 500), (300, 400), (400, 500)]
mask = prepare_mask(polygon, raccoon)
histograms = compute_histogram(mask, raccoon)

plt.figure(figsize=(10, 10))

plt.subplot(221)
plt.imshow(raccoon)
plt.title('Image')

plt.subplot(222)
plt.imshow(mask, cmap='gray')
plt.title('Mask')


plt.subplot(223)
plot_histogram(histograms)
plt.title('Histogram')

plt.show()

输出：

Answer 2

让我们定义(x,y)5个点的坐标(p0, p1, p2, p3, p4)作为区域的角点。在将点连接为 np 数组后，我们可以使用 opencv fillPoly 函数制作一个掩码。可以使用此掩码过滤图像的像素值。我使用 matplotlib 的直方图。 Opencv和numpy也有直方图函数。

import cv2
import numpy as np
from matplotlib import pyplot as plt

img_x = img.copy()
pts = np.concatenate((p0, p1, p2, p3, p4)).reshape((-1, 1, 2))
cv2.fillPoly(img_x , [pts], (255, 255, 255))
n, bins, patches = plt.hist(img[img_x == (255, 255, 255)], 256, [0, 256])

图像区域的直方图

Histogram of a region of an image

python

opencv

numpy

roi

image-processing