Для вычисления гистограммы используйте функцию np.histogram.Возвращает гистограмму и бины.Таким образом, вы можете сохранить результаты и работать с ними:
hist, bins = np.histogram(arr, bins=bins, range=range)
Если вы хотите построить график результатов, вы можете использовать plt.bar после применения np.histogram, просто передавая bins и hist:
plt.bar(bins, hist)
Другая опция использует matplotlib plt.hist, она вычисляет гистограмму и строит ее из необработанных данных:
plt.hist(arr, bins=bins)
Вот полный пример гистограммы области изображениялюбой формы:
Код:
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()
Выход:
Финальный тест на еноте:
Код:
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()
Выход:
