绘制2D numpy的数组中的矩形 [英] Drawing a rectangle inside a 2D numpy array
问题描述
我有从传感器的每个像素含有的各个数据的2D numpy的阵列。的图像与从照相机活饲料中显示的GUI。我希望能够绘制在图像的矩形,以便区分在屏幕的一个区域。它似乎pretty简单绘制一个矩形平行于图像的一边,但我最终想要能够旋转矩形。我如何知道哪些像素时,旋转的矩形覆盖?
I have a 2D numpy array containing the individual data from each pixel of a sensor. The image is displayed in a GUI with a live feed from the camera. I want to be able to draw a rectangle over the image in order to distinguish an area of the screen. It seems pretty simple to draw a rectangle which is parallel to the side of the image but I eventually want to be able to rotate the rectangle. How will I know which pixels the rectangle covers when it is rotated?
推荐答案
您可以使用Python图像库,如果你不介意的依赖。给定一个2D numpy的阵列数据
,和多边形坐标的数组聚
(带形状(N,2)),这将绘制在数组中填充值为0的多边形:
You can use the Python Imaging Library, if you don't mind the dependency. Given a 2D numpy array data
, and an array poly
of polygon coordinates (with shape (n, 2)), this will draw a polygon filled with the value 0 in the array:
img = Image.fromarray(data)
draw = ImageDraw.Draw(img)
draw.polygon([tuple(p) for p in poly], fill=0)
new_data = np.asarray(img)
下面的自包含演示:
import numpy as np
import matplotlib.pyplot as plt
# Python Imaging Library imports
import Image
import ImageDraw
def get_rect(x, y, width, height, angle):
rect = np.array([(0, 0), (width, 0), (width, height), (0, height), (0, 0)])
theta = (np.pi / 180.0) * angle
R = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
offset = np.array([x, y])
transformed_rect = np.dot(rect, R) + offset
return transformed_rect
def get_data():
"""Make an array for the demonstration."""
X, Y = np.meshgrid(np.linspace(0, np.pi, 512), np.linspace(0, 2, 512))
z = (np.sin(X) + np.cos(Y)) ** 2 + 0.25
data = (255 * (z / z.max())).astype(int)
return data
if __name__ == "__main__":
data = get_data()
# Convert the numpy array to an Image object.
img = Image.fromarray(data)
# Draw a rotated rectangle on the image.
draw = ImageDraw.Draw(img)
rect = get_rect(x=120, y=80, width=100, height=40, angle=30.0)
draw.polygon([tuple(p) for p in rect], fill=0)
# Convert the Image data to a numpy array.
new_data = np.asarray(img)
# Display the result using matplotlib. (`img.show()` could also be used.)
plt.imshow(new_data, cmap=plt.cm.gray)
plt.show()
本脚本生成此图:
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