Overlay rotated Images on a plot with Matplotlib

Question

Currently, I have plotted using rectangular patches to display a sequence of positions.

EDIT: the code used to create it (built from the RLPy library) -

def visualize_trajectory(self, trajectory=[[0,0,0,0], [0.1,0.1,0,0]]): domain_fig = plt.figure() for i, s in enumerate(trajectory): x, y, speed, heading = s[:4] car_xmin = x - self.REAR_WHEEL_RELATIVE_LOC car_ymin = y - self.CAR_WIDTH / 2. car_fig = matplotlib.patches.Rectangle( [car_xmin, car_ymin], self.CAR_LENGTH, self.CAR_WIDTH, alpha=(0.8 * i) / len(trajectory) ) rotation = Affine2D().rotate_deg_around( x, y, heading * 180 / np.pi) + plt.gca().transData car_fig.set_transform(rotation) plt.gca().add_patch(car_fig) 

Is there any way to apply each of these patches to images? Ideally, instead of each rectangle in each position there will be an image of a car.

I played with AnnotationBbox and TransformedBbox , but both of them seem inflexible when working with rotations.

Answer 1

Take a look at demo_affine_image from matplotlib gallery . It shows how to rotate the image.

 import numpy as np import matplotlib.pyplot as plt import matplotlib.transforms as mtransforms import matplotlib.cbook as cbook def get_image(): fn = cbook.get_sample_data("necked_tensile_specimen.png") arr = plt.imread(fn) # make background transparent # you won't have to do this if your car image already has a transparent background mask = (arr == (1,1,1,1)).all(axis=-1) arr[mask] = 0 return arr def imshow_affine(ax, z, *args, **kwargs): im = ax.imshow(z, *args, **kwargs) x1, x2, y1, y2 = im.get_extent() im._image_skew_coordinate = (x2, y1) return im N = 7 x = np.linspace(0, 1, N) y = x**1.1 heading = np.linspace(10, 90, N) trajectory = list(zip(x, y, heading)) width, height = 0.3, 0.3 car = get_image() fig, ax = plt.subplots() for i, t in enumerate(trajectory, start=1): xi, yi, deg = t im = imshow_affine(ax, car, interpolation='none', extent=[0, width, 0, height], clip_on=True, alpha=0.8*i/len(trajectory)) center_x, center_y = width//2, height//2 im_trans = (mtransforms.Affine2D() .rotate_deg_around(center_x, center_y, deg) .translate(xi, yi) + ax.transData) im.set_transform(im_trans) ax.set_xlim(-0.5, 1.5) ax.set_ylim(-0.5, 1.7) plt.show()