Table Of Contents
Table Of Contents

01. Predict depth from a single image with pre-trained Monodepth2 models

This is a quick demo of using GluonCV Monodepth2 model for KITTI on real-world images. Please follow the installation guide to install MXNet and GluonCV if not yet.

import numpy as np

import mxnet as mx
from import transforms
import gluoncv
# using cpu
ctx = mx.cpu(0)

Prepare the image

Let’s first download the example image,

url = ''
filename = 'test_img.png', filename, True)

Then we load the image and visualize it,

import PIL.Image as pil
img ='RGB')

from matplotlib import pyplot as plt

We resize the image make it has the same input size with pretrained model, and transfer the image to NDArray,

original_width, original_height = img.size
feed_height = 192
feed_width = 640

img = img.resize((feed_width, feed_height), pil.LANCZOS)
img = transforms.ToTensor()(mx.nd.array(img)).expand_dims(0).as_in_context(context=ctx)

Load the pre-trained model and make prediction

Next, we get a pre-trained model from our model zoo,

model = gluoncv.model_zoo.get_model('monodepth2_resnet18_kitti_stereo_640x192',
                                    pretrained_base=False, ctx=ctx, pretrained=True)

We directly make disparity map predictions on the image, and resize it to input size

outputs = model.predict(img)
disp = outputs[("disp", 0)]
disp_resized = mx.nd.contrib.BilinearResize2D(disp, height=original_height, width=original_width)

In the end, we add normalized color map for visualizing the predicted disparity map,

import matplotlib as mpl
import as cm
disp_resized_np = disp_resized.squeeze().as_in_context(mx.cpu()).asnumpy()
vmax = np.percentile(disp_resized_np, 95)
normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax)
mapper = cm.ScalarMappable(norm=normalizer, cmap='magma')
colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8)
im = pil.fromarray(colormapped_im)'test_output.png')

import matplotlib.image as mpimg
disp_map = mpimg.imread('test_output.png')

Total running time of the script: ( 0 minutes 0.000 seconds)

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