sonukiller
commented
1 year ago Hi, After you use model.predict(img), you will get logits. You have to use argmax and then you will get the label. Now you have the labels, you can directly plot them plt.imshow(labels)
In the page itself, the author has given following code, use it: import numpy as np from PIL import Image from matplotlib import pyplot as plt
from model import Deeplabv3
Generates labels using most basic setup. Supports various image sizes. Returns image labels in same format
as original image. Normalization matches MobileNetV2
trained_image_width=512 mean_subtraction_value=127.5 image = np.array(Image.open('imgs/image1.jpg'))
resize to max dimension of images from training dataset
w, h, _ = image.shape ratio = float(trained_image_width) / np.max([w, h]) resized_image = np.array(Image.fromarray(image.astype('uint8')).resize((int(ratio h), int(ratio w))))
apply normalization for trained dataset images
resized_image = (resized_image / mean_subtraction_value) - 1.
pad array to square image to match training images
pad_x = int(trained_image_width - resized_image.shape[0]) pad_y = int(trained_image_width - resized_image.shape[1]) resized_image = np.pad(resized_image, ((0, pad_x), (0, pad_y), (0, 0)), mode='constant')
make prediction
deeplab_model = Deeplabv3() res = deeplab_model.predict(np.expand_dims(resized_image, 0)) labels = np.argmax(res.squeeze(), -1)
remove padding and resize back to original image
if pad_x > 0: labels = labels[:-pad_x] if pad_y > 0: labels = labels[:, :-pad_y] labels = np.array(Image.fromarray(labels.astype('uint8')).resize((h, w)))
plt.imshow(labels)
After using predict(), I get labels now, but how can I get the labeled images? Sorry, I'm new in this, thanks!