Image segmentation and area measurement - opencv

Question

I have done image segmentation of the image using PyTorch. I am trying to get the pixel count of Boat class to measure the area. As an example in the image I want to get the pixel count to measure the boat. How do I do that? from the pixel count is it possible to measure the are of the boat?
I am confused and trying to find a way. I would appreciate if anybody can guide me for that.
**The coding is as below:
**
from torchvision import models
fcn = models.segmentation.fcn_resnet101(pretrained=True).eval()
from PIL import Image
import matplotlib.pyplot as plt
import torch
img = Image.open('boat.jpg')
plt.imshow(img)
plt.show()
# Apply the transformations needed
#Resize the image to (256 x 256)
#CenterCrop it to (224 x 224)
import torchvision.transforms as T
trf = T.Compose([T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(mean = [0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225])])
inp = trf(img).unsqueeze(0)
out = fcn(inp)['out']
print (out.shape)
#now this 21 channeled output into a 2D image or a 1 channeled image, where each pixel of that image corresponds to a class.
import numpy as np
om = torch.argmax(out.squeeze(), dim=0).detach().cpu().numpy()
print (om.shape)
print (np.unique(om))
# Define the helper function
def decode_segmap(image, nc=21):
label_colors = np.array([(0, 0, 0), # 0=background
# 1=aeroplane, 2=bicycle, 3=bird, 4=boat, 5=bottle
(128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128),
# 6=bus, 7=car, 8=cat, 9=chair, 10=cow
(0, 128, 128), (128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0),
# 11=dining table, 12=dog, 13=horse, 14=motorbike, 15=person
(192, 128, 0), (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128),
# 16=potted plant, 17=sheep, 18=sofa, 19=train, 20=tv/monitor
(0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128)])
r = np.zeros_like(image).astype(np.uint8)
g = np.zeros_like(image).astype(np.uint8)
b = np.zeros_like(image).astype(np.uint8)
for l in range(0, nc):
idx = image == l
r[idx] = label_colors[l, 0]
g[idx] = label_colors[l, 1]
b[idx] = label_colors[l, 2]
rgb = np.stack([r, g, b], axis=2)
return rgb
rgb = decode_segmap(om)
plt.imshow(rgb); plt.show()
I want to find some guidance

Answer 1

You are looking for skimage.measure.regionprops. Once you have the predicted label map (om in your code) you can apply regionprops to it and get the area of each region.

Answer 2

According to your code snippet, the output om is a tensor of category indices (0 - background, 1 - aeroplane, 2 - bicycle,....).
In order to get the area of a specific category, you just need to compare the output map with the corresponding index, then sum up the results.
For example, with the category boat with the index 4:
BOAT_INDEX = 4
area = torch.sum(om == BOAT_INDEX).item()