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predict.py
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predict.py
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import argparse
import logging
import os
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from utils.data_loading import BasicDataset
from unet import UNet
from utils.utils import plot_img_and_mask
def predict_img(net,
full_img,
device,
scale_factor=1,
out_threshold=0.5):
net.eval()
img = torch.from_numpy(BasicDataset.preprocess(None, full_img, scale_factor, is_mask=False))
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
with torch.no_grad():
output = net(img).cpu()
output = F.interpolate(output, (full_img.size[1], full_img.size[0]), mode='bilinear')
if net.n_classes > 1:
mask = output.argmax(dim=1)
else:
mask = torch.sigmoid(output) > out_threshold
return mask[0].long().squeeze().numpy()
def get_args():
parser = argparse.ArgumentParser(description='Predict masks from input images')
parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE',
help='Specify the file in which the model is stored')
parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True)
parser.add_argument('--output', '-o', metavar='OUTPUT', nargs='+', help='Filenames of output images')
parser.add_argument('--viz', '-v', action='store_true',
help='Visualize the images as they are processed')
parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks')
parser.add_argument('--mask-threshold', '-t', type=float, default=0.5,
help='Minimum probability value to consider a mask pixel white')
parser.add_argument('--scale', '-s', type=float, default=0.5,
help='Scale factor for the input images')
parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
return parser.parse_args()
def get_output_filenames(args):
def _generate_name(fn):
return f'{os.path.splitext(fn)[0]}_OUT.png'
return args.output or list(map(_generate_name, args.input))
def mask_to_image(mask: np.ndarray, mask_values):
if isinstance(mask_values[0], list):
out = np.zeros((mask.shape[-2], mask.shape[-1], len(mask_values[0])), dtype=np.uint8)
elif mask_values == [0, 1]:
out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=bool)
else:
out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=np.uint8)
if mask.ndim == 3:
mask = np.argmax(mask, axis=0)
for i, v in enumerate(mask_values):
out[mask == i] = v
return Image.fromarray(out)
def find_largest_polytope(v):
v1 = 0
v2 = 1
v3 = 2
v4 = 3
largest_area = -1
for i in range(v.shape[0]-3):
for j in range(i+1,v.shape[0]-2):
for k in range(j+1, v.shape[0]-1):
for l in range(k+1, v.shape[0]):
area = 1/2*((v[i,0]*v[j,1]+v[j,0]*v[k,1]+v[k,0]*v[l,1]+v[l,0]*v[i,1])-\
(v[j,0]*v[i,1]+v[k,0]*v[j,1]+v[l,0]*v[k,1]+v[i,0]*v[l,1]))
if area > largest_area:
largest_area = area
v1 = i
v2 = j
v3 = k
v4 = l
return hull_vertices[[v1, v2, v3, v4],:]
if __name__ == '__main__':
args = get_args()
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
in_files = args.input
out_files = get_output_filenames(args)
net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logging.info(f'Loading model {args.model}')
logging.info(f'Using device {device}')
net.to(device=device)
state_dict = torch.load(args.model, map_location=device)
mask_values = state_dict.pop('mask_values', [0, 1])
net.load_state_dict(state_dict)
logging.info('Model loaded!')
for i, filename in enumerate(in_files):
logging.info(f'Predicting image {filename} ...')
img = Image.open(filename)
mask = predict_img(net=net,
full_img=img,
scale_factor=args.scale,
out_threshold=args.mask_threshold,
device=device)
# print(np.max(mask))
# print(np.min(mask))
pixels = np.where(mask>0)
# print(pixels)
pixels_array = np.vstack((pixels[1],pixels[0])).T
# print(pixels_array.shape)
import matplotlib.pyplot as plt
plt.figure()
plt.imshow(mask)
plt.figure()
plt.imshow(img)
# plt.show()
import scipy.spatial
hull = scipy.spatial.ConvexHull(pixels_array)
# plt.plot(pixels_array[hull.vertices,0],pixels_array[hull.vertices,1],'r*')
hull_vertices = pixels_array[hull.vertices,:]
center_point = np.mean(hull_vertices,axis=0)
# plt.plot(center_point[0], center_point[1], 'b*')
pts = find_largest_polytope(hull_vertices)
upleft = np.where((pts[:,0]<center_point[0]) & (pts[:,1]<center_point[1]))
upleft_vertices = pts[upleft[0],:]
plt.plot(upleft_vertices[:,0],upleft_vertices[:,1],'r*')
bottomleft = np.where((pts[:,0]<center_point[0]) & (pts[:,1]>center_point[1]))
bottomleft_vertices = pts[bottomleft[0],:]
plt.plot(bottomleft_vertices[:,0],bottomleft_vertices[:,1],'g*')
bottomright = np.where((pts[:,0]>center_point[0]) & (pts[:,1]>center_point[1]))
bottomright_vertices = pts[bottomright[0],:]
plt.plot(bottomright_vertices[:,0],bottomright_vertices[:,1],'b*')
upright = np.where((pts[:,0]>center_point[0]) & (pts[:,1]<center_point[1]))
upright_vertices = pts[upright[0],:]
plt.plot(upright_vertices[:,0],upright_vertices[:,1],'y*')
plt.show()