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net_canny.py
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net_canny.py
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import torch
import torch.nn as nn
import numpy as np
from scipy.signal import gaussian
class Net(nn.Module):
def __init__(self, threshold=10.0, device='cpu'):
super(Net, self).__init__()
self.threshold = threshold
self.device = device
filter_size = 5
generated_filters = gaussian(filter_size,std=1.0).reshape([1,filter_size])
self.gaussian_filter_horizontal = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(1,filter_size), padding=(0,filter_size//2))
self.gaussian_filter_horizontal.weight.data.copy_(torch.from_numpy(generated_filters))
self.gaussian_filter_horizontal.bias.data.copy_(torch.from_numpy(np.array([0.0])))
self.gaussian_filter_vertical = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(filter_size,1), padding=(filter_size//2,0))
self.gaussian_filter_vertical.weight.data.copy_(torch.from_numpy(generated_filters.T))
self.gaussian_filter_vertical.bias.data.copy_(torch.from_numpy(np.array([0.0])))
sobel_filter = np.array([[1, 0, -1],
[2, 0, -2],
[1, 0, -1]])
self.sobel_filter_horizontal = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=sobel_filter.shape, padding=sobel_filter.shape[0]//2)
self.sobel_filter_horizontal.weight.data.copy_(torch.from_numpy(sobel_filter))
self.sobel_filter_horizontal.bias.data.copy_(torch.from_numpy(np.array([0.0])))
self.sobel_filter_vertical = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=sobel_filter.shape, padding=sobel_filter.shape[0]//2)
self.sobel_filter_vertical.weight.data.copy_(torch.from_numpy(sobel_filter.T))
self.sobel_filter_vertical.bias.data.copy_(torch.from_numpy(np.array([0.0])))
# filters were flipped manually
filter_0 = np.array([ [ 0, 0, 0],
[ 0, 1, -1],
[ 0, 0, 0]])
filter_45 = np.array([ [0, 0, 0],
[ 0, 1, 0],
[ 0, 0, -1]])
filter_90 = np.array([ [ 0, 0, 0],
[ 0, 1, 0],
[ 0,-1, 0]])
filter_135 = np.array([ [ 0, 0, 0],
[ 0, 1, 0],
[-1, 0, 0]])
filter_180 = np.array([ [ 0, 0, 0],
[-1, 1, 0],
[ 0, 0, 0]])
filter_225 = np.array([ [-1, 0, 0],
[ 0, 1, 0],
[ 0, 0, 0]])
filter_270 = np.array([ [ 0,-1, 0],
[ 0, 1, 0],
[ 0, 0, 0]])
filter_315 = np.array([ [ 0, 0, -1],
[ 0, 1, 0],
[ 0, 0, 0]])
all_filters = np.stack([filter_0, filter_45, filter_90, filter_135, filter_180, filter_225, filter_270, filter_315])
self.directional_filter = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=filter_0.shape, padding=filter_0.shape[-1] // 2)
self.directional_filter.weight.data.copy_(torch.from_numpy(all_filters[:, None, ...]))
self.directional_filter.bias.data.copy_(torch.from_numpy(np.zeros(shape=(all_filters.shape[0],))))
def forward(self, img):
img_r = img[:,0:1]
img_g = img[:,1:2]
img_b = img[:,2:3]
blur_horizontal = self.gaussian_filter_horizontal(img_r)
blurred_img_r = self.gaussian_filter_vertical(blur_horizontal)
blur_horizontal = self.gaussian_filter_horizontal(img_g)
blurred_img_g = self.gaussian_filter_vertical(blur_horizontal)
blur_horizontal = self.gaussian_filter_horizontal(img_b)
blurred_img_b = self.gaussian_filter_vertical(blur_horizontal)
blurred_img = torch.stack([blurred_img_r,blurred_img_g,blurred_img_b],dim=1)
blurred_img = torch.stack([torch.squeeze(blurred_img)])
grad_x_r = self.sobel_filter_horizontal(blurred_img_r)
grad_y_r = self.sobel_filter_vertical(blurred_img_r)
grad_x_g = self.sobel_filter_horizontal(blurred_img_g)
grad_y_g = self.sobel_filter_vertical(blurred_img_g)
grad_x_b = self.sobel_filter_horizontal(blurred_img_b)
grad_y_b = self.sobel_filter_vertical(blurred_img_b)
# COMPUTE THICK EDGES
# grad_mag = torch.sqrt(grad_x_r**2 + grad_y_r**2).clone() + torch.sqrt(grad_x_g**2 + grad_y_g**2).clone() + torch.sqrt(grad_x_b**2 + grad_y_b**2).clone()
grad_mag = grad_x_r**2 + grad_y_r**2 + grad_x_g**2 + grad_y_g**2 + grad_x_b**2 + grad_y_b**2
#grad_mag +=
#grad_mag +=
grad_orientation = (torch.atan2(grad_y_r+grad_y_g+grad_y_b, grad_x_r+grad_x_g+grad_x_b) * (180.0/3.14159))
grad_orientation += 180.0
grad_orientation = torch.round( grad_orientation / 45.0 ) * 45.0
# THIN EDGES (NON-MAX SUPPRESSION)
all_filtered = self.directional_filter(grad_mag)
inidices_positive = (grad_orientation / 45) % 8
inidices_negative = ((grad_orientation / 45) + 4) % 8
height = inidices_positive.size()[2]
width = inidices_positive.size()[3]
pixel_count = height * width
pixel_range = torch.FloatTensor([range(pixel_count)]).to(self.device)
indices = (inidices_positive.view(-1).data * pixel_count + pixel_range).squeeze()
channel_select_filtered_positive = all_filtered.view(-1)[indices.long()].view(1,height,width)
indices = (inidices_negative.view(-1).data * pixel_count + pixel_range).squeeze()
channel_select_filtered_negative = all_filtered.view(-1)[indices.long()].view(1,height,width)
channel_select_filtered = torch.stack([channel_select_filtered_positive,channel_select_filtered_negative])
is_max = channel_select_filtered.min(dim=0)[0] > 0.0
is_max = torch.unsqueeze(is_max, dim=0)
thin_edges = grad_mag.clone()
thin_edges[is_max==0] = 0.0
# THRESHOLD
thresholded = thin_edges.clone()
thresholded[thin_edges<self.threshold] = 0.0
early_threshold = grad_mag.clone()
early_threshold[grad_mag<self.threshold] = 0.0
assert grad_mag.size() == grad_orientation.size() == thin_edges.size() == thresholded.size() == early_threshold.size()
return blurred_img, grad_mag, grad_orientation, thin_edges, thresholded, early_threshold
if __name__ == '__main__':
Net()