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edges.py
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edges.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import skimage as sk
from skimage.io import imread
from skimage import filters
from skimage import transform
import plotting
from pathlib import Path
def load_image(fname, width=900, plot=[]):
if fname:
img0 = imread(fname)
else:
img0 = sk.data.coffee() # coffee (example image)
# ensure image is rgb (for consistency)
if len(img0.shape)<3:
img0 = sk.color.gray2rgb(img0)
# resize to same image width => tile size has always similar effect
if width is not None:
factor = width/img0.shape[1]
img0 = transform.resize(img0, (int(img0.shape[0]*factor), int(img0.shape[1]*factor)), anti_aliasing=True)
img0 = (img0*255).astype(int)
if 'original' in plot: plotting.plot_image(img0)
print (f'Size of input image: {img0.shape[0]}px * {img0.shape[1]}px')
return img0
def edges_diblasi(img, gauss=5, details=1, plot=[]):
# RGB to gray ("Luminance channel" in Di Blasi)
img_gray = sk.color.rgb2gray(img)
# equalize histogram
img_eq = sk.exposure.equalize_hist(img_gray)
# soften image
img_gauss = filters.gaussian(img_eq, sigma=16, truncate=gauss/16)
# segment bright areas to blobs
variance = img_gauss.std()**2 # evtl. direkt die std verwenden
img_seg = np.ones((img.shape[0],img.shape[1]))
threshold = variance/4*2*details
img_seg[abs(img_gauss-img_gauss.mean())>threshold] = 0
### 5. Kanten finden
img_edge = filters.laplace(img_seg, ksize=3)
img_edge[img_edge!=0]=1
if 'edges' in plot: plotting.plot_image(img_edge, inverted=True, title='Di Blasi')
return img_edge
def hed_edges(image):
import cv2 as cv
# based on https://github.com/opencv/opencv/blob/master/samples/dnn/edge_detection.py
class CropLayer(object):
def __init__(self, params, blobs):
self.xstart = 0
self.xend = 0
self.ystart = 0
self.yend = 0
# Our layer receives two inputs. We need to crop the first input blob
# to match a shape of the second one (keeping batch size and number of channels)
def getMemoryShapes(self, inputs):
inputShape, targetShape = inputs[0], inputs[1]
batchSize, numChannels = inputShape[0], inputShape[1]
height, width = targetShape[2], targetShape[3]
self.ystart = int((inputShape[2] - targetShape[2]) / 2)
self.xstart = int((inputShape[3] - targetShape[3]) / 2)
self.yend = self.ystart + height
self.xend = self.xstart + width
return [[batchSize, numChannels, height, width]]
def forward(self, inputs):
return [inputs[0][:,:,self.ystart:self.yend,self.xstart:self.xend]]
# Load the pretrained model (source: https://github.com/s9xie/hed)
script_path = Path(__file__).parent.absolute()
hed_path = Path.joinpath(script_path, 'HED')
net = cv.dnn.readNetFromCaffe(str(hed_path / 'deploy.prototxt'),
str(hed_path / 'hed_pretrained_bsds.caffemodel') )
cv.dnn_registerLayer('Crop', CropLayer)
image=cv.resize(image,(image.shape[1],image.shape[0]))
# prepare image as input dataset (mean values from full image dataset)
inp = cv.dnn.blobFromImage(image, scalefactor=1.0, size=(image.shape[1],image.shape[0]), #w,h
mean=(104.00698793, 116.66876762, 122.67891434),
swapRB=False, crop=False)
net.setInput(inp)
out = net.forward()
cv.dnn_unregisterLayer('Crop') # get rid of issues when run in a loop
out = out[0,0]
return out
def edges_hed(img, gauss=None, plot=[]):
if gauss:
img = filters.gaussian(img, sigma=16, truncate=gauss/16, multichannel=True)
img = img/np.amax(img)*255
img = img.astype(np.uint8)
hed_matrix = hed_edges(img)
# gray to binary
hed_seg = np.ones((hed_matrix.shape[0],hed_matrix.shape[1]))
hed_seg[hed_matrix<0.5]=0
# skeletonize to get inner lines
img_edges = sk.morphology.skeletonize(hed_seg).astype(int)
# option to make plot lines thicker:
#from skimage.morphology import square,dilation
#img_edges = dilation(img_edges, square(3))
if 'edges' in plot: plotting.plot_image(img_edges, inverted=True, title='HED')
return img_edges
if __name__ == '__main__':
img = sk.data.coffee()
#img_edges = edges_diblasi(img)
img_edges = edges_hed(img, gauss=0)