+# -*- coding: utf-8 -*-
#!/usr/bin/env python
# Copyright (C) 2010 Monash University
"""
from PIL import Image
-
import sys, numpy, os, random
from scipy import ndimage
#
# What to show in diagnostic output images
-Show_edges = False
+Show_edges = True
Show_stain = False
Show_infection = False
-Show_hand_marks = True
+Show_hand_marks = False
Show_computer_marks = True
-Show_cell_shapes = False
+Show_cell_shapes = True
# =================
# Image scaling
-Resize_width = 680
-Resize_height = 512
+Resize = False
+
+# Tailles pour les images dans '08.10.2015'
+Resize_width = 1296 #680
+Resize_height = 972 #512
+
+# Tailles pour les images dans '22.05.2015' (50%)
+#Resize_width = 1296
+#Resize_height = 972
+
+
+# [GB] Hemato
+# Resize_width = 2864
+# Resize_height = 2885
# ==========================================
# Size of cells, for Hough transform
Hough_min_radius = 5
-Hough_max_radius = 20
+Hough_max_radius = 32 # 20
# Portion of cell used for classification as infected/weird
# (may be abutting an infected cell, so be cautious)
Cell_inside_radius = 0.9
# Do not allow another cell center to be detected within this radius
-Cell_suppression_radius = 1.25
+Cell_suppression_radius = 1.05 # 1.25
# =======================
Max_dark_stain_proportion = 0.728
# Minimum/maximum radius (as determined by Hough transform)
-Min_cell_radius = 9
-Max_cell_radius = 20
+Min_cell_radius = 11 # 9
+Max_cell_radius = 32 # 20
# Proportion of radius that center of mass can be away from Hough determined center
Max_offcenter = 0.5
-
-
-
-
_offset_cache = { }
def offsets(inner_radius, outer_radius):
"""
print 'Processing', filename_in, '->', filename_out
# Resize (mostly done for speed increase)
- i = i.resize((Resize_width,Resize_height), Image.ANTIALIAS)
+ i = i.resize((Resize_width,Resize_height), Image.ANTIALIAS) if Resize else i
# Convert to numpy array
- a = numpy.asarray(i).astype('float64')
+ a = numpy.asarray(i).astype('float64') # [GB] Shape: (512, 680, 3)
height = a.shape[0]
width = a.shape[1]
- a_gray = numpy.sum(a, 2)
+ a_gray = numpy.sum(a, 2) # [GB] Somme de chaque composante RGB (non-normalisé)
# Make a copy of the array to doodle various diagnostic markings on
a_output = a.copy()
# Denoise/unsharp mask
- a_enhanced = gaussian_blur(a, Enhance_a) - Enhance_b*gaussian_blur(a,Enhance_c)
-
+ # [GB] (Concerne les trois channels RGB).
+ a_enhanced = gaussian_blur(a, Enhance_a) - Enhance_b * gaussian_blur(a, Enhance_c)
# Split into foreground and background using k-medians
status('fg/bg')
+ # [GB] Chaque canal de couleur est linéarisé dans un tableau.
a_raveled = numpy.reshape(a_enhanced,(width*height,a_enhanced.shape[2]))
- average = numpy.average(a_raveled, axis=0)
- # Initial guess
- color_bg = average*1.5
- color_fg = average*0.5
+ # [GB] Moyenne de chaque canal.
+ average = numpy.average(a_raveled, axis=0)
+
+ # Initial guess of the medians
+ color_bg = average * 1.5 # [GB] Plus clair
+ color_fg = average * 0.5 # [GB] Plus sombre
+
+ # Fifty times: (0 to 4).
for i in xrange(5):
- d_bg = a_raveled-color_bg[None,:]
+ d_bg = a_raveled - color_bg[None,:]
d_bg *= d_bg
- d_bg = numpy.sum(d_bg,1)
- d_fg = a_raveled-color_fg[None,:]
+ d_bg = numpy.sum(d_bg, 1) # [GB] Somme des carrés des couleurs
+ d_fg = a_raveled - color_fg[None,:]
d_fg *= d_fg
- d_fg = numpy.sum(d_fg,1)
- fg = d_fg*Background_weight < d_bg
- color_fg = numpy.median(a_raveled[fg,:],axis=0)
+ d_fg = numpy.sum(d_fg, 1)
+ fg = d_fg * Background_weight < d_bg
+ color_fg = numpy.median(a_raveled[fg,:],axis=0) # [GB] axis = 0 -> par colonne (ordonnées). Le résultat est alors 3 valeurs, une pour chaque couleur
color_bg = numpy.median(a_raveled[~fg,:],axis=0)
-
- fg = numpy.reshape(fg, (height,width))
+
+ fg = numpy.reshape(fg, (height,width)) # [GB] Image binaire du premier plan.
- edge = ndimage.maximum_filter(fg,size=3) != ndimage.minimum_filter(fg,size=3)
+ edge = ndimage.maximum_filter(fg,size=3) != ndimage.minimum_filter(fg,size=3) # [GB] image binaire des contours du premier plan.
+ # [GB] délinéarisation des tableaux résultant du k-median (niveau de gris).
d_fg = numpy.sqrt(numpy.reshape(d_fg, (height,width)))
d_bg = numpy.sqrt(numpy.reshape(d_bg, (height,width)))
# (should be invariant under changes in brightness)
mag_bg = numpy.average(color_bg)
+ # [GB] Les "dark stain" :
+ # * sont d'une intensité plus élevée que la moyenne du bg multiplié par un facteur (Dark_stain_level)
+ # * les valeurs rouges sont plus petites que la mediane du premier-plan pour les rouges
+ # * les valeurs vertes sont plus petites que la médiane du premier-plan pour les verts
+ # * appartiennent au foreground
dark_stain = (
(d_fg > mag_bg*Dark_stain_level) &
- (a_enhanced[:,:,0] < color_fg[0]) &
- (a_enhanced[:,:,1] < color_fg[1]) &
+ (a_enhanced[:,:,0] < color_fg[0]) & # [GB] Red channel
+ (a_enhanced[:,:,1] < color_fg[1]) & # [GB] Green channel
fg
)
if Show_infection:
a_output[infection,0] += 128
+ #a_output[stain, 1] += 128
+
# Hough transform
status('hough')
candidates = [ ]
for y in xrange(height):
for x in xrange(width):
- if hough[y,x] > thresh:
- candidates.append((hough[y,x],y,x))
- candidates.sort(reverse=True)
+ if hough[y, x] > thresh:
+ candidates.append((hough[y,x], y, x))
+ candidates.sort(reverse = True)
+ hough
+
status('classify')
suppress = numpy.zeros((height,width), 'bool')
claim_strength = numpy.zeros((height,width), 'float64') + 1e30
- cells = [ ] # [(y,x)]
+ cells = [] # [(y,x)]
n = 0
n_infected = 0
- n_late = 0
for _,y,x in candidates:
radius = hough_radius[y,x]
-
+
# Make sure candidate is not near a previously detected cell,
# and does not abut the edge of the image
if not suppress[y,x] and \
y >= radius and x >= radius and y < height-radius and x < width-radius:
- coords = clipped_coordinates(0,radius*Cell_inside_radius, y,x,height,width)
+ coords = clipped_coordinates(0, radius * Cell_inside_radius, y, x, height, width)
#Only include foreground
- this_fg = fg[coords[:,0],coords[:,1]]
+ this_fg = fg[coords[:,0], coords[:,1]] # [GB] Booleans
coords = coords[this_fg]
this_strength = ((coords[:,0]-y)**2 + (coords[:,1]-x)**2)
this_strength
)
- cells.append((y,x, coords,this_strength))
+ cells.append((y, x, coords, this_strength))
-
+
# Suppress detection of cells too close to here
- # (note: we do this irrespective of whether we treated this location as a hit)
-
- coords = clipped_coordinates(0,radius*Cell_suppression_radius, y,x,height,width)
+ # (note: we do this irrespective of whether we treated this location as a hit)
+ coords = clipped_coordinates(0,radius * Cell_suppression_radius, y,x,height,width)
suppress[coords[:,0],coords[:,1]] = True
#for oy, ox in offsets(0, radius*Cell_suppression_radius): #suppression_offsets:
color = (0,0,255)
file_coords.write('%d,%d,Uninfected\n' % (x,y))
+ if Show_cell_shapes:
+ a_output[coords[:,0],coords[:,1]] += [[ random.random()*64+32 for i in (0,1,2) ]]
+
# Mark detected cell on output image
if Show_computer_marks:
- set(a_output,y,x,color)
+ set(a_output, y, x, color)
for i in xrange(1,3):
set(a_output,y-i,x,color)
set(a_output,y+i,x,color)
set(a_output,y,x-i,color)
set(a_output,y,x+i,color)
- if Show_cell_shapes:
- a_output[coords[:,0],coords[:,1]] += [[ random.random()*64+32 for i in (0,1,2) ]]
projects / master-thesis.git / blobdiff