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Commit 7c3986ee authored by Geazy Menezes's avatar Geazy Menezes
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Merge branch 'DesempenhoNovosProcessadores_CorrecaoMensagens_HiddenWarnings'

parents 25e00437 6232bfa2
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Showing with 132 additions and 73 deletions
.gitignore
View file @ 7c3986ee
*.pyc
data/*
models_checkpoints/*
venv/*
!data/demo.jpg
!data/pynovisao.png
!data/demo/.gitignore
......
src/extraction/feature_extraction.py
View file @ 7c3986ee
......@@ -11,8 +11,8 @@
import io
import itertools
import os
import threading
import multiprocessing
from multiprocessing import Process, Manager
from interface.interface import InterfaceException as IException
from util.file_utils import File
......@@ -23,7 +23,9 @@ from extractor import Extractor
from tqdm import tqdm
import sys
if not sys.warnoptions:
import warnings
warnings.simplefilter("ignore")
class FeatureExtractor(object):
"""Handle the feature extraction."""
......@@ -40,13 +42,11 @@ class FeatureExtractor(object):
self.tkParent=tkParent
def extract_all(self, dataset, output_file=None, dirs=None, overwrite=True):
self.labels = []
self.types = []
self.data = []
self.data = Manager().list() #is a necessary because have a problem with use Process and normaly declaration
self.threads = []
self.labels = []
self.types = []
self.labels = Manager().list()
self.types = Manager().list()
"""Runs the feature extraction algorithms on all images of dataset.
Parameters
......@@ -101,6 +101,7 @@ class FeatureExtractor(object):
with tqdm(total=len(self.threads)) as pbar:
for t in self.threads:
t.start()
pbar.update(1)
pbar.close()
self.print_console("Waiting for workers to finish extracting attributes from images!")
......@@ -108,7 +109,6 @@ class FeatureExtractor(object):
for t in self.threads:
t.join()
ppbar.update(1)
ppbar.close()
self.print_console("The process was completed with "+str(len(self.threads))+" images!")
if len(self.data) == 0:
......@@ -116,7 +116,7 @@ class FeatureExtractor(object):
# Save the output file in ARFF format
# self._save_output(File.get_filename(dataset), classes, self.labels, self.types, self.data, output_file)
self._save_output(File.get_filename(dataset), classes, self.labels, self.types, self.data, output_file)
self._save_output(File.get_filename(dataset), classes, self.labels[0], self.types[0], self.data, output_file)
end_time = TimeUtils.get_time()
return output_file, (end_time - start_time)
......@@ -130,9 +130,8 @@ class FeatureExtractor(object):
for item in items :
if item.startswith('.'):
continue
th = threading.Thread(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
#th = threading.Thread(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
th = multiprocessing.Process(target=self.sub_job_extractor,args=(item, dataset, cl, classes))
self.threads.append(th)
......@@ -149,14 +148,17 @@ class FeatureExtractor(object):
if len(self.data) > 0:
values = list(
itertools.chain.from_iterable(zip(*([extractor().run(image) for extractor in self.extractors]))[2]))
self.data.append(values + [cl if cl in classes else classes[0]])
else:
self.labels, self.types, values = [list(itertools.chain.from_iterable(ret))
labs, tys, values = [list(itertools.chain.from_iterable(ret))
for ret in
zip(*(extractor().run(image) for extractor in self.extractors))]
self.labels.append(labs)
self.types.append(tys)
self.data.append(values + [cl if cl in classes else classes[0]])
def extract_one_file(self, dataset, image_path, output_file=None):
"""Runs the feature extraction algorithms on specific image.
......
src/extraction/hog.py
View file @ 7c3986ee
......@@ -8,6 +8,11 @@
Name: hog.py
Author: Alessandro dos Santos Ferreira ( santosferreira.alessandro@gmail.com )
Change parameter Visualise for Visualize because is deprecaded
Date:02/01/2019
Author: Diego Andre Sant Ana
"""
from skimage import feature
......@@ -36,13 +41,13 @@ class HOG(Extractor):
features : tuple
Returns a tuple containing a list of labels, type and values for each feature extracted.
"""
image_grayscale = ImageUtils.image_grayscale(image, bgr = True)
image_grayscale = ImageUtils.image_grayscale(image, bgr=True)
image_128x128 = ImageUtils.image_resize(image_grayscale, 128, 128)
values, _ = feature.hog(image_128x128, orientations=8, pixels_per_cell=(32, 32),
cells_per_block=(1, 1), visualise=True)
labels = [m+n for m,n in zip(['hog_'] * len(values),map(str,range(0,len(values))))]
cells_per_block=(1, 1), visualise=True)
labels = [m + n for m, n in zip(['hog_'] * len(values), map(str, range(0, len(values))))]
types = [Extractor.NUMERIC] * len(labels)
return labels, types, list(values)
src/extraction/image_moments.py
View file @ 7c3986ee
......@@ -16,7 +16,7 @@ import cv2
from util.utils import ImageUtils
from skimage.measure import regionprops, moments, moments_central
from skimage.morphology import label
import numpy as np
from extractor import Extractor
class RawCentralMoments(Extractor):
......@@ -53,8 +53,8 @@ class RawCentralMoments(Extractor):
row = m[0, 1] / m[0, 0]
col = m[1, 0] / m[0, 0]
mu = measure.moments_central(image_binary, row, col)
mu = measure.moments_central(image_binary, center=(row, col), order=3)
values_mu = [mu[p, q] for (p, q) in self._moments_order]
labels_mu = [M+str(p)+str(q) for M,(p,q) in zip(['Mu_'] * len(self._moments_order), self._moments_order)]
......@@ -104,8 +104,9 @@ class HuMoments(Extractor):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
values_hu= cv2.HuMoments(cv2.moments(image)).flatten()
values_hu = list(values_hu)
values_hu= np.nan_to_num(values_hu)
labels_hu = [m+n for m,n in zip(['Hu_'] * len(values_hu),map(str,range(0,len(values_hu))))]
labels = labels_hu
......
src/pynovisao.py
View file @ 7c3986ee
......@@ -5,7 +5,7 @@
Name: pynovisao.py
Author: Alessandro dos Santos Ferreira ( santosferreira.alessandro@gmail.com )
"""
import gc
from collections import OrderedDict
import numpy as np
import os
......@@ -30,8 +30,10 @@ from util.file_utils import File
from util.utils import TimeUtils
from util.utils import MetricUtils
from util.x11_colors import X11Colors
import multiprocessing
from multiprocessing import Process, Manager
import threading
from tqdm import tqdm
class Act(object):
"""Store all actions of Pynovisao."""
......@@ -199,7 +201,7 @@ class Act(object):
If there's no image opened.
"""
if self._const_image is None:
raise IException("Image not found")
raise IException("Image not found! Open an image to test, select in the menu the option File>Open Image!")
if self.tk.close_image():
self.tk.write_log("Closing image...")
......@@ -386,7 +388,7 @@ class Act(object):
If there's no image opened.
"""
if self._const_image is None:
raise IException("Image not found")
raise IException("Image not found! Open an image to test, select in the menu the option File>Open Image!")
self.tk.write_log("Running %s...", self.segmenter.get_name())
......@@ -421,7 +423,7 @@ class Act(object):
if new_config[extractor].value == True ]
if len(self.extractors) == 0:
raise IException("Please select at least one extractor")
raise IException("Please select an extractor from the menu under Features Extraction> Select extractors! ")
self.tk.append_log("\nConfig updated:\n%s",
'\n'.join(["%s: %s" % (new_config[extractor].label, "on" if new_config[extractor].value==True else "off")
......@@ -459,7 +461,7 @@ class Act(object):
The user must install the required dependencies to classifiers.
"""
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
title = "Choosing a classifier"
self.tk.write_log(title)
......@@ -488,7 +490,7 @@ class Act(object):
The user must install the required dependencies to classifiers.
"""
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
title = "Configuring %s" % self.classifier.get_name()
self.tk.write_log(title)
......@@ -509,7 +511,7 @@ class Act(object):
def run_classifier(self):
"""Run the classifier on the current image.
As result, paint the image with color corresponding to predicted class of all segment.
Raises
------
IException 'You must install python-weka-wrapper'
......@@ -518,48 +520,53 @@ class Act(object):
If there's no image opened.
"""
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
if self._const_image is None:
raise IException("Image not found")
raise IException("Image not found! Open an image to test, select in the menu the option File>Open Image!")
self.tk.write_log("Running %s...", self.classifier.get_name())
self.tk.append_log("\n%s", str(self.classifier.get_summary_config()))
#self.classifier.set
start_time = TimeUtils.get_time()
# Perform a segmentation, if needed.
list_segments = self.segmenter.get_list_segments()
if len(list_segments) == 0:
self.tk.append_log("Running %s... (%0.3f seconds)", self.segmenter.get_name(), (TimeUtils.get_time() - start_time))
self._image, _ = self.segmenter.run(self._const_image)
self.tk.refresh_image(self._image)
self.tk.refresh_image(self._image)
list_segments = self.segmenter.get_list_segments()
self._gt_segments = [None]*(max(list_segments)+1)
# New and optimized classification
tmp = ".tmp"
File.remove_dir(File.make_path(self.dataset, tmp))
self.tk.append_log("Generating test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
len_segments = {}
for idx_segment in list_segments:
segment, size_segment, idx_segment = self.segmenter.get_segment(self, idx_segment=idx_segment)[:-1]
# Problem here! Dataset removed.
filepath = File.save_only_class_image(segment, self.dataset, tmp, self._image_name, idx_segment)
len_segments[idx_segment] = size_segment
print("Wait to complete processes all images!")
with tqdm(total=len(list_segments)) as pppbar:
for idx_segment in list_segments:
segment, size_segment, idx_segment = self.segmenter.get_segment(self, idx_segment=idx_segment)[:-1]
# Problem here! Dataset removed.
filepath = File.save_only_class_image(segment, self.dataset, tmp, self._image_name, idx_segment)
len_segments[idx_segment] = size_segment
pppbar.update(1)
pppbar.close()
gc.collect()
# Perform the feature extraction of all segments in image ( not applied to ConvNets ).
if self.classifier.must_extract_features():
self.tk.append_log("Running extractors on test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
fextractor = FeatureExtractor(self.extractors)
self.tk.append_log("Running extractors on test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
fextractor = FeatureExtractor(self.extractors)
output_file, _ = fextractor.extract_all(self.dataset, "test", dirs=[tmp])
self.tk.append_log("Running classifier on test data... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
# Get the label corresponding to predict class for each segment of image.
......@@ -569,17 +576,17 @@ class Act(object):
# Result is the class for each superpixel
if type(labels) is types.ListType:
self.tk.append_log("Painting segments... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
# If ground truth mode, show alternative results
if self._ground_truth == True:
return self._show_ground_truth(list_segments, len_segments, labels, start_time)
# Create a popup with results of classification.
popup_info = "%s\n" % str(self.classifier.get_summary_config())
len_total = sum([len_segments[idx] for idx in len_segments])
popup_info += "%-16s%-16s%0.2f%%\n" % ("Total", str(len_total), (len_total*100.0)/len_total)
# Paint the image.
self._mask_image = np.zeros(self._const_image.shape[:-1], dtype="uint8")
height, width, channels = self._image.shape
......@@ -591,7 +598,7 @@ class Act(object):
for idx in idx_segment:
self._mask_image[self.segmenter._segments == idx] = c
self.class_color[self.segmenter._segments == idx] = X11Colors.get_color(cl["color"].value)
len_classes = sum([len_segments[idx] for idx in idx_segment])
popup_info += "%-16s%-16s%0.2f%%\n" % (cl["name"].value, str(len_classes), (len_classes*100.0)/len_total)
......@@ -610,18 +617,18 @@ class Act(object):
self._image = cv2.addWeighted(self._const_image, 0.7, self.class_color, 0.3, 0)
self.tk.refresh_image(self._image)
end_time = TimeUtils.get_time()
self.tk.append_log("\nClassification finished")
self.tk.append_log("Time elapsed: %0.3f seconds", (end_time - start_time))
def run_training(self):
start_time = TimeUtils.get_time()
# Training do not need an image opened (consider removing these two lines)
# if self._const_image is None:
# raise IException("Image not found")
# Training do not need an image opened (consider removing these two lines)
# if self._const_image is None:
# raise IException("Image not found")
if self.classifier.must_train():
......@@ -701,7 +708,7 @@ class Act(object):
The user must install the required dependencies to classifiers.
"""
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
if self.classifier.must_train():
self.tk.write_log("Creating training data...")
......@@ -726,12 +733,12 @@ class Act(object):
The user must install the required dependencies to classifiers.
"""
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
if self.tk.ask_ok_cancel("Experimenter All", "This may take several minutes to complete. Are you sure?"):
if self.classifier.must_train():
self.tk.write_log("Creating training data...")
fextractor = FeatureExtractor(self.extractors)
output_file, run_time = fextractor.extract_all(self.dataset, "training", overwrite = False)
self.classifier.train(self.dataset, "training")
......@@ -855,7 +862,7 @@ class Act(object):
def run_classifier_folder(self, foldername=None):
if self.classifier is None:
raise IException("Classifier not found!")
raise IException("Classifier not found! Select from the menu the option Training>Choose Classifier!")
if foldername is None:
foldername = self.tk.utils.ask_directory()
......@@ -951,6 +958,7 @@ class Act(object):
np.savetxt(f, all_frequency_weighted_IU, fmt='%.5f')
f.close()
def run_grafic_confusion_matrix(self):
'''
Generate a a graphical confusion matrix where images are classified and according to classification go to the wrong or right folder.
......@@ -1036,15 +1044,21 @@ class Act(object):
self.tk.write_log(header_output_middle + 'Initializing...')
total = str(len(images))
# internal function in method for create threads, cannot change for Process(Have a problem with JVM Instances)
total = str(len(images))
print("Waiting finish classification!")
for i, image_path in enumerate(images):
original_name=reduce(lambda a,b:a+b, image_path)
real_class_path=matrix_path+human+image_path[1]
predicted=self.classifier.single_classify(original_name, folder, self.extractors, classes)
original_name = reduce(lambda a, b: a + b, image_path)
real_class_path = matrix_path + human + image_path[1]
predicted = self.classifier.single_classify(original_name, folder, self.extractors, classes)
message = header_output_middle + str(i + 1) + ' of ' + total + ' images classifield.'
self.tk.write_log(message)
predicted_class_path = real_class_path+computer+predicted
predicted_name=predicted_class_path+image_path[2]
predicted_class_path = real_class_path + computer + predicted
predicted_name = predicted_class_path + image_path[2]
symlink(original_name, predicted_name)
message = header_output + 'Saved in ' + matrix_path
self.tk.write_log(message)
src/util/split_convertall.sh 0 → 100644
View file @ 7c3986ee
#!/bin/bash
#
# Script - convert tif to png
#
# Name: script_convertall.sh
# Author: Gabriel Kirsten Menezes (gabriel.kirsten@hotmail.com)
#
echo "[SCRIPT CONVERT ALL] Initializing..."
dir_train="../../data/demo_split/train"
dir_validation="../../data/demo_split/validation"
echo "[SCRIPT CONVERT ALL] Converting train..."
for dir_class in `ls $dir_train`;
do
echo "[SCRIPT CONVERT ALL] Converting class -" $dir_class;
convert $dir_train/$dir_class/* $dir_train/$dir_class/$dir_class.png
echo "[SCRIPT CONVERT ALL] Removing all .tif files in $dir_class ..."
rm $dir_train/$dir_class/*.tif
done
echo "[SCRIPT CONVERT ALL] Converting validation..."
for dir_class in `ls $dir_validation`;
do
echo "[SCRIPT CONVERT ALL] Converting class -" $dir_class;
convert $dir_validation/$dir_class/* $dir_validation/$dir_class/$dir_class.png
echo "[SCRIPT CONVERT ALL] Removing all .tif files in $dir_class ..."
rm $dir_validation/$dir_class/*.tif
done
echo "[SCRIPT CONVERT ALL] OK! DONE."
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