In [1]:
%pylab inline
import warnings
warnings.filterwarnings("ignore")
import nolearn
from nolearn.lasagne import NeuralNet
import readdata
import lasagne
from lasagne import layers
from sklearn import metrics
import detectobjects as det
import os.path
from scipy import misc
import cv2
from progress_bar import ProgressBar
import shapefeatures
from sklearn import ensemble
In [2]:
opts = {'img_dir': '../data/tuberculosis-subset2/',
'annotation_dir': '../data/tuberculosis-subset2/',
'detection_probability_threshold': 0.5,
'detection_overlap_threshold': 0.3,
'gauss': 1,
'patch_size': (160,160),
'image_downsample' : 8,
'detection_step': 5,
'patch_creation_step': 40,
'object_class': None,
'negative_training_discard_rate': .9
}
opts['patch_stride_training'] = int(opts['patch_size'][0]*.25)
In [3]:
reload(readdata)
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In [6]:
trainfiles, valfiles, testfiles = readdata.create_sets(opts['img_dir'], train_set_proportion=.5,
test_set_proportion=.5,
val_set_proportion=0)
train_y, train_X = readdata.create_patches(trainfiles, opts['annotation_dir'], opts['img_dir'], opts['patch_size'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], objectclass=opts['object_class'], negative_discard_rate=opts['negative_training_discard_rate'])
test_y, test_X = readdata.create_patches(testfiles, opts['annotation_dir'], opts['img_dir'], opts['patch_size'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], objectclass=opts['object_class'], negative_discard_rate=opts['negative_training_discard_rate'])
# Cut down on disproportionately large numbers of negative patches
train_X, train_y = readdata.balance(train_X, train_y, mult_neg=100)
#test_X, test_y = readdata.balance(test_X, test_y, mult_neg=100)
# Create rotated and flipped versions of the positive patches
train_X, train_y = readdata.augment_positives(train_X, train_y)
test_X, test_y = readdata.augment_positives(test_X, test_y)
print '\n'
print '%d positive training examples, %d negative training examples' % (sum(train_y), len(train_y)-sum(train_y))
print '%d positive testing examples, %d negative testing examples' % (sum(test_y), len(test_y)-sum(test_y))
print '%d patches (%.1f%% positive)' % (len(train_y)+len(test_y), 100.*((sum(train_y)+sum(test_y))/(len(train_y)+len(test_y))))
View a random selection of positive and negative patches to see if they look right
In [7]:
N_samples_to_display = 10
pos_indices = np.where(train_y)[0]
pos_indices = pos_indices[np.random.permutation(len(pos_indices))]
for i in range(N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_pos = train_X[pos_indices[i],:,:,:]
example_pos = np.swapaxes(example_pos,0,2)
plt.imshow(example_pos)
plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')
neg_indices = np.where(train_y==0)[0]
neg_indices = neg_indices[np.random.permutation(len(neg_indices))]
for i in range(N_samples_to_display,2*N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_neg = train_X[neg_indices[i],:,:,:]
example_neg = np.swapaxes(example_neg,0,2)
plt.imshow(example_neg)
plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')
plt.gcf().set_size_inches(1.5*N_samples_to_display,3)
In [8]:
def CNN(n_epochs):
net1 = NeuralNet(
layers=[
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer), #Convolutional layer. Params defined below
('pool1', layers.MaxPool2DLayer), # Like downsampling, for execution speed
('conv2', layers.Conv2DLayer),
('hidden3', layers.DenseLayer),
('output', layers.DenseLayer),
],
input_shape=(None, 3, opts['patch_size'][0]/opts['image_downsample'],
opts['patch_size'][0]/opts['image_downsample']),
conv1_num_filters=7,
conv1_filter_size=(3, 3),
conv1_nonlinearity=lasagne.nonlinearities.rectify,
pool1_pool_size=(2, 2),
conv2_num_filters=12,
conv2_filter_size=(2, 2),
conv2_nonlinearity=lasagne.nonlinearities.rectify,
hidden3_num_units=100,
output_num_units=2,
output_nonlinearity=lasagne.nonlinearities.softmax,
update_learning_rate=0.0001,
update_momentum=0.9,
max_epochs=n_epochs,
verbose=1,
)
return net1
cnn = CNN(500).fit(train_X, train_y)
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In [12]:
y_pred = cnn.predict_proba(test_X)
false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])
roc_auc = metrics.auc(false_positive_rate, true_positive_rate)
precision, recall, thresholds = metrics.precision_recall_curve(test_y, y_pred[:,1])
average_precision = metrics.average_precision_score(test_y, y_pred[:, 1])
subplot(121)
plt.title('ROC: AUC = %0.2f'% roc_auc)
plt.plot(false_positive_rate, true_positive_rate, 'b')
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.ylim([-.05, 1.05])
plt.xlim([-.05, 1.0])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
subplot(122)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall: AP={0:0.2f}'.format(average_precision))
plt.legend(loc="lower left")
plt.gcf().set_size_inches(10,4)
plt.savefig('figs/tuberculosis-patchevaluation.png', bbox_inches='tight')
In [10]:
y_pred = cnn.predict_proba(train_X)
false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(train_y, y_pred[:,1])
roc_auc = metrics.auc(false_positive_rate, true_positive_rate)
precision, recall, thresholds = metrics.precision_recall_curve(train_y, y_pred[:,1])
average_precision = metrics.average_precision_score(train_y, y_pred[:, 1])
subplot(121)
plt.title('ROC: AUC = %0.2f'% roc_auc)
plt.plot(false_positive_rate, true_positive_rate, 'b')
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.ylim([-.05, 1.05])
plt.xlim([-.05, 1.0])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
subplot(122)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall: AP={0:0.2f}'.format(average_precision))
plt.legend(loc="lower left")
plt.gcf().set_size_inches(10,4)
plt.savefig('figs/tuberculosis-patchevaluation.png', bbox_inches='tight')
In [13]:
false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])
true_positive_rate.shape, thresholds.shape
plt.plot(true_positive_rate, thresholds,label='True positive rate')
plt.plot(false_positive_rate, thresholds, label='False positive rate')
plt.xlabel('Threshold')
plt.ylim([0,1.01])
plt.legend(loc='upper left')
Out[13]:
Examine mistakes to understand network performance: false positives.
Find the negative-labelled patches with highest prediction score
In [ ]:
neg_indices = np.where(test_y==0)[0]
neg_scores = y_pred[neg_indices,1]
neg_indices = neg_indices[neg_scores.argsort()]
neg_indices = neg_indices[::-1]
neg_scores = y_pred[neg_indices,1]
N_samples_to_display = 12
offset = 55
for i in range(N_samples_to_display,2*N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_neg = test_X[neg_indices[i+offset],:,:,:]
example_neg = np.swapaxes(example_neg,0,2)
plt.imshow(example_neg)
plt.title('%.3f' % neg_scores[i+offset])
plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')
plt.gcf().set_size_inches(1.5*N_samples_to_display,3)
plt.savefig('figs/tuberculosis-falsedetections.png', bbox_inches='tight')
See highest-scored test patches
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prob_range = [.95,1.]
tmp_scores = y_pred.copy()[:,1]
tmp_scores[tmp_scores<prob_range[0]] = -1
tmp_scores[tmp_scores>prob_range[1]] = -1
pos_indices = tmp_scores.argsort()
pos_indices = pos_indices[::-1]
N_samples_to_display = 12
offset = 0
for i in range(N_samples_to_display,2*N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_neg = test_X[pos_indices[i+offset],:,:,:]
example_neg = np.swapaxes(example_neg,0,2)
plt.imshow(example_neg)
plt.title('%.3f' % (tmp_scores[pos_indices[i+offset]]))
plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')
plt.gcf().set_size_inches(1.5*N_samples_to_display,3)
plt.savefig('figs/tuberculosis-detectedpatches.png', bbox_inches='tight')
See lowest scored test patches
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pos_indices = y_pred[:,1].argsort()
N_samples_to_display = 12
for i in range(N_samples_to_display,2*N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_neg = test_X[pos_indices[i],:,:,:]
example_neg = np.swapaxes(example_neg,0,2)
plt.imshow(example_neg)
plt.title('%.3f' % (y_pred[pos_indices[i],1]))
plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')
plt.gcf().set_size_inches(1.5*N_samples_to_display,3)
plt.savefig('figs/tuberculosis-testpatches-lowprob.png', bbox_inches='tight')
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In [30]:
reload(det)
fname = testfiles[0]
imfile = opts['img_dir'] + fname
opts['detection_probability_threshold'] = 0.95
found = det.detect(imfile, cnn, opts)
im = misc.imread(imfile)
plt.box(False)
plt.xticks([])
plt.yticks([])
annofile = opts['annotation_dir'] + fname[:-3] + 'xml'
bboxes = readdata.get_bounding_boxes_for_single_image(annofile)
for bb in bboxes:
bb = bb.astype(int)
cv2.rectangle(im, (bb[0],bb[2]), (bb[1],bb[3]), (255,255,255), 8)
for f in found:
f = f.astype(int)
cv2.rectangle(im, (f[0],f[1]), (f[2],f[3]), (255,0,0), 8)
plt.gcf().set_size_inches(10,10)
plt.title('Detected objects in %s' % (imfile))
plt.imshow(im)
#cv2.imwrite('detectionimages/detected-' + os.path.basename(imfile),im)
Out[30]:
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featureset = [3,7,11,12,15,17]
centiles = [0,25,50,75,100]
pb = ProgressBar(train_X.shape[0])
train_X_f = []
for i in range(train_X.shape[0]):
if i % 100 == 0:
pb.step(i)
graypatch = cv2.cvtColor(np.swapaxes(train_X[i,:,:,:],0,2).astype('uint8'), cv2.COLOR_BGR2GRAY)
train_X_f.append(shapefeatures.extract(graypatch,attributes=featureset,centiles=centiles, momentfeatures=True))
train_X_f = np.vstack(train_X_f)
test_X_f = []
for i in range(test_X.shape[0]):
if i % 100 == 0:
pb.step(i)
graypatch = cv2.cvtColor(np.swapaxes(test_X[i,:,:,:],0,2).astype('uint8'), cv2.COLOR_BGR2GRAY)
test_X_f.append(shapefeatures.extract(graypatch,attributes=featureset,centiles=centiles, momentfeatures=True))
test_X_f = np.vstack(test_X_f)
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clf = ensemble.ExtraTreesClassifier(n_estimators=100, max_depth=5, n_jobs=-1)
clf.fit(train_X_f, train_y)
y_pred_CLF = clf.predict_proba(test_X_f)
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false_positive_rate_CNN, true_positive_rate_CNN, thresholds_CNN = metrics.roc_curve(test_y, y_pred[:,1])
roc_auc_CNN = metrics.auc(false_positive_rate_CNN, true_positive_rate_CNN)
precision_CNN, recall_CNN, thresholds_CNN = metrics.precision_recall_curve(test_y, y_pred[:,1])
average_precision_CNN = metrics.average_precision_score(test_y, y_pred[:, 1])
false_positive_rate_CLF, true_positive_rate_CLF, thresholds_CLF = metrics.roc_curve(test_y, y_pred_CLF[:,1])
roc_auc_CLF = metrics.auc(false_positive_rate_CLF, true_positive_rate_CLF)
precision_CLF, recall_CLF, thresholds_CLF = metrics.precision_recall_curve(test_y, y_pred_CLF[:,1])
average_precision_CLF = metrics.average_precision_score(test_y, y_pred_CLF[:, 1])
subplot(211)
plt.title('ROC' )
plt.plot(false_positive_rate_CNN, true_positive_rate_CNN, 'b', label='CNN: AUC=%.2f' % (roc_auc_CNN))
plt.plot(false_positive_rate_CLF, true_positive_rate_CLF, 'k--', label='ERT: AUC=%.2f' % (roc_auc_CLF))
plt.legend(loc='lower right')
plt.ylim([-.05, 1.05])
plt.xlim([-.05, 1.0])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
subplot(212)
plt.plot(recall_CNN, precision_CNN, label='CNN: AP=%.2f' % (average_precision_CNN))
plt.plot(recall_CLF, precision_CLF,'k--', label='ERT: AP=%.2f' % (average_precision_CLF))
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall')
plt.legend(loc="lower left")
plt.gcf().set_size_inches(4,10)
plt.savefig('figs/tuberculosis-patchevaluation.png', bbox_inches='tight')
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results = {
"false_positive_rate_CNN": false_positive_rate_CNN,
"true_positive_rate_CNN": true_positive_rate_CNN,
"false_positive_rate_CLF": false_positive_rate_CLF,
"true_positive_rate_CLF": true_positive_rate_CLF,
"roc_auc_CNN": roc_auc_CNN,
"roc_auc_CLF": roc_auc_CLF,
"recall_CNN": recall_CNN,
"precision_CNN": precision_CNN,
"average_precision_CNN": average_precision_CNN,
"recall_CLF": recall_CLF,
"precision_CLF": precision_CLF,
"average_precision_CLF": average_precision_CLF,
"opts": opts
}
import pickle
pickle.dump(results,open('tuberculosis-results.pkl','w'))