featureCount = algo.extract_features(all_files, TMP_DIR)
# generate codebook
clusterCount = int(sqrt(featureCount))
algo.gen_codebook(TMP_DIR, clusterCount, SIFT_CODEBOOK,
batch_size = algo.BATCH_SIZE if algo.BATCH_SIZE >= clusterCount else clusterCount)
# generate histograms
algo.compute_histograms(TMP_DIR, SIFT_CODEBOOK, TMP_DIR)
# train svm
algo.train_svm(TMP_DIR, all_labels, SVM_MODEL_FILE, all_weights = all_weights)
print "calculating predictions"
predictions = algo.predict(SVM_MODEL_FILE, SIFT_CODEBOOK, DATASETPATH2, TMP_DIR)
img = Image.open('dop' +f + '/dop-annotated.png').convert('RGBA')
overlay = Image.new('RGBA', img.size, 0)
draw = ImageDraw.Draw(overlay)
print "\n\nPredictions:"
for filepath, is_building in predictions.items():
filename = os.path.basename(filepath)
coverage, x, y = os.path.splitext(filename)[0].split('_')
x = int(x); y = int(y)
#print '{coverage}: {is_building}'.format(coverage=coverage, is_building=is_building[0])
if is_building[0] == 1:
draw.rectangle([x,y,x+PATCH_SIZE, y+PATCH_SIZE], fill=(0x8a, 0x2b, 0xe2, 0x55), outline='grey')
else:
FP = 0
TN = 0
FN = 0
R = 0
I = 0
for f in validation_files:
if all_labels[os.path.basename(f)] == 1:
R += 1
else:
I += 1
algo.__clear_dir(TMP_DIR_VALIDATION)
predictions = algo.predict(
SVM_MODEL_FILE + str(len(performances)),
currentCodebook,
VALIDATIONSET_DIR,
TMP_DIR_VALIDATION,
)
for f, p in predictions.items():
if p[0] == 1:
if all_labels[os.path.basename(f)] == 1:
TP += 1
else:
FP += 1
else:
if all_labels[os.path.basename(f)] == 1:
FN += 1
else:
TN += 1
TPR = float(TP) / float(R)
FPR = float(FP) / float(I)
# generate patches
print "---------------------"
print "## generating patches from '" + IMG_NAME + "' (" + str(IMG_SIZE[0])+"x"+str(IMG_SIZE[1]) + "; " + str(IMG_BBOX) + ")"
patch_generator.generate_patches(IMG_BBOX, IMG_SIZE,
patch_size=params['hyperparameters']['patch_size'],
offset_steps=params['hyperparameters']['patch_offset'],
target_folder=DATASET_DIR,
force_refresh=False,
data_folder=IMG_NAME,
)
print ""
# predict
print "---------------------"
print "## predicting"
predictions = algo.predict(SVM_MODEL_FILE, SIFT_CODEBOOK_FILE, DATASET_DIR, TMP_DIR)
# generate visualization
print "---------------------"
print "## generating visualization"
img = Image.open(SATELLITE_IMG_VISUALIZATION_INPUT)
overlay = Image.new('RGB', img.size, 0)
draw = ImageDraw.Draw(overlay)
print "\n\nPredictions:"
for filepath, is_building in predictions.items():
filename = os.path.basename(filepath)
print filename
coverage, x, y = os.path.splitext(filename)[0].split('_')