def predict_tree2():
with open('texton_forest-%s.pkl' % dataset._name) as fp:
tp = kontort.TextonPredict(pickle.load(fp))
with open('texton_forest2-%s.pkl' % dataset._name) as fp:
tp2 = kontort.IntegralPredict(pickle.load(fp))
save_key()
save_feature_hists(tp2.func_data, tp2.extra_dump_vars)
num_masks = tp2.func_data[0][tp2.extra_dump_vars.index('num_masks')]
print('Func Data2: [%s]' % str(tp2.func_data[0]))
for num, (masks, image) in enumerate(dataset.segmentation_boxes()):
ratio, image = resize(image)
orig_image = image
image = convert_color_bgr(image)
image = make_masks(image)
max_classes1, max_probs1, leaves1, all_probs1 = tp.predict(image, leaves=True, all_probs=True)
print('Num Tree 1 Leaves[%d]' % tp.num_leaves)
pred_integrals = convert_leaves_all_probs_pred(image, leaves1, all_probs1, tp.num_leaves)
preds, integrals = pred_integrals
print('num_masks[%s] integrals_shape[%s]' % (num_masks, integrals.shape))
assert num_masks == integrals.shape[-1]
max_classes2, max_probs2, all_probs2 = tp2.predict(pred_integrals, all_probs=True)
out = colors[max_classes2]
del integrals
try:
os.makedirs(out_root + '/view')
except OSError:
pass
cv2.imwrite('%s/view/%.05d-mask.png' % (out_root, num), out)
orig_image.save('%s/view/%.05d-img.png' % (out_root, num))
cv2.imwrite('%s/view/%.05d-texton.png' % (out_root, num), texton_colors[leaves1])
with gzip.GzipFile('%s/view/%.05d.pkl.gz' % (out_root, num), 'w') as fp:
pickle.dump({'max_classes1': max_classes1,
'max_probs1': max_probs1,
'leaves1': leaves1,
'all_probs1': all_probs1,
'max_classes2': max_classes2,
'max_probs2': max_probs2,
'all_probs2': all_probs2,
'gt_masks': masks,
'image': image,
'classes': classes}, fp, -1)
def map(self, k, v):
"""
Args:
k: Filename
v: (image_data, label_points)
Yields:
(k, (integral, label_points))
"""
image_data, label_points = v
argmax, max_probs, leaves, all_probs = self.tp.predict(image_data, all_probs=True, leaves=True)
print("Leaves[%s] AllProbs[%s]" % (str(leaves.shape), str(all_probs.shape)))
if self.integral_type == "argmax":
out = np.ascontiguousarray(imseg.convert_labels_to_integrals(argmax, self.tp.num_classes))
elif self.integral_type == "argmax_prob":
out = np.ascontiguousarray(imseg.convert_labels_probs_to_integrals(argmax, max_probs, self.tp.num_classes))
elif self.integral_type == "all_prob":
out = np.ascontiguousarray(imseg.convert_all_probs_to_integrals(all_probs))
elif self.integral_type == "spatial":
out = convert_leaves_all_probs_pred(image_data, leaves, all_probs, self.tp.num_leaves)
yield k, (out, label_points)
