def main():
img_size = 505
gpu_id, net_path, model_path, img_paths = process_arguments(sys.argv)
palette = pascal_palette_invert()
net = Segmenter(net_path, model_path, gpu_id)
for img_path in img_paths:
img, cur_h, cur_w = preprocess_image(img_path, img_size)
segm_result = net.predict([img])
segm_post = postprocess_segmentation(segm_result, cur_h, cur_w, palette)
concatenate = True
segm_name = os.path.basename(img_path).split('.')[0]+'-label.png'
save_result(segm_post, segm_name, concatenate, img_path)
def main():
img_size = 505
gpu_id, net_path, model_path, img_paths = process_arguments(sys.argv)
palette = pascal_palette_invert()
net = Segmenter(net_path, model_path, gpu_id)
for img_path in img_paths:
img, cur_h, cur_w = preprocess_image(img_path, img_size)
segm_result = net.predict([img])
segm_post = postprocess_segmentation(segm_result, cur_h, cur_w,
palette)
concatenate = True
segm_name = os.path.basename(img_path).split('.')[0] + '-label.png'
save_result(segm_post, segm_name, concatenate, img_path)
def segment(self, image, slow=False):
def predictor(X):
X = np.expand_dims(X, axis=0)
y = self.predict(X)
y = np.squeeze(y, axis=0)
return y
if slow:
input_height = self.S.image_height
input_width = self.S.image_width
else:
_, input_height, input_width = image.shape
segmenter = Segmenter(predictor,
self.S.image_depth,
input_height,
input_width,
image)
return segmenter.predict()
def test_net(net_path, model_path, images, labels, lut, gpu_id):
net = Segmenter(net_path, model_path, gpu_id)
mean_vec = np.array([103.939, 116.779, 123.68], dtype=np.float32)
reshaped_mean_vec = mean_vec.reshape(1, 1, 3);
pa_list = []
ma_list = []
m_IU_list = []
fw_IU_list = []
pb = ProgressBar(len(images))
for img_path, label_path in zip(images, labels):
im, cur_h, cur_w = preprocess_image(img_path, reshaped_mean_vec)
label = imread(label_path)
label = lut[label]
segmentation = net.predict([im])
pred = segmentation[0:cur_h, 0:cur_w]
pa = pixel_accuracy(pred, label)
ma = mean_accuracy(pred, label)
m_IU = mean_IU(pred, label)
fw_IU = frequency_weighted_IU(pred, label)
pa_list.append(pa)
ma_list.append(ma)
m_IU_list.append(m_IU)
fw_IU_list.append(fw_IU)
pb.print_progress()
print("pixel_accuracy: " + str(np.mean(pa_list)))
print("mean_accuracy: " + str(np.mean(ma_list)))
print("mean_IU: " + str(np.mean(m_IU_list)))
print("frequency_weighted: " + str(np.mean(fw_IU_list)))
