def predict(input_dir, gpu_num=None):
(file_names, inputs) = load_images(input_dir,
INPUT_IMAGE_SHAPE,
with_normalize=WITH_NORM)
network = M2Det(INPUT_IMAGE_SHAPE, BATCH_SIZE, class_num=CLASS_NUM)
priors = network.get_prior_boxes()
bbox_util = BBoxUtility(CLASS_NUM, priors)
if isinstance(gpu_num, int):
model = network.get_parallel_model(gpu_num)
else:
model = network.get_model()
# model.summary()
print('loading weghts ...')
model.load_weights(os.path.join(DIR_MODEL, FILE_MODEL))
print('... loaded')
#"""
print('predicting ...')
preds = model.predict(inputs, BATCH_SIZE)
print('... predicted')
print('result saveing ...')
pred_pbox = preds[0, :, -8:]
results = bbox_util.detection_out(preds)
image_data = __outputs_to_image_data(inputs, results, file_names)
save_images(DIR_OUTPUTS,
image_data,
file_names,
with_unnormalize=WITH_NORM)
print('... finish .')
def predict(input_dir, gpu_num=None):
h, w, c = INPUT_IMAGE_SHAPE
org_h, org_w = h - (PADDING * 2), w - (PADDING * 2)
(file_names, inputs) = load_images(input_dir, (org_h, org_w, c))
inputs = np.pad(inputs, [(0, 0), (PADDING, PADDING), (PADDING, PADDING),
(0, 0)],
'constant',
constant_values=0)
network = UNet(INPUT_IMAGE_SHAPE, CLASS_NUM)
model = network.model()
plot_model(model, to_file='../model_plot.png')
model.summary()
if isinstance(gpu_num, int):
model = multi_gpu_model(model, gpus=gpu_num)
model.load_weights(os.path.join(DIR_MODEL, File_MODEL))
print('predicting ...')
preds = model.predict(inputs, BATCH_SIZE)
print('... predicted')
print('output saveing ...')
preds = preds[:, PADDING:org_h + PADDING, PADDING:org_w + PADDING, :]
save_images(DIR_OUTPUTS, preds, file_names)
print('... saved')
def predict(input_dir):
(file_names, inputs) = load_images(input_dir, INPUT_IMAGE_SIZE)
network = UNet(INPUT_IMAGE_SIZE)
model = network.model()
model.load_weights(os.path.join(DIR_MODEL, File_MODEL))
preds = model.predict(inputs, BATCH_SIZE)
save_images(DIR_OUTPUTS, preds, file_names)
def predict(input_dir, gpu_num=None):
(file_names, inputs) = load_images(input_dir, INPUT_IMAGE_SHAPE)
network = UNetPP(INPUT_IMAGE_SHAPE, start_filter=START_FILTER, depth=DEPTH, class_num=CLASS_NUM)
if isinstance(gpu_num, int):
model = network.get_parallel_model(gpu_num)
else:
model = network.get_model()
print('loading weghts ...')
model.load_weights(os.path.join(DIR_MODEL, File_MODEL))
print('... loaded')
print('predicting ...')
preds = model.predict(inputs, BATCH_SIZE)
print('... predicted')
print('result saveing ...')
save_images(DIR_OUTPUTS, preds, file_names)
print('... finish .')
def predict(input_dir, gpu_num=None):
h, w, c = INPUT_IMAGE_SHAPE
org_h, org_w = h - (PADDING * 2), w - (PADDING * 2)
(file_names, inputs) = load_images(input_dir, (org_h, org_w, c))
inputs = np.pad(inputs, [(0, 0), (PADDING, PADDING), (PADDING, PADDING), (0, 0)], 'constant', constant_values=0)
network = DeepUNet(INPUT_IMAGE_SHAPE, internal_filter=INTERNAL_FILTER, depth=DEPTH, class_num=CLASS_NUM)
if isinstance(gpu_num, int):
model = network.get_parallel_model(gpu_num)
else:
model = network.get_model()
# model.summary()
print('loading weghts ...')
model.load_weights(os.path.join(DIR_MODEL, File_MODEL))
print('... loaded')
print('predicting ...')
preds = model.predict(inputs, BATCH_SIZE)
print('... predicted')
print('result saveing ...')
preds = preds[:, PADDING:org_h+PADDING, PADDING:org_w+PADDING, :]
save_images(DIR_OUTPUTS, preds, file_names)
print('... finish .')