image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, dsize=(image_w, image_h))
result = model.predict(image.reshape(1, image_h, image_w, 3))
res = result[0, :, :, 0]
# if image sise different from 240x320
mask = cv2.resize(res, dsize=(240, 320))
kernel = np.ones((5, 5), np.float32) / 25
mask = cv2.blur(mask, (5, 5))
# mask = cv2.GaussianBlur(mask,(3,3),0)
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
# cv2.imshow('image', mask)
# cv2.waitKey(0)
# exit()
mask = np.asarray(mask, bool)
res = remove_small_objects(mask, min_size=256, connectivity=1)
res = np.asarray(res, 'uint8')
enc = rle_encode(res)
print('Saving ' + str(i) + ' image..')
sample.set_value(int(img.split('.')[0]), 'rle_mask', enc)
sample.to_csv(exargs.name + '.csv')
model_json = model.to_json()
json_file = open('../models/' + exargs.name + '.json', 'w')
json_file.write(model_json)
json_file.close()
print('Model saved!')
K.clear_session()
m.compile(optimizer=opt, loss='categorical_crossentropy', metrics=[iou_score])
# fit model
weights_path = args.ckpt_path + 'weights.{epoch:02d}-{val_loss:.2f}-{val_iou_score:.2f}.hdf5'
callbacks = get_callbacks(weights_path, args.ckpt_path, 5, args.opt)
history = m.fit_generator(train_gen,
epochs=args.epochs,
steps_per_epoch=(NO_OF_TRAINING_IMAGES //
BATCH_SIZE),
validation_data=(val_x / 255, val_y),
shuffle=True,
callbacks=callbacks)
''' save model structure '''
model_json = m.to_json()
with open(os.path.join(args.ckpt_path, "model.json"), "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
print("Saved model to disk")
m.save(os.path.join(args.ckpt_path, 'model.h5'))
'''Evaluate and Test '''
print('======Start Evaluating======')
#don't use generator but directly from array
score = m.evaluate(val_x / 255, val_y, verbose=0)
print("%s: %.2f%%" % (m.metrics_names[0], score[0] * 100))
print("%s: %.2f%%" % (m.metrics_names[1], score[1] * 100))
with open(os.path.join(args.ckpt_path, 'output.txt'), "w") as file:
file.write("%s: %.2f%%" % (m.metrics_names[0], score[0] * 100))
file.write("%s: %.2f%%" % (m.metrics_names[1], score[1] * 100))
def workflow(self):
# define model
model = Unet(backbone_name='resnet50', encoder_weights='imagenet')
adam = keras.optimizers.Adam(lr=self.cfgs["LEARNING_RATE"])
model.summary()
# model.compile('Adam', sigmoid_cross_entropy_balanced)
model.compile(
'Adam',
# cross_entropy_balanced
loss=self.define_loss(),
# 'binary_crossentropy'
)
test_images, test_ulabels, test_elabels, test_rlabels, filelist = self.dl.get_test_data(
)
if self.cfgs["RESTORE"]:
model.load_weights(
os.path.join(self.cfgs["SAVE_DIR"], "weights", "epoch150.h5"))
print("RETORE SUCCESSFULLY!")
callback = TensorBoard('./graph')
callback.set_model(model)
train_names = [
'loss', 'u_outputs_sig_loss', 'e_fuse_sig_loss', 'r_fuse_sig_loss',
'fuse_dir_loss'
]
current_learning_rate = self.cfgs["LEARNING_RATE"]
K.set_value(model.optimizer.lr, current_learning_rate)
for i in range(self.cfgs["EPOCH"]):
print("[I] EPOCH {}".format(i))
# TRAIN
for j in tqdm(range(self.cfgs["STEP"])):
images_batch, ulabels_batch, elabels_batch, rlabels_batch, d_labels_batch = self.dl.next_batch(
"train")
Logs = model.train_on_batch(
images_batch,
self.define_train_y(ulabels_batch, elabels_batch,
rlabels_batch, d_labels_batch),
)
write_log(callback, train_names, Logs, i)
if i % self.cfgs["INTERVAL"] == 0 and i >= 0:
# TEST:
results = model.predict(test_images, batch_size=10, verbose=0)
logits = results[-1]
r_logits = results[-2]
# result analyse and show
rlt_worker = ResultManager(i, logits, test_ulabels)
# r_analyst.compute_roc(savename='roc_vegas_{}.csv'.format(i))
# rlt_worker_r = ResultManager(i, r_logits, test_rlabels)
rlt_worker.run()
# rlt_worker_r.run()
for ii in range(results[0].shape[0]):
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/images/{}'.format(filelist[ii][0])),
# test_images[ii, :] * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/labels/{}'.format(filelist[ii][1])),
# test_ulabels[ii, :] * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/labels_e/{}'.format(filelist[ii][1])),
# test_elabels[ii, :] * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/labels_r/{}'.format(filelist[ii][1])),
# test_rlabels[ii, :] * 255)
#cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/preds/{}'.format(filelist[ii][1])),
# results[-1][ii, :])
pred_threshold = threshold(results[-1][ii, :])
cv2.imwrite(
os.path.join(
self.cfgs["SAVE_DIR"],
'main_outputs/preds_threshold/{}'.format(
filelist[ii][1])), pred_threshold * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_e1/{}'.format(filelist[ii][1])),
# threshold(results[1][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_e2/{}'.format(filelist[ii][1])),
# threshold(results[2][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_e3/{}'.format(filelist[ii][1])),
# threshold(results[3][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_e4/{}'.format(filelist[ii][1])),
# threshold(results[4][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_e5/{}'.format(filelist[ii][1])),
# threshold(results[5][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_r1/{}'.format(filelist[ii][1])),
# threshold(results[6][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_r2/{}'.format(filelist[ii][1])),
# threshold(results[7][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_r3/{}'.format(filelist[ii][1])),
# threshold(results[8][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_r4/{}'.format(filelist[ii][1])),
# threshold(results[9][ii, :]) * 255)
# cv2.imwrite(os.path.join(self.cfgs["SAVE_DIR"], 'main_outputs/out_r5/{}'.format(filelist[ii][1])),
# threshold(results[10][ii, :]) * 255)
# SAVE WEIGHTS
current_learning_rate = current_learning_rate * self.cfgs[
"LEARNING_RATE_DECAY"]
K.set_value(model.optimizer.lr, current_learning_rate)
print('[I] Current Learning Rate: ', current_learning_rate)
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights(
os.path.join(self.cfgs["SAVE_DIR"],
"epoch{}.h5".format(i)))
