def expand_resize_color_data(prediction=None, submission_size=[3384, 1710], offset=690):
color_pred_mask = decode_color_labels(prediction)
color_pred_mask = np.transpose(color_pred_mask, (1, 2, 0))
color_expand_mask = cv2.resize(color_pred_mask, (submission_size[0], submission_size[1] - offset), interpolation=cv2.INTER_NEAREST)
color_submission_mask = np.zeros((submission_size[1], submission_size[0], 3), dtype='uint8')
color_submission_mask[offset:, :, :] = color_expand_mask
return color_submission_mask
def get_color_mask(pred):
pred = torch.softmax(pred, dim=1)
pred = torch.argmax(pred, dim=1)
pred = torch.squeeze(pred)
pred = pred.detach().cpu().numpy()
pred = decode_color_labels(pred)
pred = np.transpose(pred, (1, 2, 0))
return pred
def get_color_mask(pred):
pred = torch.softmax(pred, dim=1)
# 将 channel 取得的最大的响应作为标签
pred = torch.argmax(pred, dim=1)
# squeeze 将某些维度上的1 去掉
pred = torch.squeeze(pred)
pred = pred.detach().cpu().numpy()
# 转换成 color 的 label
pred = decode_color_labels(pred)
# 将通道数返回来
pred = np.transpose(pred, (1, 2, 0))
return pred
def main():
IMG_SIZE =[1536, 512]
SUBMISSION_SIZE = [3384, 1710]
save_test_logits = False
num_classes = 8
batch_size = 4
log_iters = 100
network = 'unet_simple'
# Define paths for each model
if network == 'deeplabv3p':
model_path = "./model_weights/paddle_deeplabv3p_8_end_060223"
npy_dir = '/npy_save/deeplabv3p/'
elif network == 'unet_base':
model_path = "./model_weights/paddle_unet_base_10_end_059909"
npy_dir = '/npy_save/unet_base/'
elif network == 'unet_simple':
model_path = "./model_weights/paddle_unet_simple_12_end_060577"
npy_dir = '/npy_save/unet_simple/'
program_choice = 2 # 1 - Validtion; 2 - Test
show_label = False
crop_offset = 690
data_dir = './data_list/val.csv'
test_dir = '/root/private/LaneDataSet/TestSet/Image_Data/ColorImage/'
sub_dir = './test_submission/'
# Get data list and split it into train and validation set.
val_list = pd.read_csv(data_dir)
iter_id = 0
total_loss = 0.0
total_miou = 0.0
prev_time = time.time()
# Validation
if program_choice == 1:
val_reader = val_image_gen(val_list, batch_size=batch_size, image_size=IMG_SIZE, crop_offset=crop_offset)
model = create_network(network=network)
model.load_weights(model_path)
print("loaded model from: %s" % model_path)
print('Start Validation!')
for iteration in range(int(len(val_list) / batch_size)):
val_data,val_label = next(val_reader)
results = model.evaluate(val_data,val_label)
if iter_id % log_iters == 0:
print('Finished Processing %d Images.' %(iter_id * batch_size))
iter_id += 1
total_loss += np.mean(results[0])
total_miou += np.mean(results[1])
# label to mask
if show_label == True:
label_image = val_label[0]
color_label_mask = decode_color_labels(label_image)
color_label_mask = np.transpose(color_label_mask, (1, 2, 0))
cv2.imshow('gt_label', cv2.resize(color_label_mask, (IMG_SIZE[0], IMG_SIZE[1])))
prediction = np.argmax(results[2][0], axis=0)
color_pred_mask = decode_color_labels(prediction)
color_pred_mask = np.transpose(color_pred_mask, (1, 2, 0))
cv2.imshow('pred_label', cv2.resize(color_pred_mask, (IMG_SIZE[0], IMG_SIZE[1])))
cv2.waitKey(0)
end_time = time.time()
print("validation loss: %.3f, mean iou: %.3f, time cost: %.3f s"
% (total_loss / iter_id, total_miou / iter_id, end_time - prev_time))
# Test
elif program_choice == 2:
model = create_network(network=network)
model.load_weights(model_path)
print("loaded model from: %s" % model_path)
print('Start Making Submissions!')
test_list = os.listdir(test_dir)
for test_name in test_list:
test_ori_image = cv2.imread(os.path.join(test_dir, test_name))
test_image = crop_resize_data(test_ori_image, label=None, image_size=IMG_SIZE, offset=crop_offset)
out_image = np.expand_dims(np.array(test_image), axis=0)
out_image = out_image[:, :, :, ::-1].transpose(0, 3, 1, 2).astype(np.float32) / (255.0 / 2) - 1
results_1 = model.evaluate(out_image,None)
if iter_id % 20 == 0:
print('Finished Processing %d Images.' %(iter_id))
iter_id += 1
prediction = np.argmax(results_1[0][0], axis=0)
# Save npy files
if save_test_logits == True:
np.save(npy_dir + test_name.replace('.jpg', '.npy'), results_1[0][0])
# Save Submission PNG
submission_mask = expand_resize_data(prediction, SUBMISSION_SIZE, crop_offset)
cv2.imwrite(os.path.join(sub_dir, test_name.replace('.jpg', '.png')), submission_mask)
# Show Label
if show_label == True:
cv2.imshow('test_image', cv2.resize(test_ori_image,(IMG_SIZE[0], IMG_SIZE[1])))
cv2.imshow('pred_label', cv2.resize(submission_mask,(IMG_SIZE[0], IMG_SIZE[1])))
cv2.waitKey(0)
sys.stdout.flush()
# from PIL import Image
import matplotlib as mpl
mpl.use('TkAgg') # or whatever other backend that you want
import matplotlib.pyplot as plt
# import torch
import pandas as pd
from utils.process_labels import code_init,encode_labels,decode_labels,decode_color_labels,verify_labels
from utils.image_process import crop_resize_data,CLAHE_nomalization,contrast,random_filp
from Baidureader import BaiduDataset
data=BaiduDataset('./train.csv', [1024, 384])
image,label,ins=data[0]
print(image.size())
print(label.size())
label_=np.array(label,dtype='uint8')
verify_labels(label_)
decode_mask=decode_color_labels(label_)
plt.imshow(decode_mask.transpose(1,2,0))
plt.show()
# cv2.imshow('img',image)
# cv2.imshow('label',label)
# cv2.imshow('ins',ins[0])
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# a=list()
# a.append('./170927_063811892_Camera_5.jpg')
# b=list()
# b.append('./170927_063811892_Camera_5_bin.png')
# print(len(a))
# print(len(b))
# c ={'image':a,'label':b}#合并成一个新的字典c
def main():
IMG_SIZE =[1536, 512]
SUBMISSION_SIZE = [3384, 1710]
save_test_logits = False
num_classes = 8
batch_size = 4
log_iters = 100
network = 'unet_simple'
# Define paths for each model
if network == 'deeplabv3p':
model_path = "./model_weights/paddle_deeplabv3p_8_end_060223"
npy_dir = '/npy_save/deeplabv3p/'
elif network == 'unet_base':
model_path = "./model_weights/paddle_unet_base_10_end_059909"
npy_dir = '/npy_save/unet_base/'
elif network == 'unet_simple':
model_path = "./model_weights/paddle_unet_simple_12_end_060577"
npy_dir = '/npy_save/unet_simple/'
program_choice = 2 # 1 - Validtion; 2 - Test
show_label = False
crop_offset = 690
data_dir = './data_list/val.csv'
test_dir = '../PaddlePaddle/TestSet_Final/ColorImage/'
sub_dir = './test_submission/'
# Get data list and split it into train and validation set.
val_list = pd.read_csv(data_dir)
#Initialization
images = fluid.layers.data(name='image', shape=[3, IMG_SIZE[1], IMG_SIZE[0]], dtype='float32')
labels = fluid.layers.data(name='label', shape=[1, IMG_SIZE[1], IMG_SIZE[0]], dtype='float32')
iter_id = 0
total_loss = 0.0
total_miou = 0.0
prev_time = time.time()
# Validation
if program_choice == 1:
val_reader = val_image_gen(val_list, batch_size=batch_size, image_size=IMG_SIZE, crop_offset=crop_offset)
reduced_loss, miou, pred = create_network(images, labels, num_classes, network=network, image_size=(IMG_SIZE[1], IMG_SIZE[0]), for_test=False)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid.io.load_params(exe, model_path)
print("loaded model from: %s" % model_path)
# Parallel Executor to use multi-GPUs
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.allow_op_delay = True
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
train_exe = fluid.ParallelExecutor(use_cuda=True, build_strategy=build_strategy, exec_strategy=exec_strategy)
print('Start Validation!')
for iteration in range(int(len(val_list) / batch_size)):
val_data = next(val_reader)
results = train_exe.run(
feed=get_feeder_data(val_data, place),
fetch_list=[reduced_loss.name, miou.name, pred.name])
if iter_id % log_iters == 0:
print('Finished Processing %d Images.' %(iter_id * batch_size))
iter_id += 1
total_loss += np.mean(results[0])
total_miou += np.mean(results[1])
# label to mask
if show_label == True:
label_image = val_data[1][0]
color_label_mask = decode_color_labels(label_image)
color_label_mask = np.transpose(color_label_mask, (1, 2, 0))
cv2.imshow('gt_label', cv2.resize(color_label_mask, (IMG_SIZE[0], IMG_SIZE[1])))
prediction = np.argmax(results[2][0], axis=0)
color_pred_mask = decode_color_labels(prediction)
color_pred_mask = np.transpose(color_pred_mask, (1, 2, 0))
cv2.imshow('pred_label', cv2.resize(color_pred_mask, (IMG_SIZE[0], IMG_SIZE[1])))
cv2.waitKey(0)
end_time = time.time()
print("validation loss: %.3f, mean iou: %.3f, time cost: %.3f s"
% (total_loss / iter_id, total_miou / iter_id, end_time - prev_time))
# Test
elif program_choice == 2:
predictions = create_network(images, labels, num_classes, network=network, image_size=(IMG_SIZE[1], IMG_SIZE[0]), for_test=True)
place = fluid.CUDAPlace(0)
# place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid.io.load_params(exe, model_path)
print("loaded model from: %s" % model_path)
print('Start Making Submissions!')
test_list = os.listdir(test_dir)
for test_name in test_list:
test_ori_image = cv2.imread(os.path.join(test_dir, test_name))
test_image = crop_resize_data(test_ori_image, label=None, image_size=IMG_SIZE, offset=crop_offset)
out_image = np.expand_dims(np.array(test_image), axis=0)
out_image = out_image[:, :, :, ::-1].transpose(0, 3, 1, 2).astype(np.float32) / (255.0 / 2) - 1
feed_dict = {}
feed_dict["image"] = out_image
results_1 = exe.run(
feed=feed_dict,
fetch_list=[predictions])
if iter_id % 20 == 0:
print('Finished Processing %d Images.' %(iter_id))
iter_id += 1
prediction = np.argmax(results_1[0][0], axis=0)
# Save npy files
if save_test_logits == True:
np.save(npy_dir + test_name.replace('.jpg', '.npy'), results_1[0][0])
# Save Submission PNG
submission_mask = expand_resize_data(prediction, SUBMISSION_SIZE, crop_offset)
cv2.imwrite(os.path.join(sub_dir, test_name.replace('.jpg', '.png')), submission_mask)
# Show Label
if show_label == True:
cv2.imshow('test_image', cv2.resize(test_ori_image,(IMG_SIZE[0], IMG_SIZE[1])))
cv2.imshow('pred_label', cv2.resize(submission_mask,(IMG_SIZE[0], IMG_SIZE[1])))
cv2.waitKey(0)
sys.stdout.flush()
