2、如果想要保存，利用r_image.save("img.jpg")即可保存。
3、如果想要原图和分割图不混合，可以把blend参数设置成False。
4、如果想根据mask获取对应的区域，可以参考detect_image中，利用预测结果绘图的部分。
seg_img = np.zeros((np.shape(pr)[0],np.shape(pr)[1],3))
for c in range(self.num_classes):
seg_img[:, :, 0] += ((pr == c)*( self.colors[c][0] )).astype('uint8')
seg_img[:, :, 1] += ((pr == c)*( self.colors[c][1] )).astype('uint8')
seg_img[:, :, 2] += ((pr == c)*( self.colors[c][2] )).astype('uint8')
'''
import tensorflow as tf
from PIL import Image
from pspnet import Pspnet
gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
pspnet = Pspnet()
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = pspnet.detect_image(image)
r_image.show()
# 打印以下超参数
for key in args.__dict__:
if key.find('__') == -1:
offset = 20 - key.__len__()
print(key + ' ' * offset, args.__dict__[key])
# 使用那一块显卡
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
data_path_df = pd.read_csv('dataset/path_list.csv')
data_path_df = data_path_df.sample(frac=1) # 第一次打乱
dataset = DataSet(image_path=data_path_df['image'].values,
label_path=data_path_df['label'].values)
model = Pspnet(batch_norm_decay=args.batch_norm_decay)
image = tf.placeholder(tf.float32, [None, 1024, 1024, 3], name='input_x')
label = tf.placeholder(tf.int32, [None, 1024, 1024])
lr = tf.placeholder(tf.float32, )
logits = model.forward_pass(image)
logits_prob = tf.nn.softmax(logits=logits, name='logits_prob')
predicts = tf.argmax(logits, axis=-1, name='predicts')
variables_to_restore = tf.trainable_variables(scope='resnet_v2_50')
# finetune resnet_v2_50的参数(block1到block4)
restorer = tf.train.Saver(variables_to_restore)
# cross_entropy
cross_entropy = tf.reduce_mean(
#-------------------------------------#
# 调用摄像头或者视频进行检测
# 调用摄像头直接运行即可
# 调用视频可以将cv2.VideoCapture()指定路径
# 视频的保存并不难，可以百度一下看看
#-------------------------------------#
import time
import cv2
import numpy as np
from PIL import Image
from pspnet import Pspnet
pspnet = Pspnet()
#-------------------------------------#
# 调用摄像头
# capture=cv2.VideoCapture("1.mp4")
#-------------------------------------#
capture = cv2.VideoCapture(0)
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref, frame = capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
# 进行检测
def configure_networks_single(self):
#—————————————— step：1 ——————————————#
# 设置X\Y的容器；把标签转换成one-hot形式，为了下一步计算loss时使用；
self.inputs = tf.placeholder(tf.float32,
self.input_shape,
name='inputs')
self.annotations = tf.placeholder(tf.int64,
self.output_shape,
name='annotations')
self.is_train = tf.placeholder(tf.bool, name='is_train')
expand_annotations = tf.expand_dims(self.annotations,
-1,
name='annotations/expand_dims')
one_hot_annotations = tf.squeeze(expand_annotations,
axis=[self.channel_axis],
name='annotations/squeeze')
one_hot_annotations = tf.one_hot(one_hot_annotations,
depth=self.conf.class_num,
axis=self.channel_axis,
name='annotations/one_hot')
#—————————————— step：2 ——————————————#
# 根据搭建的模型计算预测出来的Y；除了预测值可能还包括一些其他想输出的参数；
if self.conf.network_name == "ascnet":
model = Ascnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(self.inputs)
if self.conf.network_name == "segnet":
model = Segnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(self.inputs)
if self.conf.network_name == "deeplabv3":
model = Deeplabv3(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(self.inputs)
if self.conf.network_name == "deeplabv3plus":
model = Deeplabv3plus(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(self.inputs)
if self.conf.network_name == "unet":
model = Unet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(self.inputs)
if self.conf.network_name == "pspnet":
model = Pspnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates, self.pred2 = model.inference(
self.inputs)
#—————————————— step：3 ——————————————#
# 根据预测值和one-hot的标签计算loss，选择softmax_cross_entropy损失函数；
# Camvid 数据集的weight
#weights = [0.01,0.007,0.161,0.007,0.02,0.016,0.18, 0.15,0.04,0.29,1.0,0.04]
#weights = tf.convert_to_tensor(weights) #将list转成tensor, shape为[50, ]
#weights = tf.reduce_sum(tf.multiply(one_hot_annotations, weights), -1, name='loss/weights')
# 采用有weights的loss
#losses = tf.losses.softmax_cross_entropy(one_hot_annotations, self.predictions, weights=weights, scope='loss/losses')
# 采用普通的loss
losses = tf.losses.softmax_cross_entropy(one_hot_annotations,
self.predictions,
scope='loss/losses')
self.loss_op = tf.reduce_mean(losses, name='loss/loss_op')
# PSPnet有特殊的辅助loss
if self.conf.network_name == "pspnet":
# 采用有weights的loss
#losses2 = tf.losses.softmax_cross_entropy(one_hot_annotations, self.pred2, weights=weights, scope='loss/losses2')
# 采用普通的loss
losses2 = tf.losses.softmax_cross_entropy(one_hot_annotations,
self.pred2,
scope='loss/losses2')
self.loss_op2 = tf.reduce_mean(losses2, name='loss/loss_op2')
self.loss_op = self.loss_op + self.loss_op2 * 0.4
#—————————————— step：4 ——————————————#
# 选择优化器和学习率，设置训练使用的 train_op
optimizer = tf.train.AdamOptimizer(
learning_rate=self.conf.learning_rate,
beta1=self.conf.beta1,
beta2=self.conf.beta2,
epsilon=self.conf.epsilon)
# 添加一些需要训练的变量作为train_op依赖项，主要是为了使用BN
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.train_op = optimizer.minimize(self.loss_op, name='train_op')
#—————————————— step：5 ——————————————#
# 计算三个评价指标：accuracy、miou、dice，因为没有直接计算dice的函数，
# 所以保存的是预测值和标签，在验证时再用他们计算dice
# 1）计算accuracy
self.decoded_predictions = tf.argmax(self.predictions,
self.channel_axis,
name='accuracy/decode_pred')
correct_prediction = tf.equal(self.annotations,
self.decoded_predictions,
name='accuracy/correct_pred')
self.accuracy_op = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32,
name='accuracy/cast'),
name='accuracy/accuracy_op')
# 2）计算miou
weights = tf.cast(tf.greater(self.decoded_predictions,
0,
name='m_iou/greater'),
tf.int32,
name='m_iou/weights')
self.m_iou, self.miou_op = tf.metrics.mean_iou(
self.annotations,
self.decoded_predictions,
self.conf.class_num,
weights,
name='m_iou/m_ious')
# 3）计算dice----保存需要用的gt和out
self.out = tf.cast(self.decoded_predictions, tf.float32)
self.gt = tf.cast(self.annotations, tf.float32)
#—————————————— step：6 ——————————————#
# 初始化全局变量，这一步需要在session运行训练之前做
tf.set_random_seed(self.conf.random_seed)
self.sess.run(tf.global_variables_initializer())
#—————————————— step：7 ——————————————#
# 用于保存模型和summary
# 保存BN中不可训练的参数，自己去找那些参数
trainable_vars = tf.trainable_variables() #可训练的参数
g_list = tf.global_variables()
bn_moving_vars = [
g for g in g_list if 'batch_norm/moving_mean' in g.name
]
bn_moving_vars += [
g for g in g_list if 'batch_norm/moving_variance' in g.name
]
trainable_vars += bn_moving_vars
self.saver = tf.train.Saver(var_list=trainable_vars, max_to_keep=0)
self.writer = tf.summary.FileWriter(self.conf.logdir, self.sess.graph)
def configure_networks_multi(self):
#—————————————— step：1 ——————————————#
# 设置X\Y的容器；把标签转换成one-hot形式，为了下一步计算loss时使用；
self.inputs = tf.placeholder(tf.float32,
self.input_shape,
name='inputs')
self.annotations = tf.placeholder(tf.int64,
self.output_shape,
name='annotations')
self.is_train = tf.placeholder(tf.bool, name='is_train')
expand_annotations = tf.expand_dims(self.annotations,
-1,
name='annotations/expand_dims')
one_hot_annotations = tf.squeeze(expand_annotations,
axis=[self.channel_axis],
name='annotations/squeeze')
one_hot_annotations = tf.one_hot(one_hot_annotations,
depth=self.conf.class_num,
axis=self.channel_axis,
name='annotations/one_hot')
#—————————————— step：2 ——————————————#
# 利用list记录每个GPU上的指标，然后concat成一个batch后再计算；
tower_grads = []
tower_predictions = []
tower_rate = []
#—————————————— step：3 ——————————————#——设置优化器———#
optimizer = tf.train.AdamOptimizer(
learning_rate=self.conf.learning_rate,
beta1=self.conf.beta1,
beta2=self.conf.beta2,
epsilon=self.conf.epsilon)
#—————————————— step：4 ——————————————#——多个GPU的计算———#
# tf.variable_scope 作用：指定变量的作用域，用于变量共享
with tf.variable_scope(tf.get_variable_scope()):
for i in range(self.conf.gpu_num):
print("this is %d gpu" % i)
with tf.device("/gpu:%d" % i):
with tf.name_scope("tower_%d" % i):
# 拆分数据给每个GPU；并把标签转换成one-hot形式，为了下一步计算loss时使用；
self.x = self.inputs[i * self.conf.batch:(i + 1) *
self.conf.batch]
self.y = self.annotations[i * self.conf.batch:(i + 1) *
self.conf.batch]
expand_y = tf.expand_dims(self.y,
-1,
name='y/expand_dims')
one_hot_y = tf.squeeze(expand_y,
axis=[self.channel_axis],
name='y/squeeze')
one_hot_y = tf.one_hot(one_hot_y,
depth=self.conf.class_num,
axis=self.channel_axis,
name='y/one_hot')
# 计算预测出来的Y
if self.conf.network_name == "ascnet":
model = Ascnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(
self.inputs)
if self.conf.network_name == "segnet":
model = Segnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(
self.inputs)
if self.conf.network_name == "deeplabv3":
model = Deeplabv3(self.sess, self.conf,
self.is_train)
self.predictions, self.rates = model.inference(
self.inputs)
if self.conf.network_name == "deeplabv3plus":
model = Deeplabv3plus(self.sess, self.conf,
self.is_train)
self.predictions, self.rates = model.inference(
self.inputs)
if self.conf.network_name == "unet":
model = Unet(self.sess, self.conf, self.is_train)
self.predictions, self.rates = model.inference(
self.inputs)
if self.conf.network_name == "pspnet":
model = Pspnet(self.sess, self.conf, self.is_train)
self.predictions, self.rates, self.pred2 = model.inference(
self.inputs)
# 计算loss
# Camvid 数据集的weight
#weights = [0.01,0.007,0.161,0.007,0.02,0.016,0.18, 0.15,0.04,0.29,1.0,0.04]
#weights = tf.convert_to_tensor(weights) #将list转成tensor, shape为[50, ]
#weights = tf.reduce_sum(tf.multiply(one_hot_annotations, weights), -1, name='loss/weights')
# 采用有weights的loss
#losses = tf.losses.softmax_cross_entropy(one_hot_y, prediction, weights=weights, scope='loss/losses')
# 采用普通的loss
losses = tf.losses.softmax_cross_entropy(
one_hot_y, prediction, scope='loss/losses')
loss_each = tf.reduce_mean(losses,
name='loss/loss_each')
if self.conf.network_name == "pspnet":
# 采用有weights的loss
#losses2 = tf.losses.softmax_cross_entropy(one_hot_annotations, self.pred2, weights=weights, scope='loss/losses2')
# 采用普通的loss
losses2 = tf.losses.softmax_cross_entropy(
one_hot_annotations,
self.pred2,
scope='loss/losses2')
loss_each2 = tf.reduce_mean(losses2,
name='loss/loss_op2')
loss_each = loss_each + loss_each2 * 0.4
# 共享变量：在第一次声明变量之后，将控制变量重用的参数设置为True，这样可以让不同的GPU更新同一组参数
# 注意tf.name_scope函数并不会影响tf.get_variable的命名空间，它只影响tf.variable的
tf.get_variable_scope().reuse_variables()
# 计算梯度；并保存当前GPU上的指标；
grads = optimizer.compute_gradients(loss_each)
tower_grads.append(grads)
tower_predictions.append(prediction)
tower_rate.append(rate)
#—————————————— step：5 ——————————————#
# 计算平均梯度；并设置训练OP
grads = self.average_gradients(tower_grads)
# 添加一些需要训练的变量作为train_op依赖项
# optimizer.apply_gradients作用：在计算完梯度后，最小化梯度的操作，相当于optimizer.minimize的第二步
# 添加一些需要训练的变量作为train_op依赖项，主要是为了使用BN
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.train_op = optimizer.apply_gradients(grads, name='train_op')
#—————————————— step：6 ——————————————#
# 计算评价指标
# 1）计算self.predictions 和 self.rates
for i in range(self.conf.gpu_num):
if i == 0:
preds = tower_predictions[i]
r = tower_rate[i]
else:
preds = tf.concat([preds, tower_predictions[i]],
self.batch_axis,
name='preds/concat' + str(i))
r = tf.concat([r, tower_rate[i]],
self.batch_axis,
name='r/concat' + str(i))
self.predictions = preds
self.rates = r
# 2）计算loss
loss_merge = tf.losses.softmax_cross_entropy(one_hot_annotations,
self.predictions,
scope='loss/loss_merge')
self.loss_op = tf.reduce_mean(loss_merge, name='loss/loss_op')
# 3）计算accuracy
self.decoded_predictions = tf.argmax(self.predictions,
self.channel_axis,
name='accuracy/decode_pred')
correct_prediction = tf.equal(self.annotations,
self.decoded_predictions,
name='accuracy/correct_pred')
self.accuracy_op = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32,
name='accuracy/cast'),
name='accuracy/accuracy_op')
# 4）计算miou
weights = tf.cast(tf.greater(self.decoded_predictions,
0,
name='m_iou/greater'),
tf.int32,
name='m_iou/weights')
self.m_iou, self.miou_op = tf.metrics.mean_iou(
self.annotations,
self.decoded_predictions,
self.conf.class_num,
weights,
name='m_iou/m_ious')
# 5）计算dice
self.out = self.decoded_predictions
self.gt = self.annotations
#—————————————— step：7 ——————————————#——初始化全局变量———#
tf.set_random_seed(self.conf.random_seed)
self.sess.run(tf.global_variables_initializer())
for v in tf.global_variables():
print(v.name)
#—————————————— step：8 ——————————————#
# 用于保存模型和summary
# 保存BN中不可训练的参数，自己去找那些参数
trainable_vars = tf.trainable_variables() #可训练的参数
g_list = tf.global_variables()
bn_moving_vars = [
g for g in g_list if 'batch_norm/moving_mean' in g.name
]
bn_moving_vars += [
g for g in g_list if 'batch_norm/moving_variance' in g.name
]
trainable_vars += bn_moving_vars
self.saver = tf.train.Saver(var_list=trainable_vars, max_to_keep=0)
self.writer = tf.summary.FileWriter(self.conf.logdir, self.sess.graph)
#-------------------------------------------------------#
VOCdevkit_path = 'VOCdevkit'
image_ids = open(
os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),
'r').read().splitlines()
gt_dir = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
miou_out_path = "miou_out"
pred_dir = os.path.join(miou_out_path, 'detection-results')
if miou_mode == 0 or miou_mode == 1:
if not os.path.exists(pred_dir):
os.makedirs(pred_dir)
print("Load model.")
pspnet = Pspnet()
print("Load model done.")
print("Get predict result.")
for image_id in tqdm(image_ids):
image_path = os.path.join(
VOCdevkit_path, "VOC2007/JPEGImages/" + image_id + ".jpg")
image = Image.open(image_path)
image = pspnet.get_miou_png(image)
image.save(os.path.join(pred_dir, image_id + ".png"))
print("Get predict result done.")
if miou_mode == 0 or miou_mode == 2:
print("Get miou.")
hist, IoUs, PA_Recall, Precision = compute_mIoU(
gt_dir, pred_dir, image_ids, num_classes,
# 将单张图片预测、摄像头检测和FPS测试功能
# 整合到了一个py文件中，通过指定mode进行模式的修改。
#----------------------------------------------------#
import time
import cv2
import numpy as np
from PIL import Image
from pspnet import Pspnet
if __name__ == "__main__":
#-------------------------------------------------------------------------#
# 如果想要修改对应种类的颜色，到__init__函数里修改self.colors即可
#-------------------------------------------------------------------------#
pspnet = Pspnet()
#----------------------------------------------------------------------------------------------------------#
# mode用于指定测试的模式：
# 'predict'表示单张图片预测，如果想对预测过程进行修改，如保存图片，截取对象等，可以先看下方详细的注释
# 'video'表示视频检测，可调用摄像头或者视频进行检测，详情查看下方注释。
# 'fps'表示测试fps，使用的图片是img里面的street.jpg，详情查看下方注释。
# 'dir_predict'表示遍历文件夹进行检测并保存。默认遍历img文件夹，保存img_out文件夹，详情查看下方注释。
#----------------------------------------------------------------------------------------------------------#
mode = "predict"
#----------------------------------------------------------------------------------------------------------#
# video_path用于指定视频的路径，当video_path=0时表示检测摄像头
# 想要检测视频，则设置如video_path = "xxx.mp4"即可，代表读取出根目录下的xxx.mp4文件。
# video_save_path表示视频保存的路径，当video_save_path=""时表示不保存
# 想要保存视频，则设置如video_save_path = "yyy.mp4"即可，代表保存为根目录下的yyy.mp4文件。
# video_fps用于保存的视频的fps
# video_path、video_save_path和video_fps仅在mode='video'时有效