如何将rgb掩码转换为分类掩码

Question

我试图找到一个适当的解决方案，从"cam视频"数据集到分类掩码

我有rgb值的列表和相应的标签。在keras中生成分类标签的正确方法是什么?

A "cam "数据集看起来像这样:

.
├── default
│   └── 000048-NG.jpg
├── defaultannot
│   └── 000048-NG.png
├── default.txt
└── label_colors.txt
2 directories, 4 files

Answer 1

我读了你的问题，把你的问题分为两个要求

1. 从录制相机，您需要将输出转换为具有类别和
的数据集
2. 生成分类标签

这取决于你的模型学习和对输入和目标的假设，当你可以使用监督学习、自监督学习或无监督学习时。

[示例]:监督学习返回标签的快速示例。

import cv2
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tensorflow as tf
import os
from os.path import exists
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
fig = plt.figure()
image = plt.imread( "F:\datasets\downloads\cats_name\train\Symbols\01.jpg" )
im = plt.imshow( image )
list_actual_label = [ 'Shoes', 'Duck' ]
global video_capture_0
video_capture_0 = cv2.VideoCapture(0)
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\loggings.log"
if not exists(checkpoint_dir) : 
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def f1( picture ):
return tf.constant( picture ).numpy()
def animate( i ):
ret0, frame0 = video_capture_0.read()
if (ret0):      

frame0 = tf.image.resize(frame0, [29, 39]).numpy()

temp = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,2:3])
temp2 = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,1:2])
temp3 = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,0:1])
temp = tf.keras.layers.Concatenate(axis=2)([temp, temp2])
temp = tf.keras.layers.Concatenate(axis=2)([temp, temp3])
# 480, 640
temp = tf.keras.preprocessing.image.array_to_img(
temp,
data_format=None,
scale=True
)
temp = f1( temp )

im.set_array( temp )
result = predict_action( temp )
print( list_actual_label[result] )

return im,
def predict_action ( image ) :
predictions = model.predict(tf.constant(image, shape=(1, 29, 39, 3) , dtype=tf.float32))
result = tf.math.argmax(predictions[0])
return result
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 29, 39, 3 )),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Reshape((234, 32)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
])

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64))
model.add(tf.keras.layers.Dense(2))
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
list_picture = []
list_label = []
path_1 = "F:\datasets\downloads\Duck_Shoe\Duck\"
path_2 = "F:\datasets\downloads\Duck_Shoe\Shoes\"
for file in os.listdir( path_1 ):
image = plt.imread( path_1 + file )
image = tf.image.resize(image, [29, 39]).numpy()
for i in range ( 40 ) :
if i % 6 == 0 :
layer = tf.keras.layers.RandomZoom(.5, .2)
image = layer( image ).numpy()
list_picture.append( image )
elif i % 5 == 0 :
image = tf.image.random_hue(image, 0.2).numpy()
image = tf.image.random_flip_up_down(image, 1).numpy()
list_picture.append( image )
elif i % 4 == 0 :
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
image = tf.image.random_flip_left_right(image, 1).numpy()
list_picture.append( image )
elif i % 3 == 0 :
image = tf.image.random_flip_up_down(image, 1).numpy()
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
list_picture.append( image )
elif i % 2 == 0 :
image = tf.image.random_flip_left_right(image, 1).numpy()
image = tf.image.random_hue(image, 0.2).numpy()
list_picture.append( image )
else :
list_picture.append( image )

list_label.append( 1 )
for file in os.listdir( path_2 ):
image = plt.imread( path_2 + file )
image = tf.image.resize(image, [29, 39]).numpy()

for i in range ( 40 ) :
if i % 6 == 0 :
layer = tf.keras.layers.RandomZoom(.5, .2)
image = layer( image ).numpy()
list_picture.append( image )
elif i % 5 == 0 :
image = tf.image.random_hue(image, 0.2).numpy()
image = tf.image.random_flip_up_down(image, 1).numpy()
list_picture.append( image )
elif i % 4 == 0 :
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
image = tf.image.random_flip_left_right(image, 1).numpy()
list_picture.append( image )
elif i % 3 == 0 :
image = tf.image.random_flip_up_down(image, 1).numpy()
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
list_picture.append( image )
elif i % 2 == 0 :
image = tf.image.random_flip_left_right(image, 1).numpy()
image = tf.image.random_hue(image, 0.2).numpy()
list_picture.append( image )
else :
list_picture.append( image )

list_label.append( 0 )
dataset = tf.data.Dataset.from_tensor_slices((tf.constant([list_picture], shape=(len(list_picture), 1, 29, 39, 3), dtype=tf.float32),tf.constant([list_label], shape=(len(list_picture), 1, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam( learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name='Nadam' )
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.MeanSquaredLogarithmicError(reduction=tf.keras.losses.Reduction.AUTO, name='mean_squared_logarithmic_error')
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(dataset, epochs=2 ,validation_data=(dataset))
model.save_weights(checkpoint_path)
while True:
ani = animation.FuncAnimation(fig, animate, interval=50, blit=True)
plt.show()

# When everything is done, release the capture
video_capture_0.release()
cv2.destroyAllWindows()

input('...')