数据压缩：视觉数据压缩感知技术在存储优化中的应用

import numpy as np
from sklearn.linear_model import Lasso

def compress_image(image, alpha=0.1):
    ""“使用Lasso回归实现图像的稀疏表示”""
    # 将图像转换为列向量
    X = image.reshape((-1, 1))
    # 创建一个线性回归模型
    model = Lasso(alpha=alpha, max_iter=1000)
    # 训练模型找到稀疏表示
    model.fit(X, X.ravel())
    # 返回稀疏系数
    return model.coef_

def reconstruct_image(sparse_coef, image_shape):
    ""“从稀疏表示重构图像”""
    # 从稀疏系数重构图像
    return (sparse_coef.reshape(image_shape) + image.min()) * (image.max() - image.min()) / (image.max() - image.min())

# 加载图像
image = cv2.imread('example.jpg', cv2.IMREAD_GRAYSCALE)

# 压缩图像
sparse_coef = compress_image(image)

# 重构图像
reconstructed_image = reconstruct_image(sparse_coef, image.shape)

# 显示原始和重构后的图像
cv2.imshow('Original Image', image)
cv2.imshow('Reconstructed Image', reconstructed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

class VideoCompressor:
    def __init__(self, model):
        self.model = model

    def compress_frame(self, frame):
        # 特征提取
        features = self.model.feature_extraction(frame)
        # 稀疏编码
        sparse_features = self.model.sparse Coding(features)
        # 量化
        quantized_features = self.model.quantize(sparse_features)
        # 编码
        encoded_data = self.model.encode(quantized_features)
        return encoded_data

    def decompress_frame(self, encoded_data):
        # 解码
        decoded_features = self.model.decode(encoded_data)
        # 重构
        reconstructed_frame = self.model.reconstruct(decoded_features)
        return reconstructed_frame

# 假设 model 是一个预先训练好的压缩感知模型
compressor = VideoCompressor(model)

# 读取视频流
cap = cv2.VideoCapture('video_stream.mp4')

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 压缩帧
    encoded_frame = compressor.compress_frame(frame)
    # 解压缩帧
    decompressed_frame = compressor.decompress_frame(encoded_frame)

    # 显示原始和解压缩后的帧
    cv2.imshow('Original Frame', frame)
    cv2.imshow('Decompressed Frame', decompressed_frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, TimeDistributed, Reshape

# 实时视频捕获
cap = cv2.VideoCapture(0)

# 定义压缩模型
class VideoCompressor:
    def __init__(self):
        self.model = self.build_model()
    
    def build_model(self):
        model = Sequential()
        model.add(TimeDistributed(Conv2D(32, (3, 3), activation='relu'), input_shape=(None, None, 3)))
        model.add(TimeDistributed(MaxPooling2D((2, 2))))
        model.add(TimeDistributed(Flatten()))
        model.add(TimeDistributed(Dense(128, activation='relu')))
        model.add(TimeDistributed(Dense(64, activation='relu')))
        model.add(TimeDistributed(Dense(3, activation='sigmoid')))
        model.compile(optimizer='adam', loss='mse')
        return model

    def compress(self, frame):
        # 将帧转换为模型可接受的格式
        input_data = self.preprocess_frame(frame)
        # 使用模型进行压缩
        compressed_data = self.model.predict(input_data)
        # 转换回图像格式
        return self.postprocess_frame(compressed_data)
    
    def preprocess_frame(self, frame):
        # 转换颜色空间，归一化等操作
        return frame / 255.0
    
    def postprocess_frame(self, compressed_data):
        # 反归一化，转换颜色空间等操作
        return (compressed_data * 255).astype(np.uint8)

# 实例化压缩器
compressor = VideoCompressor()
# 加载模型权重，假设已经训练完成
compressor.model.load_weights('video_compressor_weights.h5')

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # 压缩帧
    compressed_frame = compressor.compress(frame)
    
    # 显示原始和压缩后的帧
    cv2.imshow('Original Frame', frame)
    cv2.imshow('Compressed Frame', compressed_frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2

# 加载预训练的MobileNetV2模型
model = MobileNetV2(weights='imagenet', include_top=False)

# 定义压缩感知的编码器和解码器
class Compressor(tf.keras.Model):
    def __init__(self):
        super(Compressor, self).__init__()
        self.encoder = model
        self.decoder = tf.keras.Sequential([
            tf.keras.layers.Dense(units=128, activation='relu'),
            tf.keras.layers.Dense(units=64, activation='relu'),
            tf.keras.layers.Dense(units=3, activation='sigmoid')
        ])
    
    def call(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

# 实例化压缩器并进行训练
compressor = Compressor()
# 假设 train_images 是训练用的图像数据
compressor.compile(optimizer='adam', loss='mse')
compressor.fit(train_images, train_images, epochs=10)