building_with_espdl-1-supplement

因为模型优化和量化部分需要X_cal.pkl和y_cal.pkl文件，而在第一部分训练模型时未生成，因此新建一个项目生成pkl。

初版

import tensorflow as tf
import numpy as np
import pickle

# 设置图片的尺寸和批量大小
img_height, img_width = 96, 96
batch_size = 16

# 数据集根目录
data_dir = "./leapGestRecog"  # 请替换为你的实际路径

# 加载和预处理数据集
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

# 创建用于保存数据的列表
X_cal = []
y_cal = []

# 遍历训练集，将图像和标签存储到列表中
for images, labels in train_ds:
    X_cal.append(images.numpy())
    y_cal.append(labels.numpy())

# 遍历验证集，将图像和标签存储到列表中
for images, labels in val_ds:
    X_cal.append(images.numpy())
    y_cal.append(labels.numpy())

# 将列表转换为 NumPy 数组
X_cal = np.concatenate(X_cal)
y_cal = np.concatenate(y_cal)

# 保存为 pickle 文件
with open('X_cal.pkl', 'wb') as f:
    pickle.dump(X_cal, f)

with open('y_cal.pkl', 'wb') as f:
    pickle.dump(y_cal, f)

print("X_cal.pkl 和 y_cal.pkl 已成功创建。")

次版

1

原因同模型训练部分，因此修改3通道改为1通道。

import tensorflow as tf
import numpy as np
import pickle

# 设置图片的尺寸和批量大小
img_height, img_width = 96, 96
batch_size = 16

# 数据集根目录
data_dir = "./leapGestRecog"  # 请替换为你的实际路径

# 加载和预处理数据集
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

# 转换为灰度图并归一化
def normalize_img(image, label):
    image = tf.image.rgb_to_grayscale(image)  # 转换为灰度
    image = tf.cast(image, tf.float32) / 255.0  # 归一化到 [0, 1]
    return image, label

# 应用转换函数到数据集
train_ds = train_ds.map(normalize_img)
val_ds = val_ds.map(normalize_img)

# 创建用于保存数据的列表
X_cal = []
y_cal = []

# 遍历训练集，将图像和标签存储到列表中
for images, labels in train_ds:
    X_cal.append(images.numpy())
    y_cal.append(labels.numpy())

# 遍历验证集，将图像和标签存储到列表中
for images, labels in val_ds:
    X_cal.append(images.numpy())
    y_cal.append(labels.numpy())

# 将列表转换为 NumPy 数组
X_cal = np.concatenate(X_cal)
y_cal = np.concatenate(y_cal)

# 保存为 pickle 文件
with open('X_cal.pkl', 'wb') as f:
    pickle.dump(X_cal, f)

with open('y_cal.pkl', 'wb') as f:
    pickle.dump(y_cal, f)

print("X_cal.pkl 和 y_cal.pkl 已成功创建。")

2

代码

手动遍历文件系统以加载数据，仅选择前 6 个手势类别。

import os
import cv2
import numpy as np
import pickle

# 设置数据集目录和参数
data_dir = "./leapGestRecog"  # 请替换为你的实际路径
img_height, img_width = 96, 96

# 定义需要加载的手势类别（仅加载前6类）
selected_gestures = ["01_palm", "02_l", "03_fist", "04_fist_moved", "05_thumb", "06_index"]

# 初始化数据和标签列表
X_cal = []
y_cal = []

# 遍历每个顶层文件夹（例如 00, 01...）
for subdir in os.listdir(data_dir):
    subdir_path = os.path.join(data_dir, subdir)
    
    # 确保顶层文件夹是目录
    if not os.path.isdir(subdir_path):
        continue
    
    # 仅遍历前六个手势类别文件夹
    for idx, gesture in enumerate(selected_gestures):
        gesture_path = os.path.join(subdir_path, gesture)
        
        # 确保手势文件夹存在
        if not os.path.isdir(gesture_path):
            continue
        
        # 遍历每张图像文件
        for img_file in os.listdir(gesture_path):
            img_path = os.path.join(gesture_path, img_file)
            
            # 读取图像，转换为灰度，并调整大小
            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
            img_resized = cv2.resize(img, (img_width, img_height))
            
            # 归一化图像数据
            img_normalized = img_resized / 255.0
            X_cal.append(img_normalized)
            y_cal.append(idx)  # 使用手势的索引作为标签

# 转换为 NumPy 数组，并调整维度
X_cal = np.array(X_cal).reshape(-1, img_height, img_width, 1)
y_cal = np.array(y_cal)

# 保存为 pickle 文件
with open('X_cal.pkl', 'wb') as f:
    pickle.dump(X_cal, f)

with open('y_cal.pkl', 'wb') as f:
    pickle.dump(y_cal, f)

print("X_cal.pkl 和 y_cal.pkl 已成功创建，包含前6个手势类别的数据。")

输出

X_cal.pkl 和 y_cal.pkl 已成功创建，包含前6个手势类别的数据。