本來(lái)計(jì)劃使用 ART-Pi Smart 進(jìn)行車(chē)標(biāo)識(shí)別的，但是現(xiàn)在實(shí)際測(cè)試發(fā)現(xiàn)攝像頭采集的數(shù)據(jù)顯示有大概率抖動(dòng)的現(xiàn)象發(fā)生，所以實(shí)現(xiàn)了將攝像頭采集的數(shù)據(jù)以 bmp 圖片格式存儲(chǔ)，然后發(fā)送到電腦端使用 tflite 格式的模型數(shù)據(jù)進(jìn)行測(cè)試。

第一部分，系統(tǒng)概述

該項(xiàng)目實(shí)現(xiàn)了在 ART-Pi Smart 上通過(guò) OV5640 攝像頭采集視頻數(shù)據(jù)，并以 bmp 格式圖片保存在設(shè)備端，然后借助 ART-Pi Smart webserver 服務(wù)將生成的 bmp 圖片下載到 PC，然后在 PC 端使用訓(xùn)練的 tflite 模型對(duì)下載的 bmp 圖片進(jìn)行預(yù)測(cè)輸出結(jié)果。

硬件框圖：

軟件框圖：

第二部分，系統(tǒng)流程

為了實(shí)現(xiàn)預(yù)期的目的，將系統(tǒng)流程分為如下幾個(gè)階段：

1、使用官方 demo 移植 tflite 到 ART-Pi Smart, 針對(duì)這部分內(nèi)容可以查看文章?在 ART-Pi Smart 上運(yùn)行 TensorFlow Lite

2、移植 TensorFLow 基礎(chǔ)的圖像分類(lèi)?mnist?的 demo 到 ART-Pi Smart，針對(duì)這部分內(nèi)容可以查看文章

3、使用 Kicad 設(shè)計(jì) ov5640 轉(zhuǎn)結(jié)板，并開(kāi)發(fā) ov5640 攝像頭驅(qū)動(dòng)，在開(kāi)發(fā)這部分軟件的過(guò)程中，遇到了問(wèn)題，具體細(xì)節(jié)可以查看問(wèn)題ov5640 顯示效果不穩(wěn)定

4、使用攝像頭采集數(shù)據(jù)判斷是否有車(chē)輛信息

本節(jié)重點(diǎn)描述系統(tǒng)流程的第 4 部分，訓(xùn)練自己的模型檢測(cè)是否有車(chē)輛信息，這部分主要分為如下步驟：

1、從網(wǎng)上下載包含有車(chē)輛的圖片數(shù)據(jù)集和沒(méi)有車(chē)輛的數(shù)據(jù)集合(這部分內(nèi)容，我選擇了一些包含有花朵的數(shù)據(jù)集)，通過(guò) python 將其批量轉(zhuǎn)換為屏幕分辨率的圖片，這部分代碼大概是這樣的：

!/usr/bin/env python

源數(shù)據(jù)的目錄

如果指定有源文件目錄，使用指定的源文件目錄

生成轉(zhuǎn)換后的源文件的目錄

最多轉(zhuǎn)換 1000 張圖片

修改原始圖片的分辨率

for i in range(src_image_len):
current_image_name = src_image_lists[i]
full_current_image_name = os.path.join(WEB_PICS, current_image_name)

target_image_name = current_image_name.split('.')[0]+'_convert.bmp'

full_target_image_name = os.path.join(TAREGT_PICS, target_image_name)

raw_image =Image.open(full_current_image_name)

image_L = raw_image.convert('L')

image = image_L.resize((480,272))

image.save(full_target_image_name)

print(np.array(image, dtype=np.float32, order=’C’).shape)

exit()

如圖所示，四個(gè)目錄分別是原始帶有車(chē)輛和沒(méi)有車(chē)輛的數(shù)據(jù)集目錄和轉(zhuǎn)換后的數(shù)據(jù)集目錄。

2.標(biāo)定帶有車(chē)輛的圖片和沒(méi)有車(chē)輛的圖片信息，使用 tensorflow 進(jìn)行訓(xùn)練，這部分代碼大概是這樣的：

``` python

#!/usr/bin/env python

# 車(chē)標(biāo)分類(lèi)算法

from PIL import Image

import os

MODELS_DIR = 'models/'

if not os.path.exists(MODELS_DIR):

os.mkdir(MODELS_DIR)

MODEL_TF = MODELS_DIR + 'model'

MODEL_NO_QUANT_TFLITE = MODELS_DIR + 'model_no_quant.tflite'

MODEL_TFLITE = MODELS_DIR + 'model.tflite'

MODEL_TFLITE_MICRO = MODELS_DIR + 'model.cc'

import tensorflow as tf

from tensorflow import keras

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

TRAIN_IMAGES_COUNTS = 1000

whole_train_images = np.ones((2 * TRAIN_IMAGES_COUNTS , 480, 272), dtype=np.float32)

whole_train_labels = np.ones((2 * TRAIN_IMAGES_COUNTS , ), dtype=np.float32)

#test_images = np.ones((2 * TEST_IMAGES_COUNTS , 480, 272), dtype=np.float32)

#test_labels = np.ones((2 * TEST_IMAGES_COUNTS , ), dtype=np.float32)

CAR_CONVERT_PICS = 'cars_train_convert_sets'

FLOWER_TAREGT_PICS = 'flowers_convert_sets'

# 矩陣行列交換

def transpose_2d(data):

# transposed = list(zip(*data))

# [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]

# 注意 zip 本身返回的數(shù)據(jù)類(lèi)型為 tuple 元組

# 其中符號(hào) * 號(hào)可以對(duì)元素進(jìn)行解壓或展開(kāi)

transposed = list(map(list, zip(*data)))

return transposed

whole_train_sets_index = 0

def merge_whole_train_sets(src, type):

global whole_train_sets_index

src_image_lists = os.listdir(src)

for i in range(TRAIN_IMAGES_COUNTS):

full_current_image_name = os.path.join(src, src_image_lists[i])

#print(full_current_image_name)

raw_image = Image.open(full_current_image_name)

#print(type(raw_image.convert('RGB')))

temp_whole_train_images = np.array(raw_image, dtype=np.float32)

whole_train_images[whole_train_sets_index] = np.array(transpose_2d(temp_whole_train_images), dtype=np.float32)

whole_train_labels[whole_train_sets_index] = type

whole_train_sets_index = whole_train_sets_index + 1

merge_whole_train_sets(CAR_CONVERT_PICS, 1)

merge_whole_train_sets(FLOWER_TAREGT_PICS, 0)

print(whole_train_labels)

print(type(whole_train_images), whole_train_images.shape)

print(type(whole_train_labels), whole_train_labels.shape)

train_images, test_images = train_test_split(whole_train_images, train_size=0.8, random_state=10)

train_labels, test_labels = train_test_split(whole_train_labels, train_size=0.8, random_state=10)

print(type(train_images), type(train_labels))

print(train_images.shape, train_labels.shape)

class_names = ['nocar', 'car']

if False:

plt.figure(figsize=(10,10))

for i in range(25):

plt.subplot(5,5,i+1)

plt.xticks([])

plt.yticks([])

plt.grid(False)

plt.imshow(train_images[i], cmap=plt.cm.binary)

plt.xlabel(class_names[train_labels[i]])

plt.show()

#exit()

#train_images = train_images

#test_images = test_images

#train_images = np.array(train_images, dtype=np.float32)

#plt.figure(figsize=(10,10))

#for i in range(25):

# plt.subplot(5,5,i+1)

# plt.xticks([])

# plt.yticks([])

# plt.grid(False)

# plt.imshow(train_images[i], cmap=plt.cm.binary)

# plt.xlabel(class_names[train_labels[i]])

#plt.show()

model = tf.keras.Sequential([

tf.keras.layers.Flatten(input_shape=(480, 272)),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dense(2)

])

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

metrics=['accuracy'])

# 如果模型已經(jīng)存在，加載整個(gè)模型

if os.path.exists(MODELS_DIR) and os.path.exists(MODEL_TF) and 0 != len(os.listdir(MODEL_TF)):

try:

#model.load_weights(MODEL_TF + 'saved_model.pb')

model = keras.models.load_model(MODEL_TF)

except:

print("load weights failed")

exit()

# 訓(xùn)練模型

else:

print(f"{MODEL_TF} no exist ???")

model.fit(train_images, train_labels, epochs=10)

# 查看 model 概要

print(model.summary())

# 測(cè)試模型

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print(' Test accuracy:', test_acc)

probability_model = tf.keras.Sequential([model,

tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

print(type(test_images), test_images.shape, " red")

print(predictions[0])

#exit()

# 測(cè)試自定義圖片

from PIL import Image

import sys

IMG_SRC = 'flowers/image_1111.jpg'

IMG_TARGET = IMG_SRC.split('/')[-1].split('.')[0] + '_gry_test' + '.bmp'

print(IMG_TARGET)

raw_image = Image.open(IMG_SRC)

image = raw_image.resize((480, 272))

image_gray = image.convert('L')

image_gray_array = np.array(image_gray, dtype=np.float32)

image_gray_array = np.array(transpose_2d(image_gray_array), dtype=np.float32).reshape(1,480,272)

#image_gray_array = image_gray_array / 255.0

image_gray.save(IMG_TARGET)

predictions = probability_model.predict(image_gray_array)

print("red prefictions")

print(predictions[0])

plt.figure()

plt.subplot(2,1,1)

plt.imshow(test_images[0], cmap=plt.cm.binary)

print(test_labels[0], "oh no red")

plt.subplot(2,1,2)

plt.imshow(raw_image, cmap=plt.cm.binary)

plt.show()

#exit()

#plt.figure(figsize=(10,10))

#plt.xticks([])

#plt.yticks([])

#plt.grid(False)

#plt.imshow(test_images[0], cmap=plt.cm.binary)

#plt.show()

# Save the model to disk

model.save(MODEL_TF)

# Convert the model to the TensorFlow Lite format without quantization

converter = tf.lite.TFLiteConverter.from_saved_model(MODEL_TF)

model_no_quant_tflite = converter.convert()

# Save the model to disk

open(MODEL_NO_QUANT_TFLITE, "wb").write(model_no_quant_tflite)

# Convert the model to the TensorFlow Lite format with quantization

def representative_dataset():

for i in range(500):

# 強(qiáng)制轉(zhuǎn)換為 float32 類(lèi)型

array = np.array(train_images[i], dtype=np.float32)

#print(array.shape, "red dbg")

#print(type(train_images[0].dtype), type(array[0].dtype), type(array), len(array))

yield([array])

# Set the optimization flag.

converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Enforce integer only quantization

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

converter.inference_input_type = tf.float32

converter.inference_output_type = tf.float32

# Provide a representative dataset to ensure we quantize correctly.

converter.representative_dataset = representative_dataset

# 轉(zhuǎn)換為 tflite 模型

model_tflite = converter.convert()

# Save the model to disk

open(MODEL_TFLITE, "wb").write(model_tflite)

該函數(shù)可以訓(xùn)練模型導(dǎo)出 model.tflite 到 models 目錄：

因?yàn)檩斎氲?tensor 有 480*272 個(gè)，所以導(dǎo)致模型特別巨大，發(fā)現(xiàn)直接編譯出的 rtthread.bin 有 16MB 左右，通過(guò) tftp 下載后無(wú)法正常運(yùn)行，就只能使用這個(gè)模型在 PC 端對(duì) ov5640 采集的圖片進(jìn)行識(shí)別了。

1、開(kāi)發(fā) save_bmp.elf 工具，支持將當(dāng)前顯存的內(nèi)容保存到 bmp 圖片中，這部分代碼大概這樣，下述 demo 簡(jiǎn)單完成了使用顯存內(nèi)容對(duì)原始 bmp 圖片的重寫(xiě)：

/*

* Copyright (c) 2006-2022, RT-Thread Development Team

*

* SPDX-License-Identifier: GPL-2.0

*

* Change Logs:

* Date Author Notes

* 2022-05-05 iysheng The first version

*/

#include

#include

#include"rtdef.h"

#include

#include"rt_lcd.h"

typedefunsignedcharuint8_t;

typedefunsignedshortuint16_t;

typedefunsignedintuint32_t;

typedefintint32_t;

#define LCD_WIDTH 480

#define LCD_HEIGHT 272

#define LCD_BUF_SIZE (LCD_WIDTH * LCD_HEIGHT)

rt_uint16_t pixel_ptr[LCD_BUF_SIZE];

rt_device_t lcd = NULL;

struct fb_fix_screeninfo f_info;

struct fb_var_screeninfo v_info;

int rt_smart_lcdinit(void)

{

rt_err_t ret =-1;

lcd = rt_device_find("lcd");

if(!lcd)return1;

ret = rt_device_open(lcd, RT_DEVICE_OFLAG_RDWR);

if(-1==ret)return1;

rt_device_control(lcd, FBIOGET_FSCREENINFO,&f_info);

printf("screen: %s - 0x%08x, size %d ", f_info.id,(unsignedint)f_info.smem_start, f_info.smem_len);

rt_device_control(lcd, FBIOGET_VSCREENINFO,&v_info);

printf("screen: bpp %d, width - %d, height - %d ", v_info.bits_per_pixel, v_info.xres, v_info.yres);

return ret;

}

#pragma pack(1)

struct bmp_header {

uint16_t file_type;// File type always BM which is 0x4D42

uint32_t file_size;// Size of the file (in bytes)

uint16_t reserved1;// Reserved, always 0

uint16_t reserved2;// Reserved, always 0

uint32_t offset_data;// Start position of pixel data (bytes from the beginning of the file)

};

struct windows_bmp_info_header {

uint32_t size;// Size of this header (in bytes)

int32_t width;// width of bitmap in pixels

int32_t height;// width of bitmap in pixels

uint16_t planes;// No. of planes for the target device, this is always 1

uint16_t bit_count;// No. of bits per pixel

uint32_t compression;// 0 or 3 - uncompressed. THIS PROGRAM CONSIDERS ONLY UNCOMPRESSED BMP images

uint32_t size_image;// 0 - for uncompressed images

int32_t x_pixels_per_meter;

int32_t y_pixels_per_meter;

uint32_t colors_used;// No. color indexes in the color table. Use 0 for the max number of colors allowed by bit_count

uint32_t colors_important;// No. of colors used for displaying the bitmap. If 0 all colors are required

};

#define RED_TFLITE_CONVERT 10

int get_bmp_at(struct fb_fix_screeninfo *finfo,struct fb_var_screeninfo *vinfo,struct windows_bmp_info_header *header,uint8_t*buffer,uint8_t bytes)

{

int32_t x, y, i =0;

uint16_t pix_tmp =0;

vinfo->xres =0;

vinfo->yres =0;

for(y = header->height; y >0; y--)

{

for(x =0; x < header->width; x++)

{

if(1== bytes)

{

buffer[i++]=*((uint16_t*)finfo->smem_start +(y -1+ vinfo->yres)* LCD_WIDTH + vinfo->xres + x);

}

elseif(2== bytes)

{

pix_tmp =*((uint16_t*)finfo->smem_start +(y -1+ vinfo->yres)* LCD_WIDTH + vinfo->xres + x);

buffer[i]= pix_tmp;

buffer[i +1]= pix_tmp >>8;

i +=2;

}

else

{

printf("error: no support this format ");

return-1;

}

}

}

/* TODO flush */

rt_device_control(lcd, RED_TFLITE_CONVERT, NULL);

return0;

}

void show_hex(uint8_t* buffer,uint16_t buffer_len,char*title)

{

uint16_t i =0;

printf("[%s](%hu)", title, buffer_len);

for(i =0; i < buffer_len; i++)

{

printf("%x ", buffer[i]);

}

printf(" ");

}

uint8_t bmp9696[480*272*2];

int main(int argc,char**argv)

{

FILE *fp;

/* 屏幕顯示起始地址 */

__attribute__((unused))int display_pos;

struct bmp_header t_bmp_header;

struct windows_bmp_info_header t_bmp_info_header;

int num;

if(0!= rt_smart_lcdinit())

{

printf("Failed init lcd. ");

return-3;

}

else

{

printf("init lcd ok ");

}

if(argc ==2)

{

printf("use default display width height and addr ");

}

elseif(argc ==3)

{

printf("ruse default display width height ");

}

elseif(argc ==4)

{

printf("use default display addr ");

}

elseif(argc ==5)

{

display_pos = atoi(argv[4]);

}

else

{

printf("Usage:show_bmp xxx.bmp 96 96 [pos]! ");

return-1;

}

fp = fopen(argv[1],"r+");

if(!fp)

{

printf("Failed open file:%s as:r+ ", argv[1]);

return-2;

}

num = fread(&t_bmp_header,1,sizeof(t_bmp_header), fp);

if(num !=sizeof(t_bmp_header))

{

printf("Read data len mismatch, please check:%d ", num);

return-3;

}

else

{

show_hex((uint8_t*)&t_bmp_header, num,"dbg_header");

printf("size=%u offset=%u ", t_bmp_header.file_size, t_bmp_header.offset_data);

}

display_pos = t_bmp_header.offset_data;

printf("size header:%u infoheader:%u ",sizeof(t_bmp_header),sizeof(t_bmp_info_header));

num = fread(&t_bmp_info_header,1,sizeof(t_bmp_info_header), fp);

if(num !=sizeof(t_bmp_info_header))

{

printf("Read data len mismatch, please check:%d ", num);

return-4;

}

else

{

printf("width=%u height=%u ", t_bmp_info_header.width, t_bmp_info_header.height);

}

/* seek offset positon */

fseek(fp, t_bmp_header.offset_data, SEEK_SET);

get_bmp_at(&f_info,&v_info,&t_bmp_info_header, bmp9696, t_bmp_info_header.bit_count /8);

num = fwrite(bmp9696,1, t_bmp_info_header.width * t_bmp_info_header.height * t_bmp_info_header.bit_count >>3, fp);

if(num != t_bmp_info_header.width * t_bmp_info_header.height * t_bmp_info_header.bit_count >>3)

{

printf("bmp raw data mismatch. ");

return-5;

}

else

{

printf("save bmp ok");

fclose(fp);

}

return0;

}

2、實(shí)驗(yàn)，我使用了一個(gè)卡車(chē)模型進(jìn)行采集，現(xiàn)場(chǎng)是這樣的：

采集回來(lái)的圖片是這樣的：

隱約可以從這個(gè)灰度圖中看出來(lái)卡車(chē)模型的輪廓，接下來(lái)使用 tflite 的模型對(duì)這個(gè)圖片進(jìn)行預(yù)測(cè)，結(jié)果可以看出來(lái)識(shí)別到這個(gè)圖片中包含有車(chē)：

為了做對(duì)比，再采集一張有花朵的圖片，進(jìn)行預(yù)測(cè)，現(xiàn)場(chǎng)是這樣的：

對(duì)攝像頭采集的圖片進(jìn)行捕捉，保存到一個(gè)灰度 bmp 圖片：

結(jié)果是：

可以看出正確識(shí)別除了是花還是汽車(chē)。

至此本次項(xiàng)目暫時(shí)告一段落了，通過(guò)這次試用，主要收獲有兩個(gè)方面：

1、通過(guò)實(shí)際對(duì)機(jī)器學(xué)習(xí)遷移到嵌入式設(shè)備端這個(gè)過(guò)程的接觸，對(duì)在邊緣節(jié)點(diǎn)進(jìn)行機(jī)器學(xué)習(xí)有了一個(gè)基礎(chǔ)的認(rèn)識(shí);

2、通過(guò)這次對(duì) ov5640 攝像頭的調(diào)試，對(duì) CSI 接口攝像頭的圖像采集以及顯示有了一個(gè)更深入的理解;

目前還有部分未完成的工作，對(duì)攝像頭采集的圖像效果不是特別滿(mǎn)意，目前分析可能是 ART-Pi Smart 到轉(zhuǎn)接板之間排線有點(diǎn)長(zhǎng)(我選的是10cm的)對(duì)顯示信號(hào)（有高頻 50MHz 附近的 PCLK 時(shí)鐘）有干擾，轉(zhuǎn)接板沒(méi)有處理好高頻信號(hào)導(dǎo)致的，看一下轉(zhuǎn)接板 PCB 和 3D 是這樣的。

審核編輯：劉清