ter = array_value($config, 'filter'); $arr = array_value($filter, $type); $enable = array_value($arr, 'enable'); $wordarr = array_value($arr, 'keyword'); if (0 == $enable || empty($wordarr)) return FALSE; foreach ($wordarr as $_keyword) { if (!$_keyword) continue; $r = strpos(strtolower($keyword), strtolower($_keyword)); if (FALSE !== $r) { $error = $_keyword; return TRUE; } } return FALSE; } // return http://domain.com OR https://domain.com function url_prefix() { $http = ((isset($_SERVER['HTTPS']) && 'on' == $_SERVER['HTTPS']) || (isset($_SERVER['HTTP_X_FORWARDED_PROTO']) && $_SERVER['HTTP_X_FORWARDED_PROTO'] == 'https')) ? 'https://' : 'http://'; return $http . $_SERVER['HTTP_HOST']; } // 唯一身份ID function uniq_id() { return uniqid(substr(md5(microtime(true) . mt_rand(1000, 9999)), 8, 8)); } // 生成订单号 14位 function trade_no() { $trade_no = str_replace('.', '', microtime(1)); $strlen = mb_strlen($trade_no, 'UTF-8'); $strlen = 14 - $strlen; $str = ''; if ($strlen) { for ($i = 0; $i <= $strlen; $i++) { if ($i < $strlen) $str .= '0'; } } return $trade_no . $str; } // 生成订单号 16位 function trade_no_16() { $explode = explode(' ', microtime()); $trade_no = $explode[1] . mb_substr($explode[0], 2, 6, 'UTF-8'); return $trade_no; } // 当前年的天数 function date_year($time = NULL) { $time = intval($time) ? $time : time(); return date('L', $time) + 365; } // 当前年份中的第几天 function date_z($time = NULL) { $time = intval($time) ? $time : time(); return date('z', $time); } // 当前月份中的第几天，没有前导零 1 到 31 function date_j($time = NULL) { $time = intval($time) ? $time : time(); return date('j', $time); } // 当前月份中的第几天，有前导零的2位数字 01 到 31 function date_d($time = NULL) { $time = intval($time) ? $time : time(); return date('d', $time); } // 当前时间为星期中的第几天 数字表示 1表示星期一 到 7表示星期天 function date_w_n($time = NULL) { $time = intval($time) ? $time : time(); return date('N', $time); } // 当前日第几周 function date_d_w($time = NULL) { $time = intval($time) ? $time : time(); return date('W', $time); } // 当前几月 没有前导零1-12 function date_n($time = NULL) { $time = intval($time) ? $time : time(); return date('n', $time); } // 当前月的天数 function date_t($time = NULL) { $time = intval($time) ? $time : time(); return date('t', $time); } // 0 o'clock on the day function clock_zero() { return strtotime(date('Ymd')); } // 24 o'clock on the day function clock_twenty_four() { return strtotime(date('Ymd')) + 86400; } // 8点过期 / expired at 8 a.m. function eight_expired($time = NULL) { $time = intval($time) ? $time : time(); // 当前时间大于8点则改为第二天8点过期 $life = date('G') <= 8 ? (strtotime(date('Ymd')) + 28800 - $time) : clock_twenty_four() - $time + 28800; return $life; } // 24点过期 / expired at 24 a.m. function twenty_four_expired($time = NULL) { $time = intval($time) ? $time : time(); $twenty_four = clock_twenty_four(); $life = $twenty_four - $time; return $life; } /** * @param $url 提交地址 * @param string $post POST数组 / 空为GET获取数据 / $post='GET'获取连续跳转最终URL * @param string $cookie cookie * @param int $timeout 超时 * @param int $ms 设为1是毫秒 * @return mixed 返回数据 */ function https_request($url, $post = '', $cookie = '', $timeout = 30, $ms = 0) { if (empty($url)) return FALSE; if (version_compare(PHP_VERSION, '5.2.3', '<')) { $ms = 0; $timeout = 30; } is_array($post) and $post = http_build_query($post); // 没有安装curl 使用http的形式，支持post if (!extension_loaded('curl')) { //throw new Exception('server not install CURL'); if ($post) { return https_post($url, $post, $cookie, $timeout); } else { return http_get($url, $cookie, $timeout); } } is_array($cookie) and $cookie = http_build_query($cookie); $curl = curl_init(); // 返回执行结果，不输出 curl_setopt($curl, CURLOPT_RETURNTRANSFER, true); //php5.5跟php5.6中的CURLOPT_SAFE_UPLOAD的默认值不同 if (class_exists('\CURLFile')) { curl_setopt($curl, CURLOPT_SAFE_UPLOAD, true); } else { defined('CURLOPT_SAFE_UPLOAD') and curl_setopt($curl, CURLOPT_SAFE_UPLOAD, false); } // 设定请求的RUL curl_setopt($curl, CURLOPT_URL, $url); // 设定返回信息中包含响应信息头 if (ini_get('safe_mode') && ini_get('open_basedir')) { // $post参数必须为GET if ('GET' == $post) { // 安全模式时将头文件的信息作为数据流输出 curl_setopt($curl, CURLOPT_HEADER, true); // 安全模式采用连续抓取 curl_setopt($curl, CURLOPT_NOBODY, true); } } else { curl_setopt($curl, CURLOPT_HEADER, false); // 允许跳转10次 curl_setopt($curl, CURLOPT_MAXREDIRS, 10); // 使用自动跳转，返回最后的Location curl_setopt($curl, CURLOPT_FOLLOWLOCATION, true); } $ua1 = 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1'; $ua = empty($_SERVER["HTTP_USER_AGENT"]) ? $ua1 : $_SERVER["HTTP_USER_AGENT"]; curl_setopt($curl, CURLOPT_USERAGENT, $ua); // 兼容HTTPS if (FALSE !== stripos($url, 'https://')) { curl_setopt($curl, CURLOPT_SSL_VERIFYPEER, FALSE); curl_setopt($curl, CURLOPT_SSL_VERIFYHOST, FALSE); //ssl版本控制 //curl_setopt($curl, CURLOPT_SSLVERSION, CURL_SSLVERSION_TLSv1); curl_setopt($curl, CURLOPT_SSLVERSION, true); } $header = array('Content-type: application/x-www-form-urlencoded;charset=UTF-8', 'X-Requested-With: XMLHttpRequest'); $cookie and $header[] = "Cookie: $cookie"; curl_setopt($curl, CURLOPT_HTTPHEADER, $header); if ($post) { // POST curl_setopt($curl, CURLOPT_POST, true); // 自动设置Referer curl_setopt($curl, CURLOPT_AUTOREFERER, true); curl_setopt($curl, CURLOPT_POSTFIELDS, $post); } if ($ms) { curl_setopt($curl, CURLOPT_NOSIGNAL, true); // 设置毫秒超时 curl_setopt($curl, CURLOPT_TIMEOUT_MS, intval($timeout)); // 超时毫秒 } else { curl_setopt($curl, CURLOPT_TIMEOUT, intval($timeout)); // 秒超时 } //优先解析 IPv6 超时后IPv4 //curl_setopt($curl, CURLOPT_IPRESOLVE, CURL_IPRESOLVE_V4); curl_setopt($curl, CURLOPT_ENCODING, 'gzip'); // 返回执行结果 $output = curl_exec($curl); // 有效URL，输出URL非URL页面内容 CURLOPT_RETURNTRANSFER 必须为false 'GET' == $post and $output = curl_getinfo($curl, CURLINFO_EFFECTIVE_URL); curl_close($curl); return $output; } function save_image($img) { $ch = curl_init(); // 设定请求的RUL curl_setopt($ch, CURLOPT_URL, $img); // 设定返回信息中包含响应信息头 启用时会将头文件的信息作为数据流输出 //curl_setopt($ch, CURLOPT_HEADER, false); //curl_setopt($ch, CURLOPT_USERAGENT, $_SERVER["HTTP_USER_AGENT"]); // true表示$html,false表示echo $html curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); curl_setopt($ch, CURLOPT_CONNECTTIMEOUT, 10); curl_setopt($ch, CURLOPT_SSL_VERIFYHOST, false); curl_setopt($ch, CURLOPT_SSL_VERIFYPEER, false); //curl_setopt($ch, CURLOPT_BINARYTRANSFER, 1); //curl_setopt($ch, CURLOPT_FOLLOWLOCATION, 0); curl_setopt($ch, CURLOPT_ENCODING, 'gzip'); $output = curl_exec($ch); curl_close($ch); return $output; } // 计算字串宽度:剧中对齐(字体大小/字串内容/字体链接/背景宽度/倍数) function calculate_str_width($size, $str, $font, $width, $multiple = 2) { $box = imagettfbbox($size, 0, $font, $str); return ($width - $box[4] - $box[6]) / $multiple; } // 搜索目录下的文件 比对文件后缀 function search_directory($path) { if (is_dir($path)) { $paths = scandir($path); foreach ($paths as $val) { $sub_path = $path . '/' . $val; if ('.' == $val || '..' == $val) { continue; } else if (is_dir($sub_path)) { //echo '目录名:' . $val . '
'; search_directory($sub_path); } else { //echo ' 最底层文件: ' . $path . '/' . $val . '
'; $ext = strtolower(file_ext($sub_path)); if (in_array($ext, array('php', 'asp', 'jsp', 'cgi', 'exe', 'dll'), TRUE)) { echo '异常文件：' . $sub_path . '
'; } } } } } // 一维数组转字符串 $sign待签名字符串 $url为urlencode转码GET参数字符串 function array_to_string($arr, &$sign = '', &$url = '') { if (count($arr) != count($arr, 1)) throw new Exception('Does not support multi-dimensional array to string'); // 注销签名 unset($arr['sign']); // 排序 ksort($arr); reset($arr); // 转字符串做签名 $url = ''; $sign = ''; foreach ($arr as $key => $val) { if (empty($val) || is_array($val)) continue; $url .= $key . '=' . urlencode($val) . '&'; $sign .= $key . '=' . $val . '&'; } $url = substr($url, 0, -1); $url = htmlspecialchars($url); $sign = substr($sign, 0, -1); } // 私钥生成签名 function rsa_create_sign($data, $key, $sign_type = 'RSA') { if (!function_exists('openssl_sign')) throw new Exception('OpenSSL extension is not enabled'); if (!defined('OPENSSL_ALGO_SHA256')) throw new Exception('Only versions above PHP 5.4.8 support SHA256'); $key = wordwrap($key, 64, "\n", true); if (FALSE === $key) throw new Exception('Private Key Error'); $key = "-----BEGIN RSA PRIVATE KEY-----\n$key\n-----END RSA PRIVATE KEY-----"; if ('RSA2' == $sign_type) { openssl_sign($data, $sign, $key, OPENSSL_ALGO_SHA256); } else { openssl_sign($data, $sign, $key, OPENSSL_ALGO_SHA1); } // 加密 return base64_encode($sign); } // 公钥验证签名 function rsa_verify_sign($data, $sign, $key, $sign_type = 'RSA') { $key = wordwrap($key, 64, "\n", true); if (FALSE === $key) throw new Exception('Public Key Error'); $key = "-----BEGIN PUBLIC KEY-----\n$key\n-----END PUBLIC KEY-----"; // 签名正确返回1 签名不正确返回0 错误-1 if ('RSA2' == $sign_type) { $result = openssl_verify($data, base64_decode($sign), $key, OPENSSL_ALGO_SHA256); } else { $result = openssl_verify($data, base64_decode($sign), $key, OPENSSL_ALGO_SHA1); } return $result === 1; } // Array to xml array('appid' => 'appid', 'code' => 'success') function array_to_xml($arr) { if (!is_array($arr) || empty($arr)) throw new Exception('Array Error'); $xml = ""; foreach ($arr as $key => $val) { if (is_numeric($val)) { $xml .= "<" . $key . ">" . $val . ""; } else { $xml .= "<" . $key . ">"; } } $xml .= ""; return $xml; } // Xml to array function xml_to_array($xml) { if (!$xml) throw new Exception('XML error'); $old = libxml_disable_entity_loader(true); // xml解析 $result = (array)simplexml_load_string($xml, null, LIBXML_NOCDATA | LIBXML_COMPACT); // 恢复旧值 if (FALSE === $old) libxml_disable_entity_loader(false); return $result; } // 逐行读取 function well_import($file) { if ($handle = fopen($file, 'r')) { while (!feof($handle)) { yield trim(fgets($handle)); } fclose($handle); } } // 计算总行数 function well_import_total($file, $key = 'well_import_total') { static $cache = array(); if (isset($cache[$key])) return $cache[$key]; $count = cache_get($key); if (NULL === $count) { $count = 0; $globs = well_import($file); while ($globs->valid()) { ++$count; $globs->next(); // 指向下一个 } $count and cache_set($key, $count, 300); } return $cache[$key] = $count; } $g_dir_file = FALSE; function well_search_dir($path) { global $g_dir_file; FALSE === $g_dir_file and $g_dir_file = array(); if (is_dir($path)) { $paths = scandir($path); foreach ($paths as $val) { $sub_path = $path . '/' . $val; if ('.' == $val || '..' == $val) { continue; } else if (is_dir($sub_path)) { well_search_dir($sub_path); } else { $g_dir_file[] = $sub_path; } } } return $g_dir_file; } ?>python - Saving and Loading Images for Tensorflow Lite - Stack Overflow

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    python - Saving and Loading Images for Tensorflow Lite - Stack Overflow

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    Planning on using Tensorflow Lite for image classification.

    To test the model, using Fashion-MNIST database to create the model following a Tensorflow basic example from their website. Created and saved the model and want to test it in TF Lite.

    I want to save an image from the mentioned database to predict its class in TF Lite. This is the code to check the images (some of them from Google search):

    import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
import tensorflow as tf
import base64
import io
from os.path import dirname, join
from PIL import Image
from matplotlib.pyplot import imread

fashion_mnist = tf.keras.datasets.fashion_mnist
(train_X, train_y), (test_X, test_y) = fashion_mnist.load_data()

# Function to check image format
def check_mnist_image_format(image_array):
    print(f"Image {index} shape: {image_array.shape}")
    print(f"Image {index} data type: {image_array.dtype}")
    print(f"Image {index} min pixel value: {np.min(image_array)}")
    print(f"Image {index} max pixel value: {np.max(image_array)}")

# Check the format of the first training image
check_mnist_image_format(train_X[0], 0)

filename = join(dirname("/home/gachaconr/tf/"), 'image.unit8')
with open(filename, 'wb') as file:
    file.write(train_X[0])
    print("image saved")    

# Open the image file
image_array = imread(filename)
check_mnist_image_format(image_array)

    Saving the image with extension .unit8 since that the format of the image database given by the properties given by the function check_mnist_image_format

    The image is saved as expected but the function imread cannot read it. This is the error:

    Traceback (most recent call last):
File "/home/gachaconr/tf/imagemnistdatacheck.py", line 40, in <module>
     image_array = imread(filename)
                   ^^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/matplotlib/pyplot.py", line 2607, in imread
     return matplotlib.image.imread(fname, format)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/matplotlib/image.py", line 1512, in imread
     with img_open(fname) as image:
          ^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/PIL/Image.py", line 3532, in open
     raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file '/home/gachaconr/tf/image.unit8'

    How can I accomplish the mentioned task? What am I doing wrong?

    Planning on using Tensorflow Lite for image classification.

    To test the model, using Fashion-MNIST database to create the model following a Tensorflow basic example from their website. Created and saved the model and want to test it in TF Lite.

    I want to save an image from the mentioned database to predict its class in TF Lite. This is the code to check the images (some of them from Google search):

    import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
import tensorflow as tf
import base64
import io
from os.path import dirname, join
from PIL import Image
from matplotlib.pyplot import imread

fashion_mnist = tf.keras.datasets.fashion_mnist
(train_X, train_y), (test_X, test_y) = fashion_mnist.load_data()

# Function to check image format
def check_mnist_image_format(image_array):
    print(f"Image {index} shape: {image_array.shape}")
    print(f"Image {index} data type: {image_array.dtype}")
    print(f"Image {index} min pixel value: {np.min(image_array)}")
    print(f"Image {index} max pixel value: {np.max(image_array)}")

# Check the format of the first training image
check_mnist_image_format(train_X[0], 0)

filename = join(dirname("/home/gachaconr/tf/"), 'image.unit8')
with open(filename, 'wb') as file:
    file.write(train_X[0])
    print("image saved")    

# Open the image file
image_array = imread(filename)
check_mnist_image_format(image_array)

    Saving the image with extension .unit8 since that the format of the image database given by the properties given by the function check_mnist_image_format

    The image is saved as expected but the function imread cannot read it. This is the error:

    Traceback (most recent call last):
File "/home/gachaconr/tf/imagemnistdatacheck.py", line 40, in <module>
     image_array = imread(filename)
                   ^^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/matplotlib/pyplot.py", line 2607, in imread
     return matplotlib.image.imread(fname, format)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/matplotlib/image.py", line 1512, in imread
     with img_open(fname) as image:
          ^^^^^^^^^^^^^^^
File "/home/gachaconr/tf/lib/python3.12/site-packages/PIL/Image.py", line 3532, in open
     raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file '/home/gachaconr/tf/image.unit8'

    How can I accomplish the mentioned task? What am I doing wrong?

    • python
    • tensorflow
    • deep-learning
    • python-imaging-library
    • tensorflow-lite
    Share Improve this question edited 21 hours ago desertnaut 60.5k32 gold badges155 silver badges181 bronze badges asked Mar 31 at 16:33 gusgus 3612 gold badges6 silver badges21 bronze badges 1
    • what is image.unit8 supposed to be? – Christoph Rackwitz Commented Mar 31 at 18:35
    Add a comment  | 

    2 Answers 2

    Reset to default 0 
    import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from PIL import Image
from os.path import join, dirname

# Load Fashion-MNIST dataset
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_X, train_y), (test_X, test_y) = fashion_mnist.load_data()

# Function to check image format
def check_mnist_image_format(image_array, index):
    print(f"Image {index} shape: {image_array.shape}")
    print(f"Image {index} data type: {image_array.dtype}")
    print(f"Image {index} min pixel value: {np.min(image_array)}")
    print(f"Image {index} max pixel value: {np.max(image_array)}")

# Check the format of the first training image
check_mnist_image_format(train_X[0], 0)

# Save the image properly as PNG
filename = join(dirname("/home/gachaconr/tf/"), 'image.png')
image = Image.fromarray(train_X[0])  # Convert NumPy array to PIL image
image.save(filename)
print("Image saved successfully as PNG.")

# Reload the image using PIL
loaded_image = Image.open(filename)
loaded_image_array = np.array(loaded_image)

# Check the reloaded image format
check_mnist_image_format(loaded_image_array, "Reloaded Image")

    Thanks Somnath.

    After some reading this is the code that works:

    import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
#import cv2
# TensorFlow and tf.keras
import tensorflow as tf
import base64
import io
from os.path import dirname, join
#from com.chaquo.python import Python
from PIL import Image
from matplotlib.pyplot import imread

fashion_mnist = tf.keras.datasets.fashion_mnist

# Load MNIST dataset
(train_X, train_y), (test_X, test_y) = fashion_mnist.load_data()

# Function to check image format
def check_mnist_image_format(image_array, index):
    print(f"Image {index} shape: {image_array.shape}")
    print(f"Image {index} data type: {image_array.dtype}")
    print(f"Image {index} min pixel value: {np.min(image_array)}")
    print(f"Image {index} max pixel value: {np.max(image_array)}")

    # Visualize the image
    #plt.imshow(image_array, cmap='gray')
    #plt.title(f"Image {index}")
    #plt.show()

# Check the format of the first training image
check_mnist_image_format(train_X[0], 0)

filename = join(dirname("/home/gachaconr/tf/"), 'imageXX.jpg')
img = train_X[0]
image = Image.fromarray(img.astype(np.uint8))
image.save(filename)
print("image saved")    

# Open the image file
image_array = imread(filename)
# Convert the image to a NumPy array
check_mnist_image_format(image_array, 0)

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