教學課程：在 Python 中執行 TensorFlow 模型

發行項
01/23/2024

本教學課程說明如何在本機使用導出的 TensorFlow 模型來分類影像。

注意

本教學課程僅適用於從「一般」（compact）」影像分類項目導出的模型。如果您匯出其他模型，請造訪我們的範例程式代碼存放庫。

必要條件

安裝 Python 2.7+ 或 Python 3.6+。
安裝 pip。

接下來，您必須安裝下列套件：

pip install tensorflow
pip install pillow
pip install numpy
pip install opencv-python

載入您的模型和標籤

從導出步驟下載 的.zip 檔案包含 model.pb 和 labels.txt 檔案。這些檔案代表定型的模型和分類標籤。第一個步驟是將模型載入您的專案。將下列程式代碼新增至新的 Python 腳本。

import tensorflow as tf
import os

graph_def = tf.compat.v1.GraphDef()
labels = []

# These are set to the default names from exported models, update as needed.
filename = "model.pb"
labels_filename = "labels.txt"

# Import the TF graph
with tf.io.gfile.GFile(filename, 'rb') as f:
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')

# Create a list of labels.
with open(labels_filename, 'rt') as lf:
    for l in lf:
        labels.append(l.strip())

準備影像以進行預測

您需要採取幾個步驟來準備影像以進行預測。這些步驟會模擬訓練期間所執行的影像操作。

開啟檔案，並在 BGR 色彩空間中建立影像

from PIL import Image
import numpy as np
import cv2

# Load from a file
imageFile = "<path to your image file>"
image = Image.open(imageFile)

# Update orientation based on EXIF tags, if the file has orientation info.
image = update_orientation(image)

# Convert to OpenCV format
image = convert_to_opencv(image)

如果影像的維度大於 1600 像素，請呼叫此方法（稍後定義）。
```
image = resize_down_to_1600_max_dim(image)
```

裁剪最大的中心方塊

h, w = image.shape[:2]
min_dim = min(w,h)
max_square_image = crop_center(image, min_dim, min_dim)

將該平方調整為 256x256

augmented_image = resize_to_256_square(max_square_image)

裁剪模型特定輸入大小的中心

# Get the input size of the model
with tf.compat.v1.Session() as sess:
    input_tensor_shape = sess.graph.get_tensor_by_name('Placeholder:0').shape.as_list()
network_input_size = input_tensor_shape[1]

# Crop the center for the specified network_input_Size
augmented_image = crop_center(augmented_image, network_input_size, network_input_size)

定義協助程式函式。上述步驟會使用下列協助程式函式：

def convert_to_opencv(image):
    # RGB -> BGR conversion is performed as well.
    image = image.convert('RGB')
    r,g,b = np.array(image).T
    opencv_image = np.array([b,g,r]).transpose()
    return opencv_image

def crop_center(img,cropx,cropy):
    h, w = img.shape[:2]
    startx = w//2-(cropx//2)
    starty = h//2-(cropy//2)
    return img[starty:starty+cropy, startx:startx+cropx]

def resize_down_to_1600_max_dim(image):
    h, w = image.shape[:2]
    if (h < 1600 and w < 1600):
        return image

    new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
    return cv2.resize(image, new_size, interpolation = cv2.INTER_LINEAR)

def resize_to_256_square(image):
    h, w = image.shape[:2]
    return cv2.resize(image, (256, 256), interpolation = cv2.INTER_LINEAR)

def update_orientation(image):
    exif_orientation_tag = 0x0112
    if hasattr(image, '_getexif'):
        exif = image._getexif()
        if (exif != None and exif_orientation_tag in exif):
            orientation = exif.get(exif_orientation_tag, 1)
            # orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
            orientation -= 1
            if orientation >= 4:
                image = image.transpose(Image.TRANSPOSE)
            if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
                image = image.transpose(Image.FLIP_TOP_BOTTOM)
            if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
                image = image.transpose(Image.FLIP_LEFT_RIGHT)
    return image

分類影像

影像準備好為張量之後，我們可以透過模型傳送它以進行預測。

# These names are part of the model and cannot be changed.
output_layer = 'loss:0'
input_node = 'Placeholder:0'

with tf.compat.v1.Session() as sess:
    try:
        prob_tensor = sess.graph.get_tensor_by_name(output_layer)
        predictions = sess.run(prob_tensor, {input_node: [augmented_image] })
    except KeyError:
        print ("Couldn't find classification output layer: " + output_layer + ".")
        print ("Verify this a model exported from an Object Detection project.")
        exit(-1)

顯示結果

然後，透過模型執行影像張量的結果必須對應回標籤。

    # Print the highest probability label
    highest_probability_index = np.argmax(predictions)
    print('Classified as: ' + labels[highest_probability_index])
    print()

    # Or you can print out all of the results mapping labels to probabilities.
    label_index = 0
    for p in predictions:
        truncated_probablity = np.float64(np.round(p,8))
        print (labels[label_index], truncated_probablity)
        label_index += 1

下一步

接下來，瞭解如何將模型包裝至行動應用程式：