# 教學課程：在 Python 中執行 TensorFlow 模型

## 必要條件

• 安裝 Python 2.7+ 或 Python 3.6+。
• 安裝 pip。

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

## 載入模型和標籤

``````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:
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())
``````

## 準備要預測的影像

1. 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

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)
``````
2. 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)
``````
3. 裁剪中央最大的正方形

``````h, w = image.shape[:2]
min_dim = min(w,h)
max_square_image = crop_center(image, min_dim, min_dim)
``````
4. 將該平方調整為 256x256

``````augmented_image = resize_to_256_square(max_square_image)
``````
5. 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)

``````
6. 定義協助程式函式。 上述步驟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

``````# 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

``````    # 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
``````