Python深度学习实战 基于tensorflow和keras的聊天机器人以及人脸、物体和语音识别

Python深度学习实战 基于tensorflow和keras的聊天机器人以及人脸、物体和语音识别

# Import the OpenCV library
import cv2
# Initialize a face cascade using the frontal face haar cascade provided
# with the OpenCV2 library. This will be required for face detection in an
# image.
faceCascade = cv2.CascadeClassifier（‘haarcascade_frontalface_default.xml‘）
# The desired output width and height can be modified according to the needs.
OUTPUT_SIZE_WIDTH = 700
OUTPUT_SIZE_HEIGHT = 600

# Open the first webcam device
capture = cv2.VideoCapture（0）

# Create two opencv named windows for showing the input output images.
cv2.namedWindow（“base-image“ cv2.WINDOW_AUTOSIZE）
cv2.namedWindow（“result-image“ cv2.WINDOW_AUTOSIZE）

# Position the windows next to each other
cv2.moveWindow（“base-image“ 20 200）
cv2.moveWindow（“result-image“ 640 200）

# Start the window thread for the two windows we are using
cv2.startWindowThread（）

rectangleColor = （0 100 255）

while（1）:
    # Retrieve the latest image from the webcam
    rcfullSizebaseImage = capture.read（）
# Resize the image to 520x420
    baseImage= cv2.resize（fullSizebaseImage （520 420））

# Check if a key was pressed and if it was Q or q then destroy all
# opencv windows and exit the application stopping the infinite loop.
    pressedKey = cv2.waitKey（2）
    if （pressedKey == ord（‘Q‘）） | （pressedKey == ord（‘q‘））:
        cv2.destroyAllWindows（）
        exit（0）
# Result image is the image we will show the user which is a
# combination of the original image captured from the webcam with the
# overlayed rectangle detecting the largest face
    resultImage = baseImage.copy（）

# We will be using gray colored image for face detection.
# So we need to convert the baseImage captured by webcam to a gray-based image
    gray_image = cv2.cvtColor（baseImage cv2.COLOR_BGR2GRAY）
    faces = faceCascade.detectMultiScale（gray_image 1.3 5）
    # As we are only interested in the ‘largest‘ face we need to
# calculate the largest area of the found rectangle.
# For this first initialize the required variables to 0.
    maxArea = 0
    x = 0
    y = 0
    w = 0
    h = 0

# Loop over all faces found in the image and check if the area for this face is
# the largest so far
    for（_x _y _w _h） in faces:
        if _w * _h > maxArea:
            x = _x
            y = _y
            w = _w
            h = _h
            maxArea = w * h
# If any face is found draw a rectangle around the largest face present in the picture
    if maxArea > 0:
        cv2.rectangle（resultImage （x-10 y-20）（x + w+10 y + h+20） rectangleColor 2）
# Since we want to show something larger on the screen than the
# original 520x420 we resize the image again

# Note that it would also be possible to keep the large version
# of the baseimage and make the result image a copy of this large
# base image and use the scaling factor to draw the rectangle
# at the right coordinates.
    largeResult = cv2.resize（resultImage（OUTPUT_SIZE_WIDTH OUTPUT_SIZE_HEIGHT））
# Finally we show the images on the screen
    cv2.imshow（“base-im

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\
     文件          66  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\.gitattributes
     文件       28610  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\9781484235157.jpg
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter10_RNN and LSTM in visual\
     文件       58177  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter10_RNN and LSTM in visual\Time Series forcasting with lstm model.ipynb
     文件       48119  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter10_RNN and LSTM in visual\sp500.csv
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter11_Speech to text and vice versa\
     文件       14096  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter11_Speech to text and vice versa\Speech to Text API and Text to Speech.ipynb
     文件      704556  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter11_Speech to text and vice versa\audio.wav
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\
     文件          72  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\Removing Punctuations.ipynb
     文件          72  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\Removing Stopwords.ipynb
     文件       26152  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\TF-IDF and Word2Vec.ipynb
     文件          72  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\Tokenization.ipynb
     文件        1393  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter12_Developing Chatbots\intent1.csv
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter13_Face Recognition\
     文件        3062  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter13_Face Recognition\Face_Detection.py
     文件        5288  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter13_Face Recognition\Face_Recognition.py
     文件        6612  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter13_Face Recognition\Face_Tracking.py
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter1_Prerequisites of  Deep Learning Numpy Pandas and Scikit-Learn\
     文件      341456  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter1_Prerequisites of  Deep Learning Numpy Pandas and Scikit-Learn\chapter1_summary.ipynb
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter2_Basics of Tensorflow\
     文件        6190  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter2_Basics of Tensorflow\TFBasics.ipynb
     文件       27538  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter2_Basics of Tensorflow\chapter2_summary.ipynb
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\
     文件       32446  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\MLPMNIST.ipynb
     文件        9107  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\Softmax _RegressionB.ipynb
     文件       34522  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\chapter3_summary.ipynb
     文件    13115488  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\model.h5
     文件     6564312  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter3_Understanding and working on Keras\modelWeight.h5
     目录           0  2018-05-22 15:41  Deep-Learning-Apps-Using-Python-master\Chapter5_Regresson to MLP in Tensorflow\
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