网页游戏架设_如何从头架设一个Google Quickdraw Demo

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import os
import json
import datetime as dt
import cv2
import base64
import io
from PIL import Image
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation
from tensorflow.keras.metrics import categorical_accuracy, top_k_categorical_accuracy, categorical_crossentropy
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import MobileNet
from tensorflow.keras.applications.mobilenet import preprocess_input
from tensorflow.keras import backend as K

BASE_SIZE = 256
NCSVS = 100
NCATS = 340
np.random.seed(seed=1987)
tf.set_random_seed(seed=1987)
 
def top_3_accuracy(y_true, y_pred):
    return top_k_categorical_accuracy(y_true, y_pred, k=3)
 
STEPS = 800
EPOCHS = 16
size = 64
batchsize = 680

def init():
    sess = tf.InteractiveSession()
    loaded_model = MobileNet(input_shape=(size, size, 1), alpha=1., weights=None, classes=NCATS)
    loaded_model.load_weights("./model/model.h5")
    loaded_model.compile(optimizer=Adam(lr=0.002), loss='categorical_crossentropy',
                  metrics=[categorical_crossentropy, categorical_accuracy, top_3_accuracy])
    print(loaded_model.summary())
    graph = tf.get_default_graph()
    return loaded_model, sess, graph
 
global model, sess, graph
model, sess, graph = init()

def prepareImage(im):
    #gray
    im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    #binary
    thresh = 127
    im = cv2.threshold(im, thresh, 255, cv2.THRESH_BINARY)[1]
    # see if need to invert
    n_white_pix = np.sum(im == 255)
    n_black_pix = np.sum(im == 0)
    if n_white_pix > n_black_pix:
        im = cv2.bitwise_not(im)
    #trim1, move content to the left-up corner;
    size = len(im[0])
    sum0 = im.sum(axis = 0)
    sum1 = im.sum(axis = 1)
    for i in range(len(sum0)):
        if sum0[i] == 0:
            im = np.delete(im, 0, 1)
            zero = np.zeros((size,1))
            im = np.append(im,zero,1)
        else :
            break
    for i in range(len(sum1)):
        if sum1[i] == 0:
            im = np.delete(im, 0, 0)
            zero = np.zeros((1,size))
            im = np.append(im,zero,0)
        else :
            break
    # trim2 crop content
    sum3 = im.sum(axis = 0)
    sum4 = im.sum(axis = 1)
    x2 = 1
    y2 = 1
    while x2 < len(sum3) and sum3[-x2] ==0:
        x2 += 1
    while y2 < len(sum4) and sum4[-y2] ==0:
        y2 += 1
    w = size - x2
    h = size - y2
    contentSize = w if w > h  else  h
    # only crop if there is realy content
    if contentSize > 16:
        im = im[0:contentSize, 0:contentSize]
    return im

def prepareImageAndPredict(model, cv2ImageData,size=64):
    try:
        # downsize to 64
        image64 = cv2.resize(cv2ImageData, (64, 64))
        x = np.zeros((1,size, size, 1))
        x[0, :, :, 0] = image64
        x = preprocess_input(x).astype(np.float32)
        prediction = model.predict(x, batch_size=128, verbose=1)
        top5 = np.argsort(-prediction, axis=1)[:, :5]
        return top5[0]
 
    except Exception as e:
        print(e)
        pass

imagePath = "./whateverDoodle.jpg"
image = cv2.imread(imagePath)
image = prepareImage(image)
 
with sess.as_default():
        with graph.as_default():
            prediction= prepareImageAndPredict(model, image).tolist()
 
print(prediction)

categories = ['airplane', 'alarm clock', 'ambulance', 'angel', 'animal migration', 'ant', 'anvil', 'apple', 'arm', 'asparagus', 'axe', 'backpack', 'banana', 'bandage', 'barn', 'baseball', 'baseball bat', 'basket', 'basketball', 'bat', 'bathtub', 'beach', 'bear', 'beard', 'bed', 'bee', 'belt', 'bench', 'bicycle', 'binoculars', 'bird', 'birthday cake', 'blackberry', 'blueberry', 'book', 'boomerang', 'bottlecap', 'bowtie', 'bracelet', 'brain', 'bread', 'bridge', 'broccoli', 'broom', 'bucket', 'bulldozer', 'bus', 'bush', 'butterfly', 'cactus', 'cake', 'calculator', 'calendar', 'camel', 'camera', 'camouflage', 'campfire', 'candle', 'cannon', 'canoe', 'car', 'carrot', 'castle', 'cat', 'ceiling fan', 'cell phone', 'cello', 'chair', 'chandelier', 'church', 'circle', 'clarinet', 'clock', 'cloud', 'coffee cup', 'compass', 'computer', 'cookie', 'cooler', 'couch', 'cow', 'crab', 'crayon', 'crocodile', 'crown', 'cruise ship', 'cup', 'diamond', 'dishwasher', 'diving board', 'dog', 'dolphin', 'donut', 'door', 'dragon', 'dresser', 'drill', 'drums', 'duck', 'dumbbell', 'ear', 'elbow', 'elephant', 'envelope', 'eraser', 'eye', 'eyeglasses', 'face', 'fan', 'feather', 'fence', 'finger', 'fire hydrant', 'fireplace', 'firetruck', 'fish', 'flamingo', 'flashlight', 'flip flops', 'floor lamp', 'flower', 'flying saucer', 'foot', 'fork', 'frog', 'frying pan', 'garden', 'garden hose', 'giraffe', 'goatee', 'golf club', 'grapes', 'grass', 'guitar', 'hamburger', 'hammer', 'hand', 'harp', 'hat', 'headphones', 'hedgehog', 'helicopter', 'helmet', 'hexagon', 'hockey puck', 'hockey stick', 'horse', 'hospital', 'hot air balloon', 'hot dog', 'hot tub', 'hourglass', 'house', 'house plant', 'hurricane', 'ice cream', 'jacket', 'jail', 'kangaroo', 'key', 'keyboard', 'knee', 'ladder', 'lantern', 'laptop', 'leaf', 'leg', 'light bulb', 'lighthouse', 'lightning', 'line', 'lion', 'lipstick', 'lobster', 'lollipop', 'mailbox', 'map', 'marker', 'matches', 'megaphone', 'mermaid', 'microphone', 'microwave', 'monkey', 'moon', 'mosquito', 'motorbike', 'mountain', 'mouse', 'moustache', 'mouth', 'mug', 'mushroom', 'nail', 'necklace', 'nose', 'ocean', 'octagon', 'octopus', 'onion', 'oven', 'owl', 'paint can', 'paintbrush', 'palm tree', 'panda', 'pants', 'paper clip', 'parachute', 'parrot', 'passport', 'peanut', 'pear', 'peas', 'pencil', 'penguin', 'piano', 'pickup truck', 'picture frame', 'pig', 'pillow', 'pineapple', 'pizza', 'pliers', 'police car', 'pond', 'pool', 'popsicle', 'postcard', 'potato', 'power outlet', 'purse', 'rabbit', 'raccoon', 'radio', 'rain', 'rainbow', 'rake', 'remote control', 'rhinoceros', 'river', 'roller coaster', 'rollerskates', 'sailboat', 'sandwich', 'saw', 'saxophone', 'school bus', 'scissors', 'scorpion', 'screwdriver', 'sea turtle', 'see saw', 'shark', 'sheep', 'shoe', 'shorts', 'shovel', 'sink', 'skateboard', 'skull', 'skyscraper', 'sleeping bag', 'smiley face', 'snail', 'snake', 'snorkel', 'snowflake', 'snowman', 'soccer ball', 'sock', 'speedboat', 'spider', 'spoon', 'spreadsheet', 'square', 'squiggle', 'squirrel', 'stairs', 'star', 'steak', 'stereo', 'stethoscope', 'stitches', 'stop sign', 'stove', 'strawberry', 'streetlight', 'string bean', 'submarine', 'suitcase', 'sun', 'swan', 'sweater', 'swing set', 'sword', 't-shirt', 'table', 'teapot', 'teddy-bear', 'telephone', 'television', 'tennis racquet', 'tent', 'The Eiffel Tower', 'The Great Wall of China', 'The Mona Lisa', 'tiger', 'toaster', 'toe', 'toilet', 'tooth', 'toothbrush', 'toothpaste', 'tornado', 'tractor', 'traffic light', 'train', 'tree', 'triangle', 'trombone', 'truck', 'trumpet', 'umbrella', 'underwear', 'van', 'vase', 'violin', 'washing machine', 'watermelon', 'waterslide', 'whale', 'wheel', 'windmill', 'wine bottle', 'wine glass', 'wristwatch', 'yoga', 'zebra', 'zigzag']

from flask import Flask , jsonify, request, render_template, send_from_directory
from flask_cors import CORS
from predictor import *
import random
import json
import pandas as pd
import numpy as np
from tensorflow.keras import models
import time
import datetime
import cv2
import sys, getopt
import os
import base64
import io
from PIL import Image

app = Flask(__name__)
CORS(app)

def stringToRGB(base64_string):
    imgdata = base64.b64decode(str(base64_string))
    image = Image.open(io.BytesIO(imgdata))
    return cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
 
@app.route("/api/doodlePredict", methods=["POST"])
def predictAPI():
    global model, graph
    print("this is the request: ", request.form.to_dict())
    image_raw = request.form.to_dict()["data"]
    image_raw = stringToRGB(image_raw)
    image = prepareImage(image_raw)
    response = {'prediction':{
    'numbers':[],
    'names':[]
    }}
    with sess.as_default():
        with graph.as_default():
            response['prediction']['numbers'] = prepareImageAndPredict(model, image).tolist()
    for i in range(len(response['prediction']['numbers'])):
        response['prediction']['names'].append(categories[response['prediction']['numbers'][i]])
    print("this is the response: ", response['prediction']['names'])
    cv2.imwrite("./doodleHistory/"+ ', '.join(response['prediction']['names']) +", "+datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y") +".jpg", image_raw)
    return jsonify(response)

if __name__ == "__main__":
    app.run(host = "0.0.0.0", port = 5800, debug = True)