HandPose/pose_classification_utils.py at master · BUlexlab/HandPose · GitHub

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import cv2
import numpy as np
from tensorflow import Graph, Session
import tensorflow as tf
import os; os.environ['KERAS_BACKEND'] = 'tensorflow'
import keras

def load_KerasGraph(path):
    print("> ====== loading Keras model for classification")
    thread_graph = Graph()
    with thread_graph.as_default():
        thread_session = Session()
        with thread_session.as_default():
            model = keras.models.load_model(path)
            #model._make_predict_function()
            graph = tf.get_default_graph()
    print(">  ====== Keras model loaded")
    return model, graph, thread_session

def classify(model, graph, sess, im):
    try:
        im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
        im = cv2.flip(im, 1)
    except:
        pass


    # Reshape
    res = cv2.resize(im, (28,28), interpolation=cv2.INTER_AREA)

    # Convert to float values between 0. and 1.
    res = res.astype(dtype="float64")
    res = res / 255
    res = np.reshape(res, (1, 28, 28, 1))
    prediction = None
    with graph.as_default():
        with sess.as_default():
            prediction= model.predict(res)
            # print(model.predict_classes(res))
    # print(prediction - np.array([0.19306985, 0.5668088,  0.00509515, 0.2350262 ]))
    # print(np.argmax(prediction - np.array([0.19306985, 0.5668088,  0.00509515, 0.2350262 ])))
    return prediction[0]

def test_classify(model, im):
    im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)

    im = cv2.flip(im, 1)

    # Reshape
    res = cv2.resize(im, (28,28), interpolation=cv2.INTER_AREA)

    # Convert to float values between 0. and 1.
    res = res.astype(dtype="float64")
    res = res / 255
    res = np.reshape(res, (1, 28, 28, 1))

    prediction= model.predict(res)
    # print(prediction)
    # print(np.argmax(prediction - np.array([0.19306985, 0.5668088,  0.00509515, 0.2350262 ])))

    return prediction[0]

if __name__ == "__main__":
    import keras

    # print(">> loading keras model for pose classification")
    # try:
    #     model = keras.models.load_model("cnn/models/hand_poses_win_wGarbage_10.h5")
    # except Exception as e:
    #     print(e)

    # # Fist
    # print('<< FIST >>')
    # im = cv2.imread("Poses/Fist/Fist_1/Fist_1_1302.png")
    # print(test_classify(model, im))

    # # Dang
    # print('<< DANG >>')
    # im = cv2.imread("Poses/Dang/Dang_1/Dang_1_1223.png")
    # print(test_classify(model, im))

    # # Four
    # print('<< FOUR >>')
    # im = cv2.imread("Poses/Four/Four_1/Four_1_867.png")
    # print(test_classify(model, im))

    # # Startrek
    # print('<< Startrek >>')
    # im = cv2.imread("Poses/Startrek/Startrek_1/Startrek_1_867.png")
    # print(test_classify(model, im))

    # # Palm
    # print('<< Palm >>')
    # im = cv2.imread("Poses/Palm/Palm_1/Palm_1_867.png")
    # print(test_classify(model, im))