Python model 模块，predict() 实例源码

def compute_test_accuracy(X_test, Y_test, model, prediction_type, cellgroup_map_array):

    prediction = model.predict(X_test)
    auc = []

    if prediction_type=="cellgroup":

        prediction = np.dot(prediction, cellgroup_map_array)
        Y_test = np.dot(Y_test, cellgroup_map_array)

    mask = ~np.logical_or(Y_test.sum(1)==0, Y_test.sum(1)==Y_test.shape[1])

    for y,pred in zip(Y_test.T,prediction.T):
        pos = np.logical_and(mask, y==1)
        neg = np.logical_and(mask, y==0)
        try:
            U = stats.mannwhitneyu(pred[pos], pred[neg])[0]
            auc.append(1.-U/(np.count_nonzero(pos)*np.count_nonzero(neg)))
        except ValueError:
            auc.append(0.5)

    return auc

def test_file():
    count = 1
    face_cascade = cv2.CascadeClassifier(
        '/usr/local/opt/opencv3/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')

    argvs = sys.argv
    for argv in argvs[1:]:
        img = cv2.imread(argv)

        if type(img) != str:
            try:
                gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                print('convert succeed')
            except:
                print('can not convert to gray image')
                continue
            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
            for (x, y, w, h) in faces:
                f = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
                model = load_model('/Users/songheqi/model/model.h5')
                num, acc = predict(model, f, 128)
                name_list = read_name_list('/Users/songheqi/train_set/')
                print('The {} picture is '.format(count) +
                      name_list[num] + ' acc : ', acc)
                count += 1

def predict():
    data = {}
    try:
        data = request.get_json()['data']
    except KeyError:
        return jsonify(status_code='400', msg='Bad Request'), 400

    data = base64.b64decode(data)

    image = io.BytesIO(data)
    predictions = model.predict(image)
    current_app.logger.info('Predictions: %s', predictions)
    return jsonify(predictions=predictions)

def test_camera():
    face_patterns = cv2.CascadeClassifier(
        '/usr/local/opt/opencv3/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')

    cameraCapture = cv2.VideoCapture(0)
    success, frame = cameraCapture.read()

    while True:
        success, frame = cameraCapture.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # ????
        faces = face_patterns.detectMultiScale(gray, 1.3, 5)  # ????
        for (x, y, w, h) in faces:
            frame = cv2.rectangle(
                frame, (x, y), (x + w, y + h), (255, 0, 0), 2)  # ?????????????
            f = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
            model = load_model('/Users/songheqi/model/model.h5')
            num, acc = predict(model, f, 128)
            name_list = read_name_list('/Users/songheqi/train_set/')
            print('You are ' + name_list[num] + ' acc : ', acc)
        cv2.imshow("Camera", frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):  # ?‘q’???
            break

    cameraCapture.release()
    cv2.destroyAllWindows()