ValueError("Tensor %s is not an element of this graph." % obj)

Question

First of all, English is not my mother language so excuse me, if I don't express myself really well, please feel free to correct me.

I'm making an emotion recognition system that uses rest services to send the image from the client's browser.

This is the code:

# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
K.clear_session()

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# starting lists for calculating modes
emotion_window = []

And the function:

def detect_emotion(self, img):

    # Convert RGB to BGR
    bgr_image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
    faces = detect_faces(face_detection, gray_image)

    for face_coordinates in faces:

        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]
        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_classifier._make_predict_function()

        emotion_prediction = emotion_classifier.predict(gray_face)

        emotion_probability = np.max(emotion_prediction)
        emotion_label_arg = np.argmax(emotion_prediction)
        emotion_text = emotion_labels[emotion_label_arg]
        emotion_window.append(emotion_text)

        if len(emotion_window) > frame_window:
            emotion_window.pop(0)
        try:
            emotion_mode = mode(emotion_window)
        except:
            continue

        if emotion_text == 'angry':
            color = emotion_probability * np.asarray((255, 0, 0))
        elif emotion_text == 'sad':
            color = emotion_probability * np.asarray((0, 0, 255))
        elif emotion_text == 'happy':
            color = emotion_probability * np.asarray((255, 255, 0))
        elif emotion_text == 'surprise':
            color = emotion_probability * np.asarray((0, 255, 255))
        else:
            color = emotion_probability * np.asarray((0, 255, 0))

        color = color.astype(int)
        color = color.tolist()

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_mode,
                  color, 0, -45, 1, 1)

    img = Image.fromarray(rgb_image)

    return img


I'm facing this error when i run my code using waitress:

    File "c:\users\afgir\documents\pythonprojects\face_reco\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3569, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj) 
    ValueError: Tensor Tensor("predictions_1/Softmax:0", shape=(?, 7), dtype=float32) is not an element of this graph.

It loads the image and does all the processing well, I'm pretty sure that the error is in the emotion_classifier.predict line, just don't know how to fix it.

I've tried with the two solutions in this question and none of them worked.

I'm really new using Tensorflow so I'm kinda stuck with this.

Answer 1

I'm just trying to find out your real environment, but I guess you may use Keras amd some Keras model to predict emotions.

Your error message caused because of the line:

K.clear_session()

which, from the documentation: keras.backend.clear_session(). So you clear all graph it has been created, then you try to run the classifier's predict(), which lost all its context this way.
Thus just simply delete this line.

This section is was about some code the Op deleted:
In this task you don't need to use tf.Graph() at all. You just simply should call emotion_classifier.predict() as a simple python method outside and without of using any tensorflow graph:

def detect_emotion(self, img):

    # Convert RGB to BGR
    bgr_image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
    faces = detect_faces(face_detection, gray_image)

    for face_coordinates in faces:

        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]
        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_classifier._make_predict_function()

        emotion_prediction = emotion_classifier.predict(gray_face)

        emotion_probability = np.max(emotion_prediction)
        emotion_label_arg = np.argmax(emotion_prediction)
        emotion_text = emotion_labels[emotion_label_arg]
        emotion_window.append(emotion_text)

        if len(emotion_window) > frame_window:
            emotion_window.pop(0)
        try:
            emotion_mode = mode(emotion_window)
        except:
            continue

        if emotion_text == 'angry':
            color = emotion_probability * np.asarray((255, 0, 0))
        elif emotion_text == 'sad':
            color = emotion_probability * np.asarray((0, 0, 255))
        elif emotion_text == 'happy':
            color = emotion_probability * np.asarray((255, 255, 0))
        elif emotion_text == 'surprise':
            color = emotion_probability * np.asarray((0, 255, 255))
        else:
            color = emotion_probability * np.asarray((0, 255, 0))

        color = color.astype(int)
        color = color.tolist()

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_mode,
                  color, 0, -45, 1, 1)

    img = Image.fromarray(rgb_image)

    return img