ValueError: The layer sequential has never been called and thus has no defined input

Question

I have written this streamlit code to detect whether a person is wearing a mask or not and if not then display their estimated age and gender using DeepFace library. I've used tensorflow to use pre-trained model MobileNet v2 and used weights from ImageNet dataset. I am using OpenCV to capture real time video feed from the webcam and do analysis on the frames returned by it using DeepFace. But I am getting ValueError in DeepFace.analyze() function This is the code:

import streamlit as st
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from deepface import DeepFace

# Load the trained mask detection model
model = load_model("mask_detection_model.h5")

# Load the pre-trained face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Create a function for real-time mask detection
def detect_mask(frame):
    # Preprocess the frame
    resized_frame = cv2.resize(frame, (128, 128))
    resized_frame = img_to_array(resized_frame)
    resized_frame = preprocess_input(resized_frame)
    resized_frame = np.expand_dims(resized_frame, axis=0)

    # Perform prediction
    predictions = model.predict(resized_frame)
    return predictions

# Streamlit web app
st.title("Real-time Face Mask Detection")

# Open the webcam
cap = cv2.VideoCapture(0)

if not cap.isOpened():
    st.error("Error: Could not open webcam.")

else:
    stframe = st.empty()
    while True:
        ret, frame = cap.read()
        if not ret:
            st.error("Error: Unable to capture frame.")
            break
        
        # Perform mask detection
        predictions = detect_mask(frame)
        label = "Mask" if np.argmax(predictions) == 1 else "No Mask"
        color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100))
        
        # Display the frame with the label
        for (x, y, w, h) in faces:
            face = frame[y:y + h, x:x + w]

            # Perform mask detection
            predictions = detect_mask(frame)
            label = "Mask" if np.argmax(predictions) == 1 else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

            # If no mask is detected, estimate age and gender
            if label == "No Mask":
                results = DeepFace.analyze(img_path = frame[y:y+h, x:x+w], actions=['age', 'gender'], enforce_detection=False)  #<-- Getting error at this line
                results = results[0]
                age = results['age']
                gender = results['dominant_gender']
                # print(results)
                # print(type(results[0]))

                # Display age and gender estimation
                cv2.putText(frame, f'Age: {age:.1f} years', (x, y - 60), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
                cv2.putText(frame, f'Gender: {gender}', (x, y - 40), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

            # Display mask detection result
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
        
        # Convert the frame to RGB for Streamlit
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        
        # Display the frame using Streamlit
        stframe.image(frame_rgb, channels="RGB", use_column_width=True)
        
        cv2.waitKey(1)

    cap.release()
    cv2.destroyAllWindows()

And this is the error when I run streamlit run app.py:

File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 542, in _run_script
    exec(code, module.__dict__)
File "D:\Machine Learning Projects\Face Mask Detection\app.py", line 63, in <module>
    results = DeepFace.analyze(img_path = frame[y:y+h, x:x+w], actions=['age', 'gender'], enforce_detection=False)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\deepface\DeepFace.py", line 222, in analyze
    return demography.analyze(
           ^^^^^^^^^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\deepface\modules\demography.py", line 157, in analyze
    apparent_age = modeling.build_model("Age").predict(img_content)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\deepface\modules\modeling.py", line 46, in build_model
    model_obj[model_name] = model()
                            ^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\deepface\extendedmodels\Age.py", line 32, in __init__
    self.model = load_model()
                 ^^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\deepface\extendedmodels\Age.py", line 61, in load_model
    age_model = Model(inputs=model.input, outputs=base_model_output)
                             ^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\keras\src\ops\operation.py", line 216, in input
    return self._get_node_attribute_at_index(0, "input_tensors", "input")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Machine Learning Projects\Face Mask Detection\.venv\Lib\site-packages\keras\src\ops\operation.py", line 247, in _get_node_attribute_at_index
    raise ValueError(
ValueError: The layer sequential has never been called and thus has no defined input.

I've gone through the documentation of DeepFace but I'm unable to figure out the possible cause of this error.

Please help at the earliest, I'd be grateful.

Answer 1

Try changing the model you using, I also had this error but mitigated it by using other face models apart from the default one though in my case I was verifying faces, currently the available models are:

models = [
    "VGG-Face", 
    "Facenet", 
    "Facenet512", 
    "OpenFace", 
    "DeepFace", 
    "DeepID", 
    "ArcFace", 
    "Dlib", 
    "SFace",
    "GhostFaceNet",
    ]

If the model option is not available on analyze you can reload the weights by first deleting the weights that were already downloaded or you can play around with the analyze function in deepface to use a custom model you want