ValueError: 层序列从未被调用，因此没有定义的输入

Question

我写了一个streamlit代码，用来检测一个人是否戴着口罩。如果没有戴口罩，就会显示他们的估计年龄和性别，这个功能是通过DeepFace库实现的。我使用了tensorflow来调用一个已经训练好的模型，叫做MobileNet v2，并且使用了来自ImageNet数据集的权重。我还用OpenCV从摄像头捕捉实时视频，并对返回的每一帧进行分析，使用DeepFace来处理。但是在调用DeepFace.analyze()这个函数时，我遇到了ValueError错误。

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()

这是我运行streamlit run app.py时出现的错误：

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.

我查阅了DeepFace的文档，但还是无法找到这个错误的可能原因。

请尽快帮我解决这个问题，我会非常感激的。

Answer 1

试着换个模型，我之前也遇到过这个错误，但我通过使用其他面部模型解决了这个问题，虽然在我的情况下我是用来验证面孔的。目前可用的模型有：

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

如果在分析时没有模型选项，你可以先删除已经下载的权重，然后重新加载权重，或者你可以在deepface的分析功能中尝试使用你想要的自定义模型。