如何修改Python面部情绪识别代码 过滤孤立预测以平滑输出结果

核心思路 #

针对预测结果跳变的问题，我们通过连续判定逻辑过滤孤立的异常预测：

• 设定平滑阈值n，代表需要连续命中同一情绪的次数
• 维护连续相同预测的计数，和当前稳定展示的情绪值
• 仅当连续n次预测结果完全一致时，才更新界面展示的情绪内容，否则保留上一次稳定的展示结果

修改步骤 #

无需修改模型训练相关代码，仅替换display模式下的代码即可：

# emotions will be displayed on your face from the webcam feed
elif mode == "display":
    model.load_weights('model.h5')

    # prevents openCL usage and unnecessary logging messages
    cv2.ocl.setUseOpenCL(False)

    # dictionary which assigns each label an emotion (alphabetical order)
    emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}

    # start the webcam feed
    cap = cv2.VideoCapture(1)
    # 平滑配置：可根据需求调整阈值，数值越大越稳定，但是响应延迟越高
    SMOOTH_THRESHOLD = 5
    last_emotion = None
    consecutive_count = 0
    stable_emotion = None
    while True:
        # Find haar cascade to draw bounding box around face
        ret, frame = cap.read()
        if not ret:
            break
        facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)

        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
            roi_gray = gray[y:y + h, x:x + w]
            cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
            prediction = model.predict(cropped_img)
            maxindex = int(np.argmax(prediction))
            text = emotion_dict[maxindex]
            if ("Sad" in text) or ("Angry" in text) or ("Disgusted" in text):
                text = "Sad"
            # 新增平滑逻辑
            if text == last_emotion:
                consecutive_count += 1
            else:
                consecutive_count = 1
                last_emotion = text
            # 连续命中阈值才更新稳定展示的情绪
            if consecutive_count >= SMOOTH_THRESHOLD:
                stable_emotion = text
            # 仅展示稳定的情绪结果
            if stable_emotion is not None and ("Happy" in stable_emotion or "Sad" in stable_emotion):
                cv2.putText(frame, stable_emotion, (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

        cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

补充说明 #

上述代码默认适配单人脸识别场景，如果需要支持多人脸识别，可将三个状态变量改为字典结构，以人脸的坐标特征作为key，单独维护每个检测到的人脸的连续计数即可。

内容的提问来源于stack exchange，提问作者angel_30