人脸识别预测错误（cv :: face :: Fisherfaces :: predict）

Question

OpenCV错误：错误的参数（此Fisherfaces模型尚未计算。是吗 在cv :: face :: Fisherfaces :: predict，

中调用Fisherfaces :: train？）

cv2.error：C：\ projects \ opencv-python \ opencv_contrib \ modules \ face \ src \ fisher_faces.cpp：137：错误：（ - 5）此Fisherfaces模型尚未计算。你有没有给Fisherfaces ::火车打电话？在函数cv :: face :: Fisherfaces :: predict

中

import cv2
import numpy as np
import argparse
import time
import glob
import os
import sys
import subprocess
import pandas
import random
import Update_Model
import math

#Define variables and load classifier
camnumber = 0
video_capture = cv2.VideoCapture()
facecascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
fishface = cv2.face.FisherFaceRecognizer_create()
try:
    fishface.read("trained_emoclassifier.xml")
except:
   print("no trained xml file found, please run program with --update flagfirst")
parser = argparse.ArgumentParser(description="Options for the emotion-based music player")
parser.add_argument("--update", help="Call to grab new images and update the model accordingly", action="store_true")
args = parser.parse_args()
facedict = {}
actions = {}
emotions = ["angry", "happy", "sad", "neutral"]
df = pandas.read_excel("EmotionLinks.xlsx") #open Excel file
actions["angry"] = [x for x in df.angry.dropna()] #We need de dropna() when columns are uneven in length, which creates NaN values at missing places. The OS won't know what to do with these if we try to open them.
actions["happy"] = [x for x in df.happy.dropna()]
actions["sad"] = [x for x in df.sad.dropna()]
actions["neutral"] = [x for x in df.neutral.dropna()]

def open_stuff(): #Open the file, credit to user4815162342, on the stackoverflow link in the text above
    if sys.platform == "win32":
        os.startfile(filename)
    else:
        opener ="open" if sys.platform == "darwin" else "xdg-open"
        subprocess.call([opener, filename])

def crop_face(clahe_image, face):
    for (x, y, w, h) in face:
        faceslice = clahe_image[y:y+h, x:x+w]
        faceslice = cv2.resize(faceslice, (350, 350))
    facedict["face%s" %(len(facedict)+1)] = faceslice
    return faceslice

def update_model(emotions):
    print("Model update mode active")
    check_folders(emotions)
    for i in range(0, len(emotions)):
        save_face(emotions[i])
    print("collected images, looking good! Now updating model...")
    Update_Model.update(emotions)
    print("Done!")

def check_folders(emotions):
    for x in emotions:
        if os.path.exists("dataset\\%s" %x):
            pass
        else:
            os.makedirs("dataset\\%s" %x)

def save_face(emotion):
    print("\n\nplease look " + emotion + ". Press enter when you're ready to have your pictures taken")
    input() #Wait until enter is pressed with the raw_input() method
    video_capture.open(0)
    while len(facedict.keys()) < 16:
        detect_face()
    video_capture.release()
    for x in facedict.keys():
        cv2.imwrite("dataset\\%s\\%s.jpg" %(emotion, len(glob.glob("dataset\\%s\\*" %emotion))), facedict[x])
    facedict.clear() 

def recognize_emotion():
    predictions = []
    confidence = []
    for x in facedict.keys():
        pred, conf = fishface.predict(facedict[x])
        cv2.imwrite("images\\%s.jpg" %x, facedict[x])
        predictions.append(pred)
        confidence.append(conf)
    recognized_emotion = emotions[max(set(predictions), key=predictions.count)]
print("I think you're %s" %recognized_emotion)            
actionlist = [x for x in actions[recognized_emotion]] #get list of actions/files for detected emotion
    random.shuffle(actionlist) #Randomly shuffle the list
    open_stuff(actionlist[0]) #Open the first entry in the list

def grab_webcamframe():
    ret, frame = video_capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    clahe_image = clahe.apply(gray)
    return clahe_image

def detect_face():
    clahe_image = grab_webcamframe()
    face = facecascade.detectMultiScale(clahe_image, scaleFactor=1.1, minNeighbors=15, minSize=(10, 10), flags=cv2.CASCADE_SCALE_IMAGE)
    if len(face) == 1: 
        faceslice = crop_face(clahe_image, face)
        recognize_emotion()
        return faceslice
    else:
        print("no/multiple faces detected, passing over frame")

def run_detection():
    while len(facedict) != 10:
        detect_face()
    recognize_emotion = recognize_emotion()
    return recognize_emotion

if args.update:
    update_model(emotions)
else:
    video_capture.open(camnumber)
    run_detection()

我该如何解决？