.h5 keras 模型和 .json tensorflow.js 模型的预测结果完全不同

Question

所以，我的模型为我提供了测试图像的准确度结果

import cv2
from IPython.display import display, Javascript
from google.colab.output import eval_js
from base64 import b64decode

import matplotlib.pyplot as plt
face_haar_cascade = cv2.CascadeClassifier('/content/gdrive/My Drive/New FEC Facial Expression/haarcascade_frontalface_default.xml')
from IPython.display import Image
try:
 filename = '/content/gdrive/My Drive/photo-1533227268428-f9ed0900fb3b.jpg'
 img = cv2.imread(filename)

 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

 faces = face_haar_cascade.detectMultiScale(gray, 1.3,6)
 print('faces', faces)
 for(x,y,w,h) in faces:
   cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
   roi_gray = gray[y:y+h, x:x+w]
   roi_color = img[y:y+h, x:x+w]
   plt.grid(None)
   plt.xticks([])
   plt.yticks([])
   imgplot = plt.imshow(img)
 # Show the image which was just taken.
 # display(Image(filename))
except Exception as err:
 # Errors will be thrown if the user does not have a webcam or if they do not
 # grant the page permission to access it.
 print(str(err))


import cv2
import sys

imagePath ='/content/gdrive/My Drive/photo-1533227268428-f9ed0900fb3b.jpg'
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier('/content/gdrive/My Drive/New FEC Facial Expression/haarcascade_frontalface_default.xml')
faces = faceCascade.detectMultiScale(
   gray,
   scaleFactor=1.3,
   minNeighbors=3,
   minSize=(30, 30)
)

print("[INFO] Found {0} Faces.".format(len(faces)))

for (x, y, w, h) in faces:
   cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
   roi_color = image[y:y + h, x:x + w]
   print("[INFO] Object found. Saving locally.")
   cv2.imwrite('/content/gdrive/My Drive/converted Images/faces.jpg', roi_color)

status = cv2.imwrite('faces_detected.jpg', image)
print("[INFO] Image faces_detected.jpg written to filesystem: ", status)
# from skimage import io
from keras.preprocessing import image
img = image.load_img('/content/gdrive/My Drive/converted Images/faces.jpg', color_mode = "grayscale", target_size=(48, 48))
x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x /= 255
show_img=image.load_img('/content/gdrive/My Drive/converted Images/faces.jpg', grayscale=False, target_size=(200, 200))
plt.gray()
plt.imshow(show_img)
plt.show()
if len(faces): 
 custom = model.predict(x)
 index = np.argmax(custom[0])
 emotion1 = custom[0][index]*100
 print(custom)
 print(emotion_label_to_text[index],' => ',  emotion1)
else:
 print('No Face Detected')

这给出了很好的结果，并且相同结果的输出是正确的，我插入的图像是快乐图像，opencv 用于检测人脸并裁剪它，然后使用裁剪后的图像放入模型中，并给了我很好的结果，

但是 tf.js 部分我使用 tfjs 转换器将 keras 模型转换为 .json 并编写了以下代码

 const classifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_ALT2);
    try {
        const canvImg = await canvas.loadImage(
            path.join(__dirname, `images/${req.file.filename}`)
        );
        const image = await cv.imread(path.join(__dirname, `/images/${req.file.filename}`));
        const classifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_ALT2);
        const { objects, numDetections } = classifier.detectMultiScale(image.bgrToGray());
        if (!objects.length) {
            return next({
                msg: 'No face detected'
            })
        } else {
            const model = await tf.loadLayersModel(
                "http://localhost:8000/models/model.json"
            );
            const obj = objects[0]
            const cnvs = canvas.createCanvas(48, 48);
            const ctx = cnvs.getContext("2d");
            ctx.drawImage(canvImg, obj.x, obj.y, obj.width, obj.height, 0, 0, cnvs.width, cnvs.height);
            var tensor = tf.browser
                .fromPixels(cnvs)
                .mean(2)
                .toFloat()
                .expandDims(-1)
                .expandDims(0, 'None')



            const prediction = await model.predict(tensor).data();
            console.log(prediction);
            var emotions = [
                "angry",
                "disgust",
                "fear",
                "happy",
                "sad",
                "surprise",
                        ];
            var index = Object.values(prediction).findIndex(
                (p) => p === Math.max(...Object.values(prediction))
            );
            res.status(200).json(emotions[index])
            fs.unlink(
                path.join(process.cwd(), "./faceDetection/images/" + req.file.filename),
                function(err, removed) {
                    if (err) console.log("file removing err");
                    else console.log("file removed");
                }
            );
        }

    } catch (e) {
        return next(e)
    }

我使用 opencv4nodejs 检测图像，canvas 裁剪图像（canvas 为我裁剪面部部分提供了很好的结果）和 tf.js 用于预测，但输出每次都给我相同的结果在对象一中的所有这些键中他们中的人将得到 1（在这种情况下是恐惧），并继续为我在 keras 中测试的相同图像提供相同的结果。

我在操纵张量时做错了什么吗？

Answer 1

一种可能的原因。 在python中，您使用x /= 255将图像输入“标准化”为 [0,1] 。 你不是用 Javascript 做的。

Answer 2

js 中的预处理与 python 中的不同。

在 python 中，图像通过除以 255 进行归一化

在 Js 中，通过计算第三轴上的平均值 (mean(2)) 将图像转换为灰度。 这是张量应该是什么：

 const tensor = tf.browser.fromPixels(cnvs)
  .div(255)
  .toFloat()
  .expandDims(0)