Question

所以，我的模型为我提供了测试图像相当准确的结果

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，并且对于相同结果的输出是正确的，我插入的图像是快乐图像，opencv用于检测人脸并对其进行裁剪，然后使用该裁剪后的图像放入模型中并给了我好的结果，

但是在tf.js部分中，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来检测图像，使用canvas来裁剪图像（canvas给我很好的结果来裁剪面部部分），并使用tf.js进行预测，但是每次在所有这些键中的输出都给我相同的结果在对象中，其中一个将得到1（在这种情况下为恐惧），并为我在keras中测试的同一张图片提供相同的结果。

我在操纵张量时做错了吗？

Answer 1

一个可能的原因。在python中，您使用nil将图像输入“标准化”为[0,1]。您不是用Javascript做的。

Answer 2

js中的预处理不同于python中的预处理。

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均值（mean（2））将图像转换为灰度。张量应该是这样的：

@{
       @:<p>Name: <strong>@Model.Email</strong></p>
 }

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

2 个答案: