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from django.shortcuts import render
from django.http import JsonResponse
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import keras
from PIL import Image,ImageOps
import numpy as np
import cv2 as cv
from tensorflow.keras import preprocessing
from tensorflow.keras.models import load_model
from tensorflow.keras.activations import softmax
from sklearn.preprocessing import OneHotEncoder
import os
import h5py
from django.views.decorators.csrf import csrf_exempt
model = tf.keras.models.load_model('api/mlModel/model.h5')
shape = ((50,50,3))
model = tf.keras.Sequential([hub.KerasLayer(model,input_shape=shape)])
modelV2 = tf.keras.models.load_model('api/mlModel/model2.0.h5')
@csrf_exempt
def predict(request):
if request.method == 'POST':
#image = Image.open("api/mlModel/0.jpg")
# Get the image from the request
print(request.FILES['image'])
image = Image.open(request.FILES['image'])
# Preprocess the image
test_image = image.resize((50,50))
test_image = preprocessing.image.img_to_array(test_image)
test_image = test_image / 255
test_image = np.expand_dims(test_image, axis =0)
class_names = ['1','2','3' ,'4', '5', '6' ,'7' ,'8' ,'9' ,'A' ,'B' ,'C' ,'D' ,'E' ,'F', 'G', 'H', 'I', 'J', 'K' ,'L' ,'M', 'N', 'O' ,'P' ,'Q', 'R' ,'S', 'T', 'U' ,'V' ,'W' ,'X' ,'Y' ,'Z']
# Make a prediction
predictions = model.predict(test_image)
scores = tf.nn.softmax(predictions[0])
scores = scores.numpy()
image_class = class_names[np.argmax(scores)]
print(image_class)
return JsonResponse({'prediction': image_class})
else:
return render(request,'predict.html')
# Create your views here.
def form_view(request):
return render(request,'predict.html')
@csrf_exempt
def predictV2(request):
if request.method == 'POST':
#image = Image.open("api/mlModel/0.jpg")
# Get the image from the request
print(request.FILES['image'])
#image1 = Image.open(request.FILES['image'])
#img = cv.imread(Image.open(request.FILES['image']))
img = cv.imdecode(np.fromstring(request.FILES['image'].read(), np.uint8), cv.IMREAD_UNCHANGED)
# Preprocess the image
resized_img = cv.resize(img, (250, 250), interpolation=cv.INTER_CUBIC)
resized_img.shape
#plt.imshow(resized_img)
img = resized_img
pred = modelV2.predict(x = np.array(img).reshape(-1,250,250,3)).flatten()
enc = OneHotEncoder()
enc.fit([['6'],
['K'],
['L'],
['R'],
['V'],
['3'],
['F'],
['M'],
['J'],
['0'],
['9'],
['U'],
['8'],
['P'],
['W'],
['Q'],
['N'],
['E'],
['Y'],
['H'],
['1'],
['X'],
['C'],
['G'],
['5'],
['O'],
['S'],
['B'],
['2'],
['7'],
['D'],
['T'],
['4'],
['I'],
['A'],
['Z']])
out = enc.inverse_transform(pred.reshape(1,-1))
print(out[0][0])
return JsonResponse({'prediction': out[0][0]})
else:
return render(request,'predict.html')
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