def predicting(imgurl: str):
modFile = 'mymod.mod'
mod = pickle.load(open(modFile, 'rb'))
a = cv.image(imgurl)
feat = a.getresnet50()
res = mod.predict([feat])
return {"class": res}
def classifier():
img_url = request.values['p_image_url']
a = cv.image(img_url)
feat = a.getmobilenet()
model = joblib.load('model_mobilenet.pkl')
#Predict classes with LinearSVC
prediction = model.predict([feat])
result = {'img_url': img_url, 'prediction': prediction[0]}
return jsonify(result)
def predictimg(imgurl):
a = cv.image(imgurl)
feat = a.getresnet50()
probList = mymod.predict_proba([feat])[0]
maxprobind = np.argmax(probList)
prob = probList[maxprobind]
outclass = mymod.classes_[maxprobind]
result = {}
result['class'] = outclass
result['probability'] = prob
return result
def classifier():
img_url = request.values['p_image_url']
a = cv.image(img_url)
feat = a.getmobilenet()
modFile = 'CDmodel.mod'
model = pickle.load(open(modFile, 'rb'))
#Predict classes with LinearSVC
prediction = model.predict([feat])
print(feat.shape)
result = {'img_url': img_url, 'prediction': prediction[0]}
return jsonify(result)
def predictImage(pic_name):
#Load model
modFile = 'gymmachine.mod'
mod = pickle.load(open(modFile, 'rb'))
# input image
test_image = cv.image(pic_name)
feat = test_image.getmobilenet()
res = mod.predict([feat])
#print(res)
return res[0]
#print('The result is: {}.'.format(predictImage('testimage.jpg')))
def predict_image(img_url):
a = cv.image(img_url)
feat = a.getmobilenet()
probList = mymod.predict_proba([feat])[0]
maxprobind = np.argmax(probList)
prob = probList[maxprobind]
outclass = mymod.classes_[maxprobind]
result = {}
result['class'] = outclass
result['probability'] = prob
food = cal_df.loc[cal_df['food_name'] == outclass]
if len(food.index) > 0:
result['cal'] = float(food['cal'].values[0])
result['fat'] = float(food['fat'].values[0])
result['protein'] = float(food['protein'].values[0])
result['carbohydrate'] = float(food['carbohydrate'].values[0])
return result
def predicting(imgurl):
a = cv.image(imgurl)
feat = a.getresnet50()
# feat = a.getmobilenet()
res = mod.predict([feat])[0]
return res
def predictimg(imgurl): a = cv.image(imgurl) feat = a.getresnet50() res = model.predict([feat])[0] return res
def predictimg(imgurl):
print('predict image running')
a = cv.image(imgurl)
feat = a.getmobilenet()
res = mod.predict([feat])
return res