def prediction_custom(image):
pred = ""
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsInceptionV3()
prediction.setModelPath(
os.path.join(execution_path, "model_ex-006_acc-0.836483.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=12, prediction_speed="fast")
predictions, probabilities = prediction.predictImage(os.path.join(
execution_path, image),
result_count=1)
# print(str(prediction + " " + probabilities))
for eachPrediction, eachProbability in zip(predictions, probabilities):
class1 = eachPrediction
pred = eachProbability
return class1, pred
def loadPrediction(self, prediction_speed='normal', num_objects=10):
if self.__modelloaded == False:
if self.__modelType == "":
raise ValueError(
"You must set a valid model type before loading the model."
)
if self.__jsonPath == "":
raise ValueError(
"You must set a valid json path before loading the model."
)
elif self.__modelType == "resnet":
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
elif self.__modelType == "squeezenet":
prediction = CustomImagePrediction()
prediction.setModelTypeAsSqueezeNet()
elif self.__modelType == "densenet":
prediction = CustomImagePrediction()
prediction.setModelTypeAsDenseNet()
elif self.__modelType == "inceptionv3":
prediction = CustomImagePrediction()
prediction.setModelTypeAsInceptionV3()
elif self.__modelType == "vgg":
prediction = CustomImagePrediction()
prediction.setModelTypeAsVgg()
prediction.setModelPath(self.modelPath)
prediction.setJsonPath(self.__jsonPath)
prediction.loadModel(prediction_speed, num_objects)
self.__prediction_collection.append(prediction)
self.__modelloaded = True
else:
raise ValueError(
"You must set a valid model type before loading the model.")
class Predict_Image:
# other model to be trained
def __init__(self,
Threshold=20,
modelName="DenseNet",
CustomModelName=None,
CustomModelJsonFilePath=None):
global Model_dir_Path, Web_app_dir
Model_dir_Path = os.path.dirname(os.path.realpath(__file__))
Web_app_dir = os.path.dirname(os.path.realpath(__file__ + "../../.."))
self.Threshold = Threshold
print("Here ....3\n")
if CustomModelName is None:
print("Here ....4\n")
self.prediction = ImagePrediction()
else:
self.prediction = CustomImagePrediction()
if modelName in "ResNet":
print("Here ....5\n")
self.prediction.setModelTypeAsResNet()
if CustomModelName is None:
self.prediction.setModelPath(
Model_dir_Path +
"/Models/resnet50_weights_tf_dim_ordering_tf_kernels.h5")
else:
self.prediction.setModelPath(Model_dir_Path + "/Models/" +
CustomModelName)
self.prediction.setJsonPath(Model_dir_Path + "/Models/" +
CustomModelJsonFilePath)
elif modelName in "SqueezeNet":
print("Here ....5\n")
self.prediction.setModelTypeAsSqueezeNet()
if CustomModelName is None:
self.prediction.setModelPath(
Model_dir_Path +
"/Models/squeezenet_weights_tf_dim_ordering_tf_kernels.h5")
else:
self.prediction.setModelPath(Model_dir_Path + "/Models/" +
CustomModelName)
self.prediction.setJsonPath(Model_dir_Path + "/Models/" +
CustomModelJsonFilePath)
elif modelName in "InceptionV3":
print("Here ....6\n")
self.prediction.setModelTypeAsInceptionV3()
if CustomModelName is None:
self.prediction.setModelPath(
Model_dir_Path +
"/Models/inception_v3_weights_tf_dim_ordering_tf_kernels.h5"
)
else:
self.prediction.setModelPath(Model_dir_Path + "/Models/" +
CustomModelName)
self.prediction.setJsonPath(Model_dir_Path + "/Models/" +
CustomModelJsonFilePath)
elif modelName in "DenseNet":
print("Here ....7\n")
self.prediction.setModelTypeAsDenseNet()
if CustomModelName is None:
print("Here ....7.3\n")
print("value of Model Dir is" + Model_dir_Path +
"/Models/DenseNet-BC-121-32.h5" + "\n")
self.prediction.setModelPath(Model_dir_Path +
"/Models/DenseNet-BC-121-32.h5")
else:
print("Here ....8\n")
self.prediction.setModelPath(Model_dir_Path + "/Models/" +
CustomModelName)
self.prediction.setJsonPath(Model_dir_Path + "/Models/" +
CustomModelJsonFilePath)
self.prediction.loadModel()
def get_classes_from_image(self, url):
save_Image = ImageSave()
self.name = os.path.basename(url)
if "local://" in url:
pass
else:
save_Image.save_Image_from_url(url, self.name)
predictions, probabilities = self.prediction.predictImage(
Web_app_dir + "/static/images/retrieved_images/" + self.name,
result_count=10)
result_set = []
for eachPrediction, eachProbability in zip(predictions, probabilities):
if eachProbability > self.Threshold:
result_set.append({
'Entity': eachPrediction,
'confidence': round(eachProbability, 2)
})
print(eachPrediction, eachProbability)
return result_set
def setModel(self, modelName):
if modelName in "ResNet":
self.prediction.setModelTypeAsResNet()
self.prediction.setModelPath(
Model_dir_Path +
"/Models/resnet50_weights_tf_dim_ordering_tf_kernels.h5")
elif modelName in "SqueezeNet":
self.prediction.setModelTypeAsSqueezeNet()
self.prediction.setModelPath(
Model_dir_Path +
"/Models/squeezenet_weights_tf_dim_ordering_tf_kernels.h5")
elif modelName in "InceptionV3":
self.prediction.setModelTypeAsInceptionV3()
self.prediction.setModelPath(
Model_dir_Path +
"/Models/inception_v3_weights_tf_dim_ordering_tf_kernels.h5")
elif modelName in "DenseNet":
self.prediction.setModelTypeAsDenseNet()
self.prediction.setModelPath(Model_dir_Path +
"/Models/DenseNet-BC-121-32.h5")
self.prediction.loadModel()
from flask import Flask, request, jsonify
import os
import numpy as np
import cv2
from imageai.Prediction.Custom import CustomImagePrediction
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
app = Flask(__name__)
image1 = None
prediction = CustomImagePrediction()
prediction.setModelTypeAsInceptionV3()
prediction.setModelPath("model_ex-053_acc-0.997352.h5")
prediction.setJsonPath("model_class.json")
def model_stra_pota():
prediction.loadModel(num_objects=31)
predictions, probabilities = prediction.predictImage('img.jpg',
result_count=1)
print(predictions, probabilities)
predictions = predictions[0]
probabilities = probabilities[0]
ans = {"pred": predictions, "prob": probabilities}
#e = {predictions:probabilities}
#print (e)
