def predictMultipleImages(self,
sent_images_array,
result_count_per_image=2,
input_type="file"):
"""
'predictMultipleImages()' function is used to predict more than one image by receiving the following arguments:
* input_type , the type of inputs contained in the parsed array. Acceptable values are "file", "array" and "stream"
* sent_images_array , an array of image file paths, image numpy array or image file stream
* result_count_per_image (optionally) , the number of predictions to be sent per image, which must be whole numbers between
1 and 1000. The default is 2.
This function returns an array of dictionaries, with each dictionary containing 2 arrays namely 'prediction_results' and 'prediction_probabilities'. The 'prediction_results'
contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities'
contains the percentage probability of each object class. The position of each object class in the 'prediction_results'
array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array.
:param input_type:
:param sent_images_array:
:param result_count_per_image:
:return output_array:
"""
output_array = []
for image_input in sent_images_array:
prediction_results = []
prediction_probabilities = []
if (self.__modelLoaded == False):
raise ValueError(
"You must call the loadModel() function before making predictions."
)
else:
if (self.__modelType == "squeezenet"):
from .imagenet_utils import preprocess_input, decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(
image_input,
target_size=(self.__input_image_size,
self.__input_image_size))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have set a path to an invalid image file."
)
elif (input_type == "array"):
try:
image_input = Image.fromarray(
np.uint8(image_input))
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong numpy array for the image"
)
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong stream for the image"
)
model = self.__model_collection[0]
prediction = model.predict(image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(result[2] *
100)
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "resnet"):
model = self.__model_collection[0]
from .imagenet_utils import preprocess_input, decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(
image_input,
target_size=(self.__input_image_size,
self.__input_image_size))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have set a path to an invalid image file."
)
elif (input_type == "array"):
try:
image_input = Image.fromarray(
np.uint8(image_input))
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong numpy array for the image"
)
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong stream for the image"
)
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(result[2] *
100)
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "densenet"):
model = self.__model_collection[0]
from .DenseNet.densenet import preprocess_input, decode_predictions
from .DenseNet.densenet import DenseNetImageNet121
if (input_type == "file"):
try:
image_to_predict = image.load_img(
image_input,
target_size=(self.__input_image_size,
self.__input_image_size))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have set a path to an invalid image file."
)
elif (input_type == "array"):
try:
image_input = Image.fromarray(
np.uint8(image_input))
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong numpy array for the image"
)
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong stream for the image"
)
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(result[2] *
100)
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "inceptionv3"):
model = self.__model_collection[0]
from imageai.Prediction.InceptionV3.inceptionv3 import InceptionV3, preprocess_input, \
decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(
image_input,
target_size=(self.__input_image_size,
self.__input_image_size))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have set a path to an invalid image file."
)
elif (input_type == "array"):
try:
image_input = Image.fromarray(
np.uint8(image_input))
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong numpy array for the image"
)
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize(
(self.__input_image_size,
self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict,
dtype=np.float64)
image_to_predict = preprocess_input(
image_to_predict)
except:
raise ValueError(
"You have parsed in a wrong stream for the image"
)
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(result[2] *
100)
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
return output_array
def predictMultipleImages(self,
sent_images_array,
result_count_per_image=2):
"""
'predictMultipleImages()' function is used to predict more than one image by receiving the following arguments:
* sent_images_array , an array of image file paths
* result_count_per_image (optionally) , the number of predictions to be sent per image, which must be whole numbers between
1 and 1000. The default is 2.
This function returns an array of dictionaries, with each dictionary containing 2 arrays namely 'prediction_results' and 'prediction_probabilities'. The 'prediction_results'
contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities'
contains the percentage probability of each object class. The position of each object class in the 'prediction_results'
array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array.
:param sent_images_array:
:param result_count_per_image:
:return output_array:
"""
output_array = []
for eachImage in sent_images_array:
prediction_results = []
prediction_probabilities = []
if (self.__modelLoaded == False):
raise ValueError(
"You must call the loadModel() function before making predictions."
)
else:
if (self.__modelType == "squeezenet"):
try:
from .SqueezeNet.imagenet_utils import preprocess_input, decode_predictions
img = image.load_img(eachImage, target_size=(227, 227))
img = image.img_to_array(img,
data_format="channels_last")
img = np.expand_dims(img, axis=0)
img = preprocess_input(img,
data_format="channels_last")
except:
raise ValueError(
"You have set a path to an invalid image file.")
model = self.__model_collection[0]
prediction = model.predict(img, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
except:
raise ValueError(
"You have set a wrong path to the JSON file")
elif (self.__modelType == "resnet"):
model = self.__model_collection[0]
try:
from .ResNet.imagenet_utils import preprocess_input, decode_predictions
target_image = image.load_img(eachImage,
grayscale=False,
target_size=(224, 224))
target_image = image.img_to_array(
target_image, data_format="channels_last")
target_image = np.expand_dims(target_image, axis=0)
target_image = preprocess_input(
target_image, data_format="channels_last")
except:
raise ValueError(
"You have set a path to an invalid image file.")
prediction = model.predict(x=target_image, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
except:
raise ValueError(
"You have set a wrong path to the JSON file")
elif (self.__modelType == "densenet"):
model = self.__model_collection[0]
try:
from .DenseNet.densenet import DenseNetImageNet121, preprocess_input, decode_predictions
from .DenseNet.densenet import DenseNetImageNet121
image_to_predict = image.load_img(eachImage,
grayscale=False,
target_size=(224,
224))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(
image_to_predict, data_format="channels_last")
except:
raise ValueError(
"You have set a path to an invalid image file.")
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
except:
raise ValueError(
"You have set a wrong path to the JSON file")
elif (self.__modelType == "inceptionv3"):
model = self.__model_collection[0]
try:
from imageai.Prediction.InceptionV3.inceptionv3 import InceptionV3
from imageai.Prediction.InceptionV3.inceptionv3 import preprocess_input, decode_predictions
image_to_predict = image.load_img(eachImage,
grayscale=False,
target_size=(299,
299))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict,
axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError(
"You have set a path to an invalid image file.")
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details[
"percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
except:
raise ValueError(
"You have set a wrong path to the JSON file")
return output_array
def predictMultipleImages(self, sent_images_array, result_count_per_image=2, input_type="file"):
"""
'predictMultipleImages()' function is used to predict more than one image by receiving the following arguments:
* input_type , the type of inputs contained in the parsed array. Acceptable values are "file", "array" and "stream"
* sent_images_array , an array of image file paths, image numpy array or image file stream
* result_count_per_image (optionally) , the number of predictions to be sent per image, which must be whole numbers between
1 and 1000. The default is 2.
This function returns an array of dictionaries, with each dictionary containing 2 arrays namely 'prediction_results' and 'prediction_probabilities'. The 'prediction_results'
contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities'
contains the percentage probability of each object class. The position of each object class in the 'prediction_results'
array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array.
:param input_type:
:param sent_images_array:
:param result_count_per_image:
:return output_array:
"""
output_array = []
for image_input in sent_images_array:
prediction_results = []
prediction_probabilities = []
if (self.__modelLoaded == False):
raise ValueError("You must call the loadModel() function before making predictions.")
else:
if (self.__modelType == "squeezenet"):
from .imagenet_utils import preprocess_input, decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size))
image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have set a path to an invalid image file.")
elif (input_type == "array"):
try:
image_input = Image.fromarray(np.uint8(image_input))
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong numpy array for the image")
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong stream for the image")
model = self.__model_collection[0]
prediction = model.predict(image_to_predict, steps=1)
try:
predictiondata = decode_predictions(prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(str(result[2] * 100))
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details["percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "resnet"):
model = self.__model_collection[0]
from .imagenet_utils import preprocess_input, decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size))
image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have set a path to an invalid image file.")
elif (input_type == "array"):
try:
image_input = Image.fromarray(np.uint8(image_input))
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong numpy array for the image")
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong stream for the image")
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(str(result[2] * 100))
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details["percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "densenet"):
model = self.__model_collection[0]
from .DenseNet.densenet import preprocess_input, decode_predictions
from .DenseNet.densenet import DenseNetImageNet121
if (input_type == "file"):
try:
image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size))
image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have set a path to an invalid image file.")
elif (input_type == "array"):
try:
image_input = Image.fromarray(np.uint8(image_input))
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong numpy array for the image")
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong stream for the image")
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(str(result[2] * 100))
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details["percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
elif (self.__modelType == "inceptionv3"):
model = self.__model_collection[0]
from imageai.Prediction.InceptionV3.inceptionv3 import InceptionV3, preprocess_input, \
decode_predictions
if (input_type == "file"):
try:
image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size))
image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have set a path to an invalid image file.")
elif (input_type == "array"):
try:
image_input = Image.fromarray(np.uint8(image_input))
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong numpy array for the image")
elif (input_type == "stream"):
try:
image_input = Image.open(image_input)
image_input = image_input.resize((self.__input_image_size, self.__input_image_size))
image_input = np.expand_dims(image_input, axis=0)
image_to_predict = image_input.copy()
image_to_predict = np.asarray(image_to_predict, dtype=np.float64)
image_to_predict = preprocess_input(image_to_predict)
except:
raise ValueError("You have parsed in a wrong stream for the image")
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(prediction, top=int(result_count_per_image))
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
prediction_results.append(str(result[1]))
prediction_probabilities.append(str(result[2] * 100))
except:
raise ValueError("An error occured! Try again.")
each_image_details = {}
each_image_details["predictions"] = prediction_results
each_image_details["percentage_probabilities"] = prediction_probabilities
output_array.append(each_image_details)
return output_array
def predictImage(self, image_path, result_count=5):
"""
'predictImage()' function is used to predict a given image by receiving the following arguemants:
* image_path , file path to the image
* result_count (optionally) , the number of predictions to be sent which must be whole numbers between
1 and 1000. The default is 5.
This function returns 2 arrays namely 'result_prediction' and 'prediction_probabilities'. The 'result_prediction'
contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities'
contains the percentage probability of each object class. The position of each object class in the 'result_prediction'
array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array.
:param image_path:
:param result_count:
:return result_prediction, prediction_probabilities:
"""
result_prediction = []
prediction_probabilities = []
if (self.__modelLoaded == False):
raise "You must call the loadModel() function before making predictions."
else:
if (self.__modelType == "squeezenet"):
try:
from .SqueezeNet.imagenet_utils import preprocess_input, decode_predictions
img = image.load_img(image_path, target_size=(227, 227))
img = image.img_to_array(img, data_format="channels_last")
img = np.expand_dims(img, axis=0)
img = preprocess_input(img, data_format="channels_last")
except:
raise "You have set a path to an invalid image file."
model = self.__model_collection[0]
prediction = model.predict(img, steps=1)
try:
predictiondata = decode_predictions(
prediction,
top=int(result_count),
index_file_path=self.jsonPath)
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
result_prediction.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
except:
raise "You have set a wrong path to the JSON file"
return result_prediction, prediction_probabilities
elif (self.__modelType == "resnet"):
model = self.__model_collection[0]
try:
from .ResNet.imagenet_utils import preprocess_input, decode_predictions
target_image = image.load_img(image_path,
grayscale=False,
target_size=(224, 224))
target_image = image.img_to_array(
target_image, data_format="channels_last")
target_image = np.expand_dims(target_image, axis=0)
target_image = preprocess_input(
target_image, data_format="channels_last")
except:
raise "You have set a path to an invalid image file."
prediction = model.predict(x=target_image, steps=1)
try:
predictiondata = decode_predictions(
prediction,
top=int(result_count),
index_file_path=self.jsonPath)
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
result_prediction.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
except:
raise "You have set a wrong path to the JSON file"
return result_prediction, prediction_probabilities
elif (self.__modelType == "densenet"):
model = self.__model_collection[0]
try:
from .DenseNet.densenet import DenseNetImageNet121, preprocess_input, decode_predictions
from .DenseNet.densenet import DenseNetImageNet121
image_to_predict = image.load_img(image_path,
grayscale=False,
target_size=(224, 224))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(
image_to_predict, data_format="channels_last")
except:
raise "You have set a path to an invalid image file."
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction,
top=int(result_count),
index_file_path=self.jsonPath)
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
result_prediction.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
except:
raise "You have set a wrong path to the JSON file"
return result_prediction, prediction_probabilities
elif (self.__modelType == "inceptionv3"):
model = self.__model_collection[0]
try:
from imageai.Prediction.InceptionV3.inceptionv3 import InceptionV3
from imageai.Prediction.InceptionV3.inceptionv3 import preprocess_input, decode_predictions
image_to_predict = image.load_img(image_path,
grayscale=False,
target_size=(299, 299))
image_to_predict = image.img_to_array(
image_to_predict, data_format="channels_last")
image_to_predict = np.expand_dims(image_to_predict, axis=0)
image_to_predict = preprocess_input(image_to_predict)
except:
raise "You have set a path to an invalid image file."
prediction = model.predict(x=image_to_predict, steps=1)
try:
predictiondata = decode_predictions(
prediction,
top=int(result_count),
index_file_path=self.jsonPath)
for results in predictiondata:
countdown = 0
for result in results:
countdown += 1
result_prediction.append(str(result[1]))
prediction_probabilities.append(
str(result[2] * 100))
except:
raise "You have set a wrong path to the JSON file"
return result_prediction, prediction_probabilities