def test_recognition_model_squeezenet():
try:
keras.backend.clear_session()
except:
None
predictor = ImagePrediction()
predictor.setModelTypeAsSqueezeNet()
predictor.setModelPath(
os.path.join(main_folder, "data-models",
"squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
predictor.loadModel()
images_to_image_array()
result_array = predictor.predictMultipleImages(
sent_images_array=all_images_array)
assert isinstance(result_array, list)
for result in result_array:
assert "predictions" in result
assert "percentage_probabilities" in result
assert isinstance(result["predictions"], list)
assert isinstance(result["percentage_probabilities"], list)
assert isinstance(result["predictions"][0], str)
assert isinstance(result["percentage_probabilities"][0], float)
def multiple_predict(threshold):
execution_path = os.getcwd()
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeAsInceptionV3()
multiple_prediction.setModelPath(
os.path.join(execution_path,
"inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
multiple_prediction.loadModel()
all_images_array = []
all_files = os.listdir(execution_path + "/adian")
for each_file in all_files:
if (each_file.endswith(".jpg") or each_file.endswith(".png")):
all_images_array.append("adian/" + each_file)
results_array = multiple_prediction.predictMultipleImages(
all_images_array, result_count_per_image=1)
objects = []
for each_result in results_array:
predictions, percentage_probabilities = each_result[
"predictions"], each_result["percentage_probabilities"]
for index in range(len(predictions)):
print(predictions[index], " : ", percentage_probabilities[index])
prob = percentage_probabilities[index]
if prob > threshold:
objects.append(predictions[index])
#print("-----------------------")
# Remove duplicates
#objects = list(dict.fromkeys(objects))
objects = list(set(objects))
supported_list = [
'banana', 'orange', 'mixing_bowl', 'soup_bowl', 'washbowl',
'washbasin', 'handbasin', 'lavabo', 'wash-hand basin', 'custard apple',
'pomegranate', 'beer_bottle', 'pop_bottle', 'soda_bottle',
'water_bottle', 'wine_bottle', 'sundial', 'bathtub', 'mortar', 'orange'
]
objects = [i for i in objects if i in supported_list]
objects = [
i if i not in ['pomegranate', 'custard apple', 'sundial'] else 'apple'
for i in objects
]
objects = [i if i not in ['lemon'] else 'orange' for i in objects]
objects = [
i if i not in ['pop_bottle', 'soda_bottle', 'wine_bottle'] else
'water_bottle' for i in objects
]
objects = [
i if i not in [
'soup_bowl', 'washbowl', 'washbasin', 'handbasin', 'bathtub',
'mortar'
] else 'mixing_bowl' for i in objects
]
objects = list(set(objects))
return objects
def test_recognition_model_densenet():
predictor = ImagePrediction()
predictor.setModelTypeAsDenseNet()
predictor.setModelPath(
os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
predictor.loadModel()
images_to_image_array()
result_array = predictor.predictMultipleImages(
sent_images_array=all_images_array)
assert isinstance(result_array, list)
for result in result_array:
assert "predictions" in result
assert "percentage_probabilities" in result
assert isinstance(result["predictions"], list)
assert isinstance(result["percentage_probabilities"], list)
assert isinstance(result["predictions"][0], str)
assert isinstance(result["percentage_probabilities"][0], float)
def test_recognition_model_inceptionv3():
predictor = ImagePrediction()
predictor.setModelTypeAsInceptionV3()
predictor.setModelPath(
os.path.join(main_folder, "data-models",
"inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
predictor.loadModel()
images_to_image_array()
result_array = predictor.predictMultipleImages(
sent_images_array=all_images_array)
assert isinstance(result_array, list)
for result in result_array:
assert "predictions" in result
assert "percentage_probabilities" in result
assert isinstance(result["predictions"], list)
assert isinstance(result["percentage_probabilities"], list)
assert isinstance(result["predictions"][0], str)
assert isinstance(result["percentage_probabilities"][0], float)
def main(path_model, path_dir):
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeAsResNet()
multiple_prediction.setModelPath(path_model)
multiple_prediction.loadModel()
all_images_array = []
all_files = os.listdir(path_dir)
for each_file in all_files:
if each_file.endswith(".jpg" or each_file.endswith(".png")):
all_images_array.append(each_file)
results_array = multiple_prediction.predictMultipleImages(
all_images_array, result_count_per_image=5)
for each_result in results_array:
for idx in range(len(each_result["predictions"])):
print(each_result["predictions"][idx], " : ",
each_result["percentage_probabilities"][idx])
print("-----------------------")
from imageai.Prediction import ImagePrediction
import os
execution_path = os.getcwd()
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeAsResNet()
multiple_prediction.setModelPath(
os.path.join(execution_path,
"resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
multiple_prediction.loadModel()
all_images_array = []
all_files = os.listdir(execution_path)
for each_file in all_files:
if (each_file.endswith(".jpg") or each_file.endswith(".png")):
all_images_array.append(each_file)
results_array = multiple_prediction.predictMultipleImages(
all_images_array, result_count_per_image=4)
for each_result in results_array:
predictions, percentage_probabilities = each_result[
"predictions"], each_result["percentage_probabilities"]
for index in range(len(predictions)):
print(predictions[index], " : ", percentage_probabilities[index])
print("-----------------------")
#Multiple image prediction
#
from imageai.Prediction import ImagePrediction
import os
working_path = os.getcwd()
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeResNet()
multiple_prediction.setModelPath(os.path.join(working_path,
"resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
multiple_prediction.loadModel(prediction_speed="fast")
all_images = []
all_files = os.listdir(working_path)
for file in all_files:
if(file.endswith(".jpg") or file.endswith(".png")):
all_images.append(file)
final_array = multiple_prediction.predictMultipleImages(all_images, result_count_per_image = 5)
#Display the output
for each_result in final_array:
pred , percent = each_result["pred"],each_result["percent"]
for i in range(len(pred)):
print(pred[i]," : ", percent[i])
#Initializxe a text file to write to
def write_to_txt(line):
with open('results.txt', mode='a') as output:
output.write(line)
for model_abs_path in models_abs_paths:
multiple_prediction = ImagePrediction()
if 'squeezenet' in model_abs_path.lower():
try:
multiple_prediction.setModelTypeAsSqueezeNet()
multiple_prediction.setModelPath(model_abs_path)
multiple_prediction.loadModel()
results_array = multiple_prediction.predictMultipleImages(
image_files, result_count_per_image=result_count)
# print(f"results_array: {results_array}")
results_array_idx = 0
for each_result in results_array:
predictions, percentage_probabilities = each_result[
"predictions"], each_result["percentage_probabilities"]
line = (
f"\n\nThe SqueezeNet model's top {result_count} predictions with probability for {image_files[results_array_idx]}: "
)
print(line, end='')
write_to_txt(line)
for index in range(len(predictions)):
line = f"\n{predictions[index]}: {percentage_probabilities[index]}"
print(line, end='')
write_to_txt(line)
print("-----------------------")
output_image_path=output_path)
for eachItem in detection:
print(eachItem["name"], " : ", eachItem["percentage_probability"])
current_dir = os.getcwd()
image_predictions = ImagePrediction()
image_predictions.setModelTypeAsResNet()
image_predictions.setModelPath(
os.path.join(current_dir,
"resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
image_predictions.loadModel()
images_array = list(
filter(lambda x: x.endswith(".jpg") or x.endswith(".png"),
os.listdir(current_dir)))
results_array = image_predictions.predictMultipleImages(
images_array, result_count_per_image=10)
def consolidate_prediction_results(results):
list_of_prediction = []
for each_result in results:
predictions, percentage_probabilities = each_result[
"predictions"], each_result["percentage_probabilities"]
list_of_prediction.append(predictions)
for index in range(len(predictions)):
print(predictions[index], " : ", percentage_probabilities[index])
print("______________________________")
print('set_of_prediction:',
set([item for items in list_of_prediction for item in items]))
