def _obtain_y_pred(self,
prob_threshold=0.75):
y_pred = []
y_scores = []
predicted_class_list = []
actual_class_list = []
coverage_count = 0
for hra_class in self.sorted_categories_names:
# variable that contains the main dir alongside the selected category
tmp = os.path.join(self.main_test_dir, hra_class)
img_names = sorted(os.listdir(tmp))
for raw_img in img_names:
# variable that contains the final image to be loaded
print(' Processing [' + raw_img + ']')
final_img = os.path.join(tmp, raw_img)
preds = displaceNet_inference(img_path=final_img,
emotic_model_a_backend_name=self.emotic_model_a_backend_name,
emotic_model_b_backend_name=self.emotic_model_b_backend_name,
emotic_model_c_backend_name=self.emotic_model_c_backend_name,
hra_model_backend_name=self.hra_model_backend_name,
nb_of_fine_tuned_conv_layers=self.nb_of_conv_layers_to_fine_tune,
violation_class=self.violation_class)
preds = preds[0]
y_pred.append(int(preds[0][0]))
y_scores.append(preds[0][2])
top_1_predicted_probability = preds[0][2]
# top_1_predicted = np.argmax(preds)
top_1_predicted_label = preds[0][1]
if top_1_predicted_probability >= prob_threshold:
coverage_count += 1
# print ('`' + hra_class + '/' + raw_img + '` ===> `' +
# top_1_predicted_label + '`' + ' with ' + str(top_1_predicted_probability) + ' P')
print(' GT `' + hra_class + '`' + '` <--> ` Pred. `' +
top_1_predicted_label + '`' + ' with ' + str(top_1_predicted_probability))
print ('\n')
predicted_class_list.append(top_1_predicted_label)
actual_class_list.append(hra_class)
total_coverage_per = (coverage_count * 100) / self.total_nb_of_test_images
return y_pred, self.y_true, y_scores, total_coverage_per
model_backend_name=hra_model_backend_name,
nb_of_conv_layers_to_fine_tune=nb_of_conv_layers_to_fine_tune)
img = image.load_img(img_path, target_size=(224, 224))
print ('Vanilla CNN prediction: ', raw_preds[0])
emotic_model_a_backend_name = 'VGG16'
emotic_model_b_backend_name = None
emotic_model_c_backend_name = None
final_preds = displaceNet_inference(img_path,
emotic_model_a_backend_name,
emotic_model_b_backend_name,
emotic_model_c_backend_name,
hra_model_backend_name,
nb_of_conv_layers_to_fine_tune,
violation_class)
print('DisplaceNet prediction: ', final_preds)
numpy_img_path = imread(img_path)
plt.figure(figsize=(10, 12))
plt.imshow(numpy_img_path)
current_axis = plt.gca()
# configure colours for bounding box and text
'''
'''
from __future__ import print_function
import argparse
from inference.displacenet_single_image_inference_unified import displaceNet_inference
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--img_path", type = str,help = 'path of the input image')
parser.add_argument("--hra_model_backend_name", type = str,help = 'One of `VGG16`, `VGG19`, `ResNet50` or `VGG16_Places365`')
parser.add_argument("--emotic_model_backend_name", type=str, help='One of `VGG16`, `VGG19` or `ResNet50`')
parser.add_argument("--nb_of_conv_layers_to_fine_tune", type=int, help='indicates the number of convolutional layers that were fine-tuned during training')
args = parser.parse_args()
return args
args = get_args()
DisplaceNet_preds = displaceNet_inference(img_path=args.img_path,
emotic_model_a_backend_name=args.emotic_model_backend_name,
emotic_model_b_backend_name=None,
emotic_model_c_backend_name=None,
hra_model_backend_name=args.hra_model_backend_name,
nb_of_fine_tuned_conv_layers=args.nb_of_conv_layers_to_fine_tune,
violation_class='dp')
print (DisplaceNet_preds)
print (DisplaceNet_preds[0])