def main():
print('Initializing main function')
# Withdrawing tkinter
# Loading model dirs
list_folder_data = [
('/home/mauricio/CNN/Classes/Door', 0.05),
('/home/mauricio/CNN/Classes/Tires', 0.05),
('/home/mauricio/CNN/Classes/Walk', 0.05),
]
list_hmm = []
for folder_data in list_folder_data:
label_name = get_label_from_folder(folder_data[0])
full_model_dir = os.path.join(hnn_model_folder,
'{0}.pkl'.format(label_name))
list_hmm.append(ClassHMM(full_model_dir))
# Initializing instances
instance_pose = ClassOpenPose()
instance_nn = ClassNN.load_from_params(nn_model_dir)
option = input('Select 1 to train, 2 to eval hmm, 3 to preprocess: ')
if option == '1':
print('Train hmm selection')
train_hmm(list_folder_data, list_hmm, instance_nn, instance_pose)
elif option == '2':
eval_hmm(list_folder_data, list_hmm, instance_nn, instance_pose)
elif option == '3':
recalculate = False
pre_process_images(instance_pose, list_folder_data, recalculate)
else:
print('Invalid argument: {0}'.format(option))
def main():
print('Initializing main function')
# Initializing instances
instance_pose = ClassOpenPose()
instance_net = ClassNN.load_from_params(model_dir)
# Withdrawing list
Tk().withdraw()
# Select directory to process
init_dir = '/home/mauricio/CNN/Images'
options = {'initialdir': init_dir}
dir_name = filedialog.askdirectory(**options)
if not dir_name:
print('Directory not selected')
else:
# Loading images
list_files = os.listdir(dir_name)
list_files.sort()
desc_list = list()
for file in list_files:
full_path = os.path.join(dir_name, file)
print('Processing image {0}'.format(full_path))
image = cv2.imread(full_path)
arr = instance_pose.recognize_image(image)
arr_pass = list()
for person_arr in arr:
if ClassUtils.check_vector_integrity_part(
person_arr, min_pose_score):
arr_pass.append(person_arr)
if len(arr_pass) != 1:
print('Invalid len {0} for image {1}'.format(
len(arr_pass), full_path))
continue
else:
result_des = ClassDescriptors.get_person_descriptors(
arr_pass[0], min_pose_score)
descriptor_arr = result_des['fullDesc']
# Add descriptors to list
desc_list.append(descriptor_arr)
# Convert to numpy array
print('Total poses: {0}'.format(len(desc_list)))
# Transform list and predict
desc_list_np = np.asarray(desc_list, dtype=np.float)
print('ndim pose list: {0}'.format(desc_list_np.ndim))
list_classes = list()
predict_results = instance_net.predict_model_array(desc_list_np)
for result in predict_results:
list_classes.append(result['classes'])
print('Predict results: {0}'.format(list_classes))
print('Classes label: {0}'.format(instance_net.label_names))
print('Done!')
def main():
print('Initializing main function')
# Prompt for user input
cam_number_str = input('Insert camera number to process: ')
cam_number = int(cam_number_str)
# Open video from opencv
cap = cv2.VideoCapture(cam_number)
# Initializing open pose distance
instance_pose = ClassOpenPose()
# Initializing variables
model_dir = '/home/mauricio/models/nn_classifier'
instance_nn = ClassNN.load_from_params(model_dir)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
# Processing frame with openpose
arr, frame = instance_pose.recognize_image_tuple(frame)
# Check if there is one frame with vector integrity
arr_pass = list()
min_score = 0.05
# Checking vector integrity for all elements
# Verify there is at least one arm and one leg
for elem in arr:
if ClassUtils.check_vector_integrity_pos(elem, min_score):
arr_pass.append(elem)
if len(arr_pass) != 1:
print('Invalid len for arr_pass: {0}'.format(arr_pass))
else:
person_array = arr_pass[0]
# Getting person descriptors
results = ClassDescriptors.get_person_descriptors(
person_array, min_score)
# Descriptors
data_to_add = results['angles']
data_to_add += ClassUtils.get_flat_list(
results['transformedPoints'])
data_np = np.asanyarray(data_to_add, dtype=np.float)
# Getting result predict
result_predict = instance_nn.predict_model(data_np)
detected_class = result_predict['classes']
label_class = get_label_name(instance_nn.label_names,
detected_class)
print('Detected: {0} - Label: {1}'.format(detected_class,
label_class))
# Draw text class into image - Evaluation purposes
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.6
font_color = (255, 255, 255)
line_type = 2
cv2.putText(frame, '{0}'.format(label_class), (0, 0), font,
font_scale, font_color, line_type)
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
print('Done!')