def pre_process_images(list_folders_scores, recalculate):
print('Start pre_processing images')
# Loading instances
instance_pose = ClassOpenPose()
for folder, min_score in list_folders_scores:
for file in os.listdir(folder):
full_path = os.path.join(folder, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension != '.jpg':
print('Ignoring file {0}'.format(full_path))
continue
file_no_ext = ClassUtils.get_filename_no_extension(full_path)
arr_file_name = os.path.join(folder, '{0}.json'.format(file_no_ext))
# If image recalculation
if not recalculate:
if os.path.isfile(arr_file_name):
print('File already processed {0}'.format(full_path))
continue
# Processing file
print('Processing file {0}'.format(full_path))
image = cv2.imread(full_path)
arr, img_draw = instance_pose.recognize_image_tuple(image)
arr_pass = list()
# 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_part(elem, min_score):
arr_pass.append(elem)
# If there is more than one person with vector integrity
if len(arr_pass) != 1:
for elem in arr_pass:
pt1, pt2 = ClassUtils.get_rectangle_bounds(elem, ClassUtils.MIN_POSE_SCORE)
cv2.rectangle(img_draw, pt1, pt2, (0, 0, 255), 3)
cv2.namedWindow('main_window')
cv2.imshow('main_window', img_draw)
print(arr)
print(arr_pass)
cv2.waitKey(0)
cv2.destroyAllWindows()
raise Exception('Invalid len: {0} file {1}'.format(len(arr_pass), full_path))
person_arr = arr_pass[0]
arr_str = json.dumps(person_arr.tolist())
with open(arr_file_name, 'w') as text_file:
text_file.write(arr_str)
print('Done!')
def get_poses_seq(folder: str,
instance_nn: ClassNN,
instance_pose: ClassOpenPose,
only_json=False):
# List all folders
list_files = []
for file in os.listdir(folder):
list_files.append(os.path.join(folder, file))
# Sorting elements
list_files.sort()
# Get elements
list_desc = list()
for path in list_files:
ext = ClassUtils.get_filename_extension(path)
if only_json:
if ext != '.json':
print('Ignoring file: {0}'.format(path))
continue
with open(path, 'r') as file:
person_arr_str = file.read()
person_arr = json.loads(person_arr_str)
else:
if ext != '.jpg':
print('Ignoring file: {0}'.format(path))
continue
print('Processing file {0}'.format(path))
image = cv2.imread(path)
arr = instance_pose.recognize_image(image)
arr_pass = []
for person_arr in arr:
if ClassUtils.check_vector_integrity_part(
person_arr, min_score):
arr_pass.append(person_arr)
if len(arr_pass) != 1:
print('Ignoring file {0} - Len arr_pass: {1}'.format(
path, len(arr_pass)))
continue
person_arr = arr_pass[0]
result_desc = ClassDescriptors.get_person_descriptors(
person_arr, min_score)
list_desc.append(result_desc['fullDesc'])
list_desc_np = np.asarray(list_desc, np.float)
results = instance_nn.predict_model_array(list_desc_np)
list_classes = []
for result in results:
list_classes.append(result['classes'])
return list_classes
def get_sets_folder(base_folder, label, instance_pose: ClassOpenPose):
list_files = os.listdir(base_folder)
# Work 70, 30
total_files = len(list_files)
training = int(total_files * 70 / 100)
tr_features = list()
tr_labels = list()
eval_features = list()
eval_labels = list()
for index, file in enumerate(list_files):
full_name = os.path.join(base_folder, file)
image = cv2.imread(full_name)
if image is None:
raise Exception('Error reading image: {0}'.format(full_name))
print('Processing image: {0}'.format(full_name))
arr = instance_pose.recognize_image(image)
# Selecting array for list of vectors
person_array = []
if len(arr) == 0:
raise Exception('Invalid len for image {0}: {1}'.format(
full_name, len(arr)))
elif len(arr) == 1:
person_array = arr[0]
integrity = ClassUtils.check_vector_integrity_part(
person_array, min_score)
if not integrity:
raise Exception(
'Invalid integrity for points in image: {0}'.format(
full_name))
else:
found = False
for arr_index in arr:
integrity = ClassUtils.check_vector_integrity_part(
arr_index, min_score)
if integrity:
found = True
person_array = arr_index
break
if not found:
raise Exception(
'Cant find a valid person array for image {0}'.format(
full_name))
results = ClassDescriptors.get_person_descriptors(
person_array, min_score)
if index < training:
tr_features.append(results['angles'])
tr_labels.append(label)
else:
eval_features.append(results['angles'])
eval_labels.append(label)
return tr_features, tr_labels, eval_features, eval_labels
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')
# Initializing instances
instance_pose = ClassOpenPose()
# Withdrawing tkinter
Tk().withdraw()
# Loading folder images
folder_images = '/home/mauricio/PosesProcessed/folder_images'
folder_images_draw = '/home/mauricio/PosesProcessed/folder_images_draw'
pose_base_folder = '/home/mauricio/Pictures/PosesNew'
# Reading all elements in list
list_files = sorted(os.listdir(folder_images_draw))
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
index = 0
while True:
file = list_files[index]
full_path_draw = os.path.join(folder_images_draw, file)
image_draw = cv2.imread(full_path_draw)
cv2.imshow('main_window', image_draw)
key = cv2.waitKey(0)
print('Key pressed: {0}'.format(key))
if key == 52:
# Left arrow
index -= 1
if index < 0:
index = 0
elif key == 54:
# Right arrow
index += 1
if index == len(list_files):
index = len(list_files) - 1
elif key == 27:
# Esc
# Ask are you sure question
result = messagebox.askyesno("Exit", "Are you sure to exit?")
print(result)
if result:
break
elif key == 115:
# Check JSON file
image_full_path = os.path.join(folder_images, file)
json_path = image_full_path.replace(".jpg", ".json")
if not os.path.exists(json_path):
# Generating JSON array
image = cv2.imread(image_full_path)
arr = instance_pose.recognize_image(image).tolist()
else:
with open(json_path, 'r') as file:
arr_str = file.read()
arr = json.load(arr_str)
arr_pass = []
min_score = 0.05
# Drawing candidates
for elem in arr:
if ClassUtils.check_vector_integrity_part(elem, min_score):
arr_pass.append(elem)
if len(arr_pass) == 0:
print('Arr pass size equals to zero')
continue
elif len(arr_pass) == 1:
selection_int = 0
person_arr = arr_pass[0]
arr_str = json.dumps(person_arr)
else:
# Draw rectangles for all candidates
for index_elem, person_arr in enumerate(arr_pass):
pt1, pt2 = ClassUtils.get_rectangle_bounds(person_arr, min_score)
cv2.rectangle(image_draw, pt1, pt2, (0, 0, 255), 5)
font = cv2.FONT_HERSHEY_SIMPLEX
bottom_left_corner = pt2
font_scale = 0.6
font_color = (255, 255, 255)
line_type = 2
cv2.putText(image_draw, '{0}'.format(index_elem),
bottom_left_corner,
font,
font_scale,
font_color,
line_type)
while True:
print('Select image to put')
cv2.imshow('main_window', image_draw)
wait_key = cv2.waitKey(0)
print('Wait Key: {0}'.format(wait_key))
selection_int = ClassUtils.key_to_number(wait_key)
print('Selection: {0}'.format(selection_int))
if 0 <= selection_int < len(arr_pass):
break
person_arr = arr_pass[selection_int]
arr_str = json.dumps(person_arr)
# Getting new image using numpy slicing
pt1, pt2 = ClassUtils.get_rectangle_bounds(person_arr, min_score)
image_draw = image_draw[pt1[1]:pt2[1], pt1[0]:pt2[0]]
# Selecting directory after processing image!
init_dir = '/home/mauricio/Pictures/PosesNew'
options = {'initialdir': init_dir}
dir_name = filedialog.askdirectory(**options)
if not dir_name:
print('Directory not selected')
else:
new_name = ClassUtils.get_filename_no_extension(file)
new_name = "{0}_{1}.{2}".format(new_name, selection_int, 'jpg')
new_image_path = os.path.join(dir_name, new_name)
new_json_path = os.path.join(dir_name, new_name.replace('.jpg', '.json'))
cv2.imwrite(new_image_path, image_draw)
with open(new_json_path, 'w') as file:
file.write(arr_str)
print('File copied from {0} to {1}'.format(full_path_draw, new_image_path))
index += 1
if index == len(list_files):
index = len(list_files) - 1
cv2.destroyAllWindows()
print('Done!')