def reprocess_images(list_folder_data):
print('Init reprocess_images')
for index, item in enumerate(list_folder_data):
folder = item[0]
min_score = item[1]
print('Processing folder: {0}'.format(folder))
list_files = os.listdir(folder)
random.Random(seed).shuffle(list_files)
for num_file, filename in enumerate(list_files):
file = os.path.join(folder, filename)
extension = ClassUtils.get_filename_extension(file)
if extension == '.json':
with open(file, 'r') as f:
data_str = f.read()
data_json = json.loads(data_str)
if 'vectors' in data_json:
print('Processing json file with new format: {0}'.format(file))
person_arr = data_json['vectors']
else:
print('Processing json file: {0}'.format(file))
person_arr = data_json
valid = ClassUtils.check_vector_integrity_pos(person_arr, min_score)
only_pos = ClassUtils.check_vector_only_pos(person_arr, min_score)
if not valid:
raise Exception('Vector integrity not valid for file: {0}'.format(file))
if only_pos:
raise Exception('Invalid vector to perform detection')
descriptors = ClassDescriptors.get_person_descriptors(person_arr, min_score, cam_number=0,
image=None, calib_params=None,
decode_img=False,
instance_nn_pose=None)
with open(file, 'w') as f:
f.write(json.dumps(descriptors))
transformed_points = descriptors['transformedPoints']
# Save pose for debugging purposes
re_scale_factor = 100
new_points = ClassDescriptors.re_scale_pose_factor(transformed_points,
re_scale_factor, min_score)
img_pose = ClassDescriptors.draw_pose_image(new_points, min_score, is_transformed=True)
new_file_name = ClassUtils.get_filename_no_extension(file) + '_1.jpg'
cv2.imwrite(new_file_name, img_pose)
print('Done!')
def process_single():
print('Initializing process_single')
# Loading instances
instance_pose = ClassOpenPose()
# Opening filename
init_dir = '/home/mauricio/Pictures'
options = {'initialdir': init_dir}
filename = askopenfilename(**options)
if not filename:
filename = '/home/mauricio/Pictures/Poses/left_walk/636550788328999936_420.jpg'
# Reading video extension
extension = os.path.splitext(filename)[1]
if extension != '.jpg' and extension != '.jpeg':
raise Exception('Extension is not jpg or jpeg')
# Opening filename
image = cv2.imread(filename)
# Loading image and array
arr, processed_img = instance_pose.recognize_image_tuple(image)
# Showing image to generate elements
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
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('There is more than one person in the image')
cv2.imshow('main_window', processed_img)
cv2.waitKey(0)
else:
person_array = arr_pass[0]
print('Person array: {0}'.format(person_array))
generate_descriptors(person_array, image, processed_img, min_score)
def main():
print('Initializing main function')
Tk().withdraw()
# Loading instances
instance_pose = ClassOpenPose()
# Loading filename
init_dir = '/home/mauricio/Pictures/TestPlumb'
options = {'initialdir': init_dir}
filename = askopenfilename(**options)
if not filename:
filename = '/home/mauricio/Pictures/419.jpg'
print('Filename: {0}'.format(filename))
image = cv2.imread(filename)
arr = instance_pose.recognize_image(image)
valid_arr = list()
for person_arr in arr:
if ClassUtils.check_vector_integrity_pos(person_arr, min_score):
valid_arr.append(person_arr)
if len(valid_arr) != 1:
raise Exception('Invalid len for arr: {0}'.format(len(valid_arr)))
person_arr = valid_arr[0]
ClassDescriptors.draw_pose(image, person_arr, min_score)
cv2.namedWindow('main_window', cv2.WINDOW_AUTOSIZE)
cv2.imshow('main_window', image)
print_distances(person_arr)
cv2.waitKey()
cv2.destroyAllWindows()
print('Done!')
def load_pose_descriptors(cls, type_desc: EnumDesc):
training_data = list()
training_labels = list()
training_files = list()
eval_data = list()
eval_labels = list()
eval_files = list()
classes_number = 0
if type_desc == EnumDesc.ANGLES or type_desc == EnumDesc.ANGLES_TRANSFORMED:
data_folder = cls.list_folder_data_angles
else:
data_folder = cls.list_folder_data
cont = True
while cont:
cont = False
for folder_data in data_folder:
if folder_data[2] == classes_number:
classes_number += 1
cont = True
break
# Iterate folder
for index, item in enumerate(data_folder):
folder = item[0]
min_score = item[1]
label = item[2]
list_files = os.listdir(folder)
random.Random(cls.seed).shuffle(list_files)
total_train = int(len(list_files)) * 70 / 100
for num_file, file in enumerate(list_files):
full_path = os.path.join(folder, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension != '.json':
print('Ignoring file {0}'.format(full_path))
continue
with open(full_path, 'r') as text_file:
arr_json = text_file.read()
params = json.loads(arr_json)
vectors = params['vectors']
angles = params['angles']
transformed_points = params['transformedPoints']
valid = ClassUtils.check_vector_integrity_pos(
vectors, min_score)
only_pos = ClassUtils.check_vector_only_pos(vectors, min_score)
if not valid:
raise Exception(
'Vector integrity not valid for file: {0}'.format(
full_path))
if only_pos:
raise Exception('Invalid vector to perform detection')
# Fill training and eval list
# Use angles and position information
data_to_add = cls._get_descriptor_list(angles,
transformed_points,
type_desc)
if num_file < total_train:
training_data.append(data_to_add)
training_labels.append(label)
training_files.append(full_path)
else:
eval_data.append(data_to_add)
eval_labels.append(label)
eval_files.append(full_path)
# Convert data to numpy array
training_data_np = np.asanyarray(training_data, dtype=np.float)
training_labels_np = np.asanyarray(training_labels, dtype=int)
print(training_labels_np)
eval_data_np = np.asanyarray(eval_data, dtype=np.float)
eval_labels_np = np.asanyarray(eval_labels, dtype=int)
print(eval_labels_np)
training_files_np = np.asanyarray(training_files, dtype=np.str)
eval_files_np = np.asanyarray(eval_files, dtype=np.str)
# Getting label_names
label_names = []
for folder, _, label in data_folder:
names = folder.split('/')
label_name = names[-1]
# Check if last character is /
if len(label_name) == 0:
label_names = names[-2]
label_names.append((label_name, label))
print('Total training: {0}'.format(len(training_labels)))
print('Total eval: {0}'.format(len(eval_labels)))
print('Shape training: {0}'.format(training_data_np.shape))
print('Shape eval: {0}'.format(eval_data_np.shape))
print('Classes number: {0}'.format(classes_number))
results = {
'trainingData': training_data_np,
'trainingLabels': training_labels_np,
'evalData': eval_data_np,
'evalLabels': eval_labels_np,
'trainingFiles': training_files_np,
'evalFiles': eval_files_np,
'labelNames': label_names,
'classesNumber': classes_number
}
return results
def main():
print('Initializing main function')
Tk().withdraw()
# filename1 = '/home/mauricio/Pictures/Poses/temp/636550787632290048_419.jpg'
# filename2 = '/home/mauricio/Pictures/Poses/walk_front/636550801813440000_424.jpg'
filename1 = '/home/mauricio/Pictures/Poses/bend_left/636453039344460032_1.jpg'
filename2 = '/home/mauricio/Pictures/Poses/left_walk/636550795366450048_420.jpg'
print('Select first file')
init_dir = '/home/mauricio/Pictures/Poses'
options = {'initialdir': init_dir}
filename1_tmp = askopenfilename(**options)
if filename1_tmp:
filename1 = filename1_tmp
print('File selected: {0}'.format(filename1))
print('Select second file')
filename2_tmp = askopenfilename(**options)
if filename2_tmp:
filename2 = filename2_tmp
print('File selected: {0}'.format(filename2))
img1 = cv2.imread(filename1)
img2 = cv2.imread(filename2)
instance_pose = ClassOpenPose()
vectors1, img_draw1 = instance_pose.recognize_image_tuple(img1)
vectors2, img_draw2 = instance_pose.recognize_image_tuple(img2)
if len(vectors1) != 1:
raise Exception('Invalid len for vector 1')
if len(vectors2) != 1:
raise Exception('Invalid len for vector 2')
person_vector1 = vectors1[0]
person_vector2 = vectors2[0]
min_percent = 0.05
if not ClassUtils.check_vector_integrity_pos(person_vector1, min_percent):
raise Exception('Invalid integrity for vector 1')
if not ClassUtils.check_vector_integrity_pos(person_vector2, min_percent):
raise Exception('Invalid integrity for vector 2')
colors1 = ClassDescriptors.process_colors(vectors1, min_percent, img1, decode_img=False)
colors2 = ClassDescriptors.process_colors(vectors2, min_percent, img2, decode_img=False)
upper1 = colors1[0][0]
lower1 = colors1[1][0]
color_diff1 = ClassUtils.get_color_diff_rgb(upper1, lower1)
upper2 = colors2[0][0]
lower2 = colors2[1][0]
color_diff2 = ClassUtils.get_color_diff_rgb(upper2, lower2)
diff_upper = ClassUtils.get_color_diff_rgb(upper1, upper2)
diff_lower = ClassUtils.get_color_diff_rgb(lower1, lower2)
diff_diff = math.fabs(color_diff1 - color_diff2)
print('Diff upper: {0}'.format(diff_upper))
print('Diff lower: {0}'.format(diff_lower))
print('Diff diffs: {0}'.format(diff_diff))
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow('main_window', np.hstack((img_draw1, img_draw2)))
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
def main():
Tk().withdraw()
instance_pose = ClassOpenPose()
print('Initializing main function')
list_files1 = os.listdir(base1_folder)
hists_1 = read_hists(base1_folder, list_files1)
list_files2 = os.listdir(base2_folder)
hists_2 = read_hists(base2_folder, list_files2)
cum_hists_1 = ClassDescriptors.get_cumulative_hists(hists_1)
cum_hists_2 = ClassDescriptors.get_cumulative_hists(hists_2)
# Processing img
filename1 = '/home/mauricio/Pictures/Poses/bend_left/636453039344460032_1.jpg'
filename2 = '/home/mauricio/Pictures/Poses/left_walk/636550795366450048_420.jpg'
init_dir = '/home/mauricio/Pictures'
options = {'initialdir': init_dir}
filename1 = askopenfilename(**options)
if not filename1:
filename1 = '/home/mauricio/Pictures/2_1.jpg'
ext1 = os.path.splitext(filename1)[1]
if ext1 != '.jpg' and ext1 != '.jpeg':
raise Exception('Extension1 is not jpg or jpeg')
print('Loading filename 2')
filename2 = askopenfilename(**options)
if not filename2:
filename2 = '/home/mauricio/Pictures/2_2.jpg'
ext2 = os.path.splitext(filename2)[1]
if ext2 != '.jpg' and ext2 != '.jpeg':
raise Exception('Extension2 is not jpg or jpeg')
image1 = cv2.imread(filename1)
if image1 is None:
raise Exception('Invalid image in filename {0}'.format(filename1))
image2 = cv2.imread(filename2)
if image2 is None:
raise Exception('Invalid image in filename {0}'.format(filename2))
is_json = True
new_file_1 = filename1.replace('.jpeg', '.json')
new_file_1 = new_file_1.replace('.jpg', '.json')
new_file_2 = filename2.replace('.jpeg', '.json')
new_file_2 = new_file_2.replace('.jpg', '.json')
if not os.path.exists(new_file_1):
print('File not found: {0}'.format(new_file_1))
is_json = False
if not os.path.exists(new_file_2):
print('File not found: {0}'.format(new_file_2))
is_json = False
if not is_json:
poses1 = instance_pose.recognize_image(image1)
poses2 = instance_pose.recognize_image(image2)
if len(poses1) != 1:
raise Exception('Invalid len for pose1: {0}'.format(len(poses1)))
if len(poses2) != 1:
raise Exception('Invalid len for pose2: {0}'.format(len(poses2)))
if not ClassUtils.check_vector_integrity_pos(poses1[0], min_score):
raise Exception('Pose 1 not valid')
if not ClassUtils.check_vector_integrity_pos(poses2[0], min_score):
raise Exception('Pose 2 not valid')
pose1 = poses1[0]
pose2 = poses2[0]
else:
with open(new_file_1, 'r') as f:
obj_json1 = json.loads(f.read())
with open(new_file_2, 'r') as f:
obj_json2 = json.loads(f.read())
pose1 = obj_json1['vector']
pose2 = obj_json2['vector']
if not ClassUtils.check_vector_integrity_pos(pose1, min_score):
raise Exception('Pose 1 not valid')
if not ClassUtils.check_vector_integrity_pos(pose2, min_score):
raise Exception('Pose 2 not valid')
# Plotting
# plot_histograms(hists1)
# Showing first images without transformation
cv2.namedWindow('main_window', cv2.WINDOW_AUTOSIZE)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image2,
decode_img=False)
print('Upper1 {0} - Lower1 {0}'.format(upper1, lower1))
print('Upper2 {0} - Lower2 {0}'.format(upper2, lower2))
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image2)))
print('Press any key to continue')
cv2.waitKey(0)
# Perform image transformation
image_tr = ClassDescriptors.transform_image(image2, cum_hists_2,
cum_hists_1)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image_tr,
decode_img=False)
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image_tr)))
print('Press any key to continue')
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
def main():
print('Initializing main function')
print('Folder selection')
folder_images = '/home/mauricio/PosesProcessed/folder_images'
folder_images_draw = '/home/mauricio/PosesProcessed/folder_images_draw'
Tk().withdraw()
# Getting options
init_dir = '/home/mauricio/Videos/Oviedo'
options = {'initialdir': init_dir}
dir_name = askdirectory(**options)
if not dir_name:
raise Exception('Directory not selected')
# Create directory if does not exists
if not os.path.isdir(folder_images):
os.makedirs(folder_images)
# Create directory if does not exists
if not os.path.isdir(folder_images_draw):
os.makedirs(folder_images_draw)
# Initializing openpose instance
instance_pose = ClassOpenPose()
for root, subdirs, files in os.walk(dir_name):
for file in files:
full_path = os.path.join(root, file)
print('Processing {0}'.format(full_path))
extension = ClassUtils.get_filename_extension(full_path)
if extension == '.mjpeg':
file_info = ClassMjpegReader.process_video(full_path)
else:
print('Extension ignored: {0}'.format(extension))
continue
# Getting idcam from file
id_cam = full_path.split('/')[-2]
print('IdCam: {0}'.format(id_cam))
for index, info in enumerate(file_info):
print('Processing {0} of {1} from {2}'.format(index, len(file_info), full_path))
frame = info[0]
ticks = info[1]
image_np = np.frombuffer(frame, dtype=np.uint8)
image = cv2.imdecode(image_np, cv2.IMREAD_ANYCOLOR)
arr, image_draw = instance_pose.recognize_image_tuple(image)
min_score = 0.05
arr_pass = list()
for elem in arr:
if ClassUtils.check_vector_integrity_pos(elem, min_score):
arr_pass.append(elem)
if len(arr_pass) > 0:
# Draw rectangles for all candidates
for person_arr in arr_pass:
pt1, pt2 = ClassUtils.get_rectangle_bounds(person_arr, min_score)
cv2.rectangle(image_draw, pt1, pt2, (0, 0, 255), 5)
# Overwriting 1
full_path_images = os.path.join(folder_images, '{0}_{1}.jpg'.format(ticks, id_cam))
print('Writing image {0}'.format(full_path_images))
cv2.imwrite(full_path_images, image)
# Overwriting 2
full_path_draw = os.path.join(folder_images_draw, '{0}_{1}.jpg'.format(ticks, id_cam))
print('Writing image {0}'.format(full_path_images))
cv2.imwrite(full_path_draw, image_draw)
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!')
