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 main():
print('Initializing main function')
# Initializing instances
instance_pose = ClassOpenPose()
# Withdrawing Tk window
Tk().withdraw()
# Loading folder from element in list
init_dir = '/home/mauricio/Pictures/Poses'
options = {'initialdir': init_dir}
dir_name = filedialog.askdirectory(**options)
if not dir_name:
print('Directory not selected')
else:
print('Selected dir: {0}'.format(dir_name))
list_files = os.listdir(dir_name)
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
index = 0
print('Press arrows to move, ESC to exit')
while True:
file = list_files[index]
full_path = os.path.join(dir_name, file)
print('Loading image {0}'.format(full_path))
image = cv2.imread(full_path)
arr, pro_img = instance_pose.recognize_image_tuple(image)
cv2.imshow('main_window', pro_img)
key = cv2.waitKey(0)
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
break
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')
print('Initializing instance')
instance = ClassOpenPose()
print('Reading image')
image = cv2.imread('/home/mauricio/Pictures/2_2.jpg')
image2 = cv2.imread('/home/mauricio/Pictures/430.jpg')
print('Recognizing image 1')
arr, img_open = instance.recognize_image_tuple(image)
print(arr)
cv2.namedWindow('main_window', cv2.WINDOW_AUTOSIZE)
cv2.imshow('main_window', img_open)
cv2.waitKey()
print('Recognizing image 2')
arr = instance.recognize_image(image2)
print(arr)
print('Done generating elements!')
def main():
print('Generating angle descriptors')
# Initializing instances
instance_pose = ClassOpenPose()
# Reading pose from dir path
image = '/home/mauricio/Pictures/walk.jpg'
if not os.path.exists(image):
print('The path {0} does not exists'.format(image))
else:
img_cv = cv2.imread(image)
# Forwarding image
arr, output_img = instance_pose.recognize_image_tuple(img_cv)
person_array = arr[0]
# Generating other descriptors
# Assume image is OK
shoulder_dis = ClassUtils.get_euclidean_distance_pt(person_array[1], person_array[2]) + \
ClassUtils.get_euclidean_distance_pt(person_array[1], person_array[5])
torso_dis = ClassUtils.get_euclidean_distance_pt(person_array[1], person_array[8])
# In total, we have a vector with 8 angles
# We need to extract the characteristics of the 8 angles
relation = shoulder_dis / torso_dis
print(relation)
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow('main_window', output_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
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():
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!')
