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 test_color_compare_hist(perform_eq=False):
print('Test color comparision')
print('Loading image comparision')
# Loading instances
instance_pose = ClassOpenPose()
ignore_json_color = False
if perform_eq:
ignore_json_color = True
# Avoid to open two prompts
obj_img = ClassDescriptors.load_images_comparision_ext(
instance_pose,
min_score,
load_one_img=True,
perform_eq=perform_eq,
ignore_json_color=ignore_json_color)
# Extract color comparision from image
hist1 = obj_img['listPoints1']
# Generating examples folder
list_process = list()
for root, _, files in os.walk(EXAMPLES_FOLDER):
for file in files:
full_path = os.path.join(root, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension == '.jpg':
list_process.append(full_path)
# Sorting list
list_process.sort()
"""
list_result = list()
score_max_pt = -1
"""
for full_path in list_process:
print('Processing file: {0}'.format(full_path))
json_path = full_path.replace('.jpg', '.json')
with open(json_path, 'r') as f:
obj_json = json.loads(f.read())
image2 = cv2.imread(full_path)
if perform_eq:
image2 = ClassUtils.equalize_hist(image2)
if 'vector' in obj_json:
pose2 = obj_json['vector']
elif 'vectors' in obj_json:
pose2 = obj_json['vectors']
else:
raise Exception('Invalid vector property for vector custom')
hist2 = ClassDescriptors.get_points_by_pose(image2, pose2, min_score)
diff = ClassDescriptors.get_kmeans_diff(hist1, hist2)
# Getting mean y from image 2 - discarding purposes
pt1, pt2 = ClassUtils.get_rectangle_bounds(pose2, min_score)
image2_crop = image2[pt1[1]:pt2[1], pt1[0]:pt2[0]]
image2_ycc = cv2.cvtColor(image2_crop, cv2.COLOR_BGR2YCrCb)
mean_y = np.mean(image2_ycc[:, :, 0])
print('Diff color: {0} - Mean y: {1}'.format(diff, mean_y))
"""
list_result.sort(key=lambda x: x['score'])
print('Printing list result')
print(list_result)
print('min_score: {0}'.format(score_max_pt))
"""
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!')
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!')
