def create_table(self, table_name, csv_fname, skip_row_num=0, skip_col_num=0, auto_inc_pk_=True, show_=False):
if show_:
self.logger.info(" # Create \"{}\" table.".format(table_name))
utils.file_exists(csv_fname, exit_=True)
df = pd.read_csv(csv_fname, skiprows=skip_row_num)
df = df.drop(df.columns[[x for x in range(skip_col_num)]], axis=1)
sql = "CREATE TABLE " + table_name + " ("
if auto_inc_pk_:
sql += "id INT AUTO_INCREMENT PRIMARY KEY,"
sql += " {} {}".format(df.loc[0][0].strip(), df.loc[0][1].strip())
if isinstance(df.loc[0][2], str) and len(df.loc[0][2]) > 0:
sql += " " + df.loc[0][2].strip()
for row in range(1, df.shape[0]):
if not isinstance(df.loc[row][0], str):
break
sql += ", {} {}".format(df.loc[row][0].strip(), df.loc[row][1].strip())
if isinstance(df.loc[row][2], str) and len(df.loc[row][2]) > 0:
sql += " " + df.loc[row][2].strip()
sql += " )"
cursor = self.con.cursor()
try:
cursor.execute(sql)
except Exception as e:
self.logger.error(e)
def insert_csv_file_into_table(self, table_name, csv_fname, row_num=-1):
utils.file_exists(csv_fname, exit_=True)
df = pd.read_csv(csv_fname, dtype=object)
sql = "INSERT INTO " + table_name + " (" + ', '.join(df.columns) + ") "
sql += "VALUES (" + ", ".join(["%s"] * len(df.columns)) + ")"
if row_num < 0:
row_num = df.shape[0]
vals = []
for i in range(row_num):
if not isinstance(df.loc[i][0], str):
break
val = []
for j in range(len(df.columns)):
col = df.loc[i][j]
if not isinstance(col, str):
col = ""
val.append(col)
vals.append(tuple(val))
try:
cursor = self.con.cursor()
cursor.executemany(sql, vals)
self.con.commit()
self.print_cursor(cursor)
self.logger.info("")
self.logger.info(" # Insert csv file into table: {:d} was inserted.".format(cursor.rowcount))
except Exception as e:
self.logger.error(e)
return True
def detect_text_area(img,
area_ratio_thresh=0.25,
char_min_pxl=10,
box_img_=False,
debug_=False):
# convert to gray scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# smooth the image to avoid noises
filtered = cv2.medianBlur(gray, 3)
# Apply adaptive threshold
# thresh = cv2.adaptiveThreshold(gray, 255, 1, 1, 11, 2)
_, bin = cv2.threshold(filtered,
thresh=128,
maxval=255,
type=cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
kernel = np.ones((5, 5), np.uint8)
"""
kernel[0:2,0:2] = 0
kernel[0:2,3:5] = 0
kernel[3:5,3:5] = 0
kernel[3:5,0:2] = 0
kernel[1,1] = 1
kernel[1,3] = 1
kernel[3,1] = 1
kernel[3,3] = 1
"""
morph = np.copy(bin)
for _ in range(5):
morph = cv2.dilate(morph, kernel, iterations=3)
morph = cv2.erode(morph, kernel, iterations=3)
# Find the contours
image, contours, hierarchy = cv2.findContours(morph, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# For each contour, find the bounding rectangle and draw it
boxes = []
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
contour_area = cv2.contourArea(contour)
rect_area = w * h
if (contour_area / rect_area >
area_ratio_thresh) and w > char_min_pxl and h > char_min_pxl:
boxes.append([x, y, x + w, y + h])
boxes_img = []
if debug_:
boxes_img = hp.draw_boxes_on_img(np.copy(img),
boxes,
color=hp.RED,
thickness=4)
hp.imshow(morph)
hp.imshow(boxes_img)
return boxes, boxes_img
def run__erase_lines_in_image():
img_path = 'test_videos/census-1.jpg'
img_prefix = ''
imgs, filenames = hp.hp_imread_all_images(img_path,
fname_prefix=img_prefix)
for img in imgs:
erase_img = erase_lines_in_image(img, pause_img_sec=0)
hp.hp_imshow(np.hstack((cv2.cvtColor(img,
cv2.COLOR_RGB2GRAY), erase_img)),
desc="erase lines")
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--source", default=None, help="source video filename")
parser.add_argument("--target", default=None, help="source video filename")
args = parser.parse_args(argv)
args.source = utils.unicode_normalize(args.source)
args.target = utils.unicode_normalize(args.target)
return args
def __init__(self, camera_info, sim_save_dir, logger):
self.pd_info_dict = dict()
self.camera_info = camera_info
self.sim_save_dir = sim_save_dir
self.sim_out_dir = self.get_sim_out_dir(edge_uid, sim_save_dir)
if os.path.isdir(self.sim_out_dir):
shutil.rmtree(self.sim_out_dir)
utils.folder_exists(self.sim_out_dir, create_=True)
self.logger = logger
def run__derotate():
img_path = 'test_videos/census-rotate-2.jpeg'
imgs, filenames = hp.hp_imread_all_images(img_path,
fname_prefix='census-rotate-')
for img in imgs:
rot_img = derotate_image(img,
rot_img_fname=None,
check_time_=False,
pause_img_sec=0)
hp.hp_imshow(np.hstack((img, rot_img)), desc="de-rotation")
def make_window_masked_image(self,
img,
win_pos,
color=utils.BLACK,
show_sec=-1):
img = utils.draw_box_on_img(img,
win_pos,
color=color,
thickness=-1,
alpha=0)
utils.imshow(img, desc="make window masked image", pause_sec=show_sec)
return img
def make_face_boxed_image(img,
feat,
color=utils.RED,
thickness=4,
alpha=0.5,
show_conf_=True):
if feat.face_pos_arr is None or feat.face_conf_arr is None:
return img
thickness = thickness if show_conf_ else -1
for pos, conf in zip(feat.face_pos_arr, feat.face_conf_arr):
img = utils.draw_box_on_img(img,
pos,
color=color,
thickness=thickness,
alpha=alpha)
if show_conf_:
img = cv2.putText(img, "{:3d}".format(conf),
(pos[0] + 4, pos[3] - 4),
cv2.FONT_HERSHEY_SIMPLEX, 1, utils.WHITE, 5)
img = cv2.putText(img, "{:3d}".format(conf),
(pos[0] + 4, pos[3] - 4),
cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
return img
def init_shm_files(self, shm_ini, shm_shape=None):
self.shm_idx = 0
self.shm_file_num = int(shm_ini['shm_file_num'])
self.prefix = shm_ini['shm_file_prefix']
if shm_shape:
self.shm_shape = shm_shape
else:
shm_width = int(shm_ini['shm_width'])
shm_height = int(shm_ini['shm_height'])
self.shm_shape = (shm_height, shm_width, 3)
self.shm_arr = []
for i in range(self.shm_file_num):
shm_name = self.prefix + str(i)
shm_path = os.path.join('/dev/shm/', shm_name)
if utils.file_exists(shm_path):
os.remove(shm_path)
shm_size = self.shm_shape[0] * self.shm_shape[1] * self.shm_shape[2]
shm = shared_memory.SharedMemory(name=shm_name,
create=True,
size=shm_size)
# shm = np.ndarray(self.shm_shape, dtype=np.uint8, buffer=shm.buf)
self.shm_arr.append(shm)
def draw_line_from_rho_and_theta(img, rho, theta, pause_sec=-1):
img_sz = img.shape[1::-1]
a, b = np.cos(theta), np.sin(theta)
x0, y0 = a * rho, b * rho
x = []
y = []
if b != 0:
slope = -a / b
y1 = slope * (-x0) + y0
if 0 <= y1 < img_sz[1]:
x.append(0)
y.append(y1)
y1 = slope * (img_sz[0] - 1 - x0) + y0
if 0 <= y1 < img_sz[1]:
x.append(img_sz[0] - 1)
y.append(y1)
x1 = (-y0) / slope + x0
if 0 <= x1 < img_sz[0]:
x.append(x1)
y.append(0)
x1 = (img_sz[1] - 1 - y0) / slope + x0
if 0 <= x1 < img_sz[0]:
x.append(x1)
y.append(img_sz[1] - 1)
else:
x = [x0, x0]
y = [0, img_sz[1] - 1]
angle = (90 - (theta * 180 / np.pi))
if pause_sec >= 0:
print(" # rotated angle = {:f} <- ({:.3f}, {:.3f})".format(
angle, theta, rho))
if len(x) is 2:
pts = [[int(x[0] + 0.5), int(y[0] + 0.5)],
[int(x[1] + 0.5), int(y[1] + 0.5)]]
else:
if pause_sec >= 0:
print(" @ Warning: rho is zero.\n")
pts = [[0, 0], [0, 0]]
line_img = np.copy(img)
cv2.line(line_img, (pts[0][0], pts[0][1]), (pts[1][0], pts[1][1]), hp.RED,
4)
hp.hp_imshow(line_img, pause_sec=pause_sec)
return pts
def make_window_boxed_image(img, veh_feats, color=None, show_sec=-1):
for veh_idx, veh_feat in enumerate(veh_feats):
if veh_feat.win_conf_arr:
color = utils.get_color(i=veh_idx,
primary_=False) if color is None else color
for win_idx, win_pos in enumerate(veh_feat.win_pos_arr):
img = utils.draw_quadrilateral_on_image(img,
win_pos,
color=color,
thickness=4)
text = "{}: {:2d}".format("Window",
veh_feat.win_conf_arr[win_idx])
img = cv2.putText(img, text, (win_pos[0], win_pos[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 2, color, 4)
utils.imshow(img, desc="make window boxed image", pause_sec=show_sec)
return img
def get_roi(img, roi, imshow_sec=-1, clockwise_=True):
roi = np.array(roi)
roi = roi * np.array(img.shape[1::-1]) if not sum(sum(roi > 1)) else roi
if clockwise_:
roi[[2, 3]] = roi[[3, 2]]
roi_corners = np.array([[tuple(x) for x in roi]], dtype=np.int32)
ignore_mask_color = (255, ) * img.shape[2]
mask = cv2.fillPoly(np.zeros(img.shape, dtype=np.uint8),
roi_corners,
color=ignore_mask_color)
roi_img = cv2.bitwise_and(img, mask)
utils.imshow(roi_img, desc="roi image", pause_sec=imshow_sec)
# gray_img = cv2.cvtColor(roi_img, code=cv2.COLOR_RGB2GRAY)
offset = [[0, 0], list(img.shape[1::-1])]
return roi_img, offset
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--info_path", required=True, help="ini filename")
parser.add_argument("--save_dir", required=True, help="ini filename")
args = parser.parse_args(argv)
args.info_path = utils.unicode_normalize(args.info_path)
return args
def get_images_from_video(vid_fname,
out_path,
frame_interval,
logger=Logger.get_stdout_logger()):
utils.file_exists(vid_fname, exit_=True)
utils.folder_exists(out_path, exit_=False, create_=True, print_=True)
logger.info(" # Extract image from video, {}".format(vid_fname))
vid = mpy.VideoFileClip(vid_fname)
base_fname = os.path.splitext(os.path.basename(vid_fname))[0]
i_digit = int(np.log10(vid.duration / frame_interval)) + 1
n_digit = int(np.log10(vid.duration)) + 3
for i, s in enumerate(itools.numeric_range(0, vid.duration,
frame_interval)):
frame = vid.get_frame(s)
time_info = "__" + \
"{:0{width}d}".format(i, width=i_digit) + \
"__" + \
"{:0{width}.1f}sec".format(s, width=n_digit)
out_fname = os.path.join(out_path,
base_fname + time_info + IMAGE_FILE_EXT)
utils.imwrite(frame, out_fname)
logger.info(" # save image, {}".format(out_fname))
def find_black_rects(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
retval, thresh_gray = cv2.threshold(gray,
thresh=100,
maxval=255,
type=cv2.THRESH_BINARY)
# rsz_img = cv2.resize(thresh_gray, (0, 0), fx=0.3, fy=0.3)
# cv2.imshow("result", rsz_img)
# cv2.waitKey(0)
cv2.bitwise_not(thresh_gray, thresh_gray)
_, contours, hierarchy = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
i = 0
for cont in contours:
x1, y1, w, h = cv2.boundingRect(cont)
k = float(h) / w
if 0.9 < k < 1.1 and 1000 < w * h < 1500:
cv2.drawContours(img, contours, i, (0, 250, 0), thickness=10)
i += 1
hp.hp_imshow(img)
def run__crop():
paper_size = 'A4'
ref_dim = [1280, None]
org_img = hp.hp_imread("test_videos/DB_1.jpg")
ret, det_vertices, box_img = detect_four_corners_based_on_ref_squares(
org_img, (0, 0),
search_margin=0.1,
square_width=50. / 2480,
debug_=False)
if paper_size == 'A4':
ref_dim[1] = int(ref_dim[0] * np.sqrt(2.))
tar_vertices = [[0, 0], [ref_dim[0], 0], [0, ref_dim[1]],
[ref_dim[0], ref_dim[1]]]
mtx = cv2.getPerspectiveTransform(np.float32(det_vertices),
np.float32(tar_vertices))
warp_img = cv2.warpPerspective(org_img,
mtx,
dsize=tuple(ref_dim),
flags=cv2.INTER_LINEAR)
hp.hp_imshow(warp_img, "output")
hp.hp_imwrite(warp_img, "output.png")
def make_face_masked_image(img,
face_pos_arr,
color=utils.RED,
margin=0,
show_sec=-1):
if face_pos_arr is None:
return img
for face_pos in face_pos_arr:
pos = [
face_pos[0] - margin, face_pos[1] - margin, face_pos[2] + margin,
face_pos[3] + margin
]
pos = utils.check_box_boundary(pos, img.shape[1::-1])
img = utils.draw_box_on_img(img,
pos,
color=color,
thickness=-1,
alpha=0)
utils.imshow(img, desc="make face masked image", pause_sec=show_sec)
return img
def create_multi_svr_ini(_this_basename_, ini_fname, proc_offset, port_inc=10):
ini = utils.get_ini_parameters(ini_fname)
if proc_offset >= 0:
logger_name = ini['LOGGER']['name'] + '_{}'.format(proc_offset + 1)
ini['LOGGER']['name'] = logger_name
ini['LOGGER']['prefix'] = logger_name + '.'
ini['LOGGER']['folder'] = 'Log/{}/'.format(proc_offset + 1)
ini['SERVER_MODE']['name'] = logger_name
ini['SERVER_MODE']['port'] = str(
int(ini['SERVER_MODE']['port']) + proc_offset * port_inc)
ini['SERVER_MODE']['acronym'] = ini['SERVER_MODE'][
'acronym'] + '{}'.format(proc_offset + 1)
return ini
def __init__(self,
write_video: str = None,
limit_time: int = None,
input_video_path: str = None):
# Init ini
self.ini = utils.get_ini_parameters(INI_FNAME)
# Init logger
self.logger = setup_logger_with_ini(self.ini['LOGGER'],
logging_=True,
console_=True)
# Image size
self.img_height = None
self.img_width = None
self.img_height = int(self.ini['OUT_VIDEO']['height'])
self.img_width = int(self.ini['OUT_VIDEO']['width'])
# Init video captuer updater
status = pyinotify.Stats()
self.video_capture_updater = VideoCaptureUpdater(
status, input_video_path=input_video_path)
# change process name
setproctitle("python StreamAdaptor.py in StreamAdaptor")
# last send socket time
self.last_send_socket_time = time.time()
# Write Video
self.write_video = write_video
self.video_writer = None
self.limit_time = limit_time
self.start_time = time.time()
if self.write_video is not None:
self.logger.info('WRITE VIDEO MODE : ON')
CODEC = cv2.VideoWriter_fourcc(*'XVID')
self.video_writer = cv2.VideoWriter(
self.write_video,
fourcc=CODEC,
fps=float(self.video_capture_updater.frame_fps),
frameSize=(self.img_width, self.img_height))
def __init__(self, username, passwd, hostname=DEFAULT_HOST, port=DEFAULT_PORT,
database=None, table=None, logger=None, show_=False):
self.con = None
self.db_name = database
self.table_name = table
self.db_name = database
self.logger = logger
if self.logger is None:
self.logger = utils.setup_logger(None, None, logger_=False)
if show_:
self.logger.info(" # connect db : {}:{}".format(hostname, port))
self.logger.info(" # Create the instance of MariaDB handler, \"{}.{}\"".format(database, table))
try:
self.con = mysql.connector.connect(host=hostname,
port=port,
user=username,
passwd=passwd,
database=database)
except Exception as e:
self.logger.error(e)
def get(self):
frame_no = self.frame_no
self.vid_cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no)
ret, frame = self.vid_cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if self.mmap is None:
frame_info = frame
else:
if self.mmap.mmap_shape[0] != frame.shape[
0] or self.mmap.mmap_shape[1] != frame.shape[1]:
frame = utils.imresize(frame,
width=self.mmap.mmap_shape[0],
height=self.mmap.mmap_shape[1])
frame_info = self.mmap.write_mmap(frame)
self.frame_no += self.move_frame
else:
frame_info = None
return frame_no, frame_info
def check_lines_in_img(img, algorithm='HoughLineTransform'):
if img.shape != 3:
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img_rgb = np.copy(img)
else:
img_gray = np.copy(img)
img_rgb = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB)
ret, img_bw = cv2.threshold(img_gray, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
img_edge = cv2.Canny(img_bw, 50, 150, apertureSize=3)
if algorithm is 'HoughLineTransform':
lines = cv2.HoughLines(img_edge, 1, np.pi / 180, 100)
print(" # Total lines: {:d}".format(len(lines)))
for line in lines:
img_lines = np.copy(img_rgb)
dim = img_lines.shape
rho = line[0][0]
theta = line[0][1]
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x = []
y = []
if b != 0:
slope = -a / b
y1 = slope * (-x0) + y0
if 0 <= y1 < dim[0]:
x.append(0)
y.append(y1)
y1 = slope * (dim[1] - 1 - x0) + y0
if 0 <= y1 < dim[0]:
x.append(dim[1] - 1)
y.append(y1)
x1 = (-y0) / slope + x0
if 0 <= x1 < dim[1]:
x.append(x1)
y.append(0)
x1 = (dim[0] - 1 - y0) / slope + x0
if 0 <= x1 < dim[1]:
x.append(x1)
y.append(dim[0] - 1)
else:
x = [x0, x0]
y = [0, dim[0] - 1]
angle = (90 - (theta * 180 / np.pi))
print(" # rotated angle = {:.1f} <- ({:f}, {:f})".format(
angle, theta, rho))
if len(x) is 2:
img_lines = cv2.line(img_rgb, (int(x[0]), int(y[0])),
(int(x[1]), int(y[1])), hp.RED, 4)
hp.plt_imshow(img_lines)
# if -5 < angle < 0 or 0 < angle < 5:
# plt_imshow(img_line)
else:
print(" @ Warning: something wrong.\n")
pass
elif algorithm == 'ProbabilisticHoughTransform':
end_pts_list = cv2.HoughLinesP(img_edge,
1,
np.pi / 180,
threshold=100,
minLineLength=100,
maxLineGap=50)
img_lines = np.copy(img_rgb)
print(" # Total lines: {:d}".format(len(end_pts_list)))
for end_pts in end_pts_list:
cv2.line(img_lines, tuple(end_pts[0][0:2]), tuple(end_pts[0][2:]),
hp.RED, 10)
angle = np.arctan2(end_pts[0][3] - end_pts[0][1],
end_pts[0][2] - end_pts[0][0]) * 180. / np.pi
print(" # rotated angle = {:.1f}".format(angle))
hp.hp_imshow(img_lines)
# if -5 < angle < 0 or 0 < angle < 5:
# plt_imshow(img_line)
return True
def process_frame(self, frame_no, in_frame):
if frame_no == 1714:
a = 0
ret_img = in_frame
ret_img = self.draw_text(ret_img,
self.frame_no_text_pos,
"Frame : {}".format(frame_no),
font=self.font_frame)
# 기존 plate 검출을 가지고 있다가 점점 박스 두께를 줄이도록 하며, final 검출 이후엔 없애도록 하자!
# 그런데, 두개를 동시에 잡았을 땐 어떻게???
plt_info_list = []
if frame_no in self.pd_info_dict:
plt_pos_arr, plt_list = self.pd_info_dict[frame_no]
for plt_pos in plt_pos_arr:
plt_pts = utils.transform_quadrilateral_to_rectangle(
plt_pos, algo='max', margin=0)
crop_plt_img = ret_img[plt_pts[0][1]:plt_pts[1][1],
plt_pts[0][0]:plt_pts[1][0]]
plt_info_list.append((plt_pts, crop_plt_img.copy()))
ret_img = utils.draw_quadrilateral_on_image(ret_img,
self.camera_roi,
color=utils.GREEN,
clockwise_=False,
thickness=4)
if len(plt_info_list) > 0:
# ROI 선들이 번호판위를 지나갈수 있기 때문에 한번더 그려준다.
for plt_info in plt_info_list:
pts, crop_plt_img = plt_info
ret_img[pts[0][1]:pts[1][1],
pts[0][0]:pts[1][0]] = crop_plt_img
# 박스를 그리자!
ret_img = utils.draw_quadrilateral_on_image(ret_img,
plt_pos_arr,
color=utils.RED,
clockwise_=True,
thickness=10)
if plt_list is not None:
for idx in range(len(plt_pos_arr)):
if idx < len(plt_list):
plt_num = plt_list[idx]['plt_num']
else:
plt_num = "not found"
plt_pos = plt_pos_arr[idx]
test_box2 = (min(plt_pos[0][0] + 600,
ret_img.shape[1]), plt_pos[0][1] - 10)
text_box1 = (test_box2[0] - 600,
plt_pos[0][1] - (self.font_height + 20))
ret_img = cv2.rectangle(ret_img, text_box1, test_box2,
utils.BLACK, -1)
ret_img = self.draw_text(ret_img,
(text_box1[0], text_box1[1]),
plt_num)
self.last_plt_list = (plt_info_list, plt_list)
if self.last_plt_list:
plt_info_list, plt_list = self.last_plt_list
box_l = self.last_box_tl[0]
box_r = self.last_box_br[0]
text_pos_x = self.last_text_pos[0]
for idx in range(len(plt_info_list)):
if idx < len(plt_list):
if self.is_paint_black_box:
ret_img = cv2.rectangle(ret_img,
(box_l, self.last_box_tl[1]),
(box_r, self.last_box_br[1]),
utils.BLACK, -1)
plt_num = plt_list[idx]['plt_num']
plt_url = plt_list[idx]['plt_uri']
ret_img = self.draw_text(
ret_img, (text_pos_x, self.last_text_pos[1]), plt_num)
plt_img = utils.imread(plt_url)
if plt_img is not None:
plt_img = utils.imresize(plt_img,
height=self.plt_height)
plt_height = plt_img.shape[0]
plt_width = plt_img.shape[1]
ret_img[self.last_plt_pos_y:self.last_plt_pos_y +
plt_height,
text_pos_x:text_pos_x + plt_width] = plt_img
box_l += 700
box_r += 700
text_pos_x += 700
return ret_img
def make(self):
if not self.sim_save_dir:
return
video_fname = self.camera_id + '.'
video_fname += utils.get_datetime().replace(":", "-") + '.mp4'
video_path = os.path.join(self.sim_save_dir, video_fname)
if os.path.isfile(video_path):
shutil.rmtree(video_path)
self.logger.info(
"start making video from ImgExtract and ImgAVR. {}".format(
video_path))
if self.total_frame:
process1 = (ffmpeg.input(self.video_url).output(
'pipe:',
vframes=self.total_frame,
format='rawvideo',
pix_fmt='rgb24').run_async(pipe_stdout=True))
else:
process1 = (ffmpeg.input(self.video_url).output(
'pipe:', format='rawvideo',
pix_fmt='rgb24').run_async(pipe_stdout=True))
process2 = (ffmpeg.input(
'pipe:',
format='rawvideo',
pix_fmt='rgb24',
s='{}x{}'.format(self.video_width, self.video_height)).output(
video_path, pix_fmt='yuv420p').overwrite_output().run_async(
pipe_stdin=True))
read_size = self.video_width * self.video_height * 3
frame_no = 0
while True:
in_bytes = process1.stdout.read(read_size)
if not in_bytes:
break
in_frame = (np.frombuffer(in_bytes, np.uint8).reshape(
[self.video_height, self.video_width, 3]))
if self.img_width != self.video_width or self.img_height != self.video_height:
in_frame = utils.imresize(in_frame,
width=self.img_width,
height=self.img_height)
out_frame = self.process_frame(frame_no, in_frame)
process2.stdin.write(out_frame.astype(np.uint8).tobytes())
if frame_no != 0 and (frame_no % (30 * 60)) == 0:
self.logger.info(
"making video. {} th frame writing".format(frame_no))
frame_no += 1
process2.stdin.close()
process1.wait()
process2.wait()
self.logger.info("end making video. total:{} frame".format(frame_no))
def line_removal(img):
"""
:param img:
:return:
"""
if img is None:
print("Image is empty!")
pass
hp.hp_imshow(img, desc="Original image")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hp.hp_imshow(gray, desc="Gray image")
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 15, -2)
hp.hp_imshow(bw, desc="Binary image")
hz = bw
vt = bw
hz_size = hz.shape[1] / 30
hz_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (hz_size, 1, 1))
hz = cv2.erode(hz, hz_structure, iterations=1)
hz = cv2.dilate(hz, hz_structure, iterations=1)
hp.hp_imshow(hz, desc="horizontal")
vt_size = vt.shape[0] / 30
vt_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, vt_size, 1))
vt = cv2.erode(vt, vt_structure, iterations=1)
vt = cv2.dilate(vt, vt_structure, iterations=1)
hp.hp_imshow(vt, desc="vertical")
# bitwise_not
vt = cv2.bitwise_not(vt)
hp.hp_imshow(vt, desc="vertical bit")
"""
Extract edges and smooth image according to the logic
1. extract edges
2. dilate(edges)
3. src.copyTo(smooth)
4. blur smooth img
5. smooth.copyTo(src, edges)
"""
edges = cv2.adaptiveThreshold(vt, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 3, -2)
hp.hp_imshow(edges, desc="edges")
kernel = np.ones((2, 2), dtype="uint8")
edges = cv2.dilate(edges, kernel)
hp.hp_imshow(edges, desc="dilated edges")
smooth = vt
smooth = cv2.blur(smooth, (2, 2, 1))
vt, edges = smooth
hp.hp_imshow(vt, desc="smooth")
def derotate_image(img,
max_angle=30,
max_angle_candidates=50,
angle_resolution=0.5,
inside_margin_ratio=0.1,
rot_img_fname=None,
check_time_=False,
pause_img_sec=-1):
"""
Derotate image.
:param img:
:param max_angle: Maximum rotated angle. The angles above this should be ignored.
:param max_angle_candidates:
:param angle_resolution:
:param inside_margin_ratio:
:param rot_img_fname:
:param check_time_:
:param pause_img_sec:
:return:
"""
start_time = None
if check_time_:
start_time = time.time()
if len(img.shape) == 3:
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# img_gray = np.amin(img, axis=2)
else:
img_gray = np.copy(img)
inside_margin = [int(x * inside_margin_ratio) for x in img.shape[1::-1]]
img_gray[:inside_margin[1], :] = 255
img_gray[-inside_margin[1]:, :] = 255
img_gray[:, :inside_margin[0]] = 255
img_gray[:, -inside_margin[0]:] = 255
if False:
check_lines_in_img(img, algorithm='HoughLineTransform')
check_lines_in_img(img, algorithm='ProbabilisticHoughTransform')
ret, img_bw = cv2.threshold(img_gray, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
"""
kernel = np.ones((5, 5), np.uint8) # note this is a horizontal kernel
bw = np.copy(img_bw)
for i in range(9):
bw = cv2.erode(bw, kernel, iterations=1)
bw = cv2.dilate(bw, kernel, iterations=1)
hp.hp_imshow(hp.hstack_images((img_bw, bw)))
"""
img_edge = cv2.Canny(img_bw, 50, 150, apertureSize=3)
if False:
hp.hp_imshow(img_edge)
# hp.plt_imshow(edges)
lines = cv2.HoughLines(img_edge, 1, np.pi / 360,
int(min(img_edge.shape) / 8.))
angles = []
if lines is not None:
for cnt, line in enumerate(lines):
angle = int((90 - line[0][1] * 180 / np.pi) /
float(angle_resolution)) * angle_resolution
draw_line_from_rho_and_theta(img,
line[0][0],
line[0][1],
pause_sec=-1)
if abs(angle) < max_angle:
angles.append(angle)
if max_angle_candidates < cnt:
break
# rot_angle = max(set(angles), key=angles.count)
sorted_angles = sorted({x: angles.count(x)
for x in angles}.items(),
key=operator.itemgetter(1),
reverse=True)
if len(sorted_angles) == 0:
rot_angle = 0
elif len(sorted_angles) == 1:
rot_angle = sorted_angles[0][0]
elif sorted_angles[0][0] == 0 and (sorted_angles[0][1] <
2 * sorted_angles[1][1]):
rot_angle = sorted_angles[1][0]
elif (sorted_angles[0][1] / sorted_angles[1][1]
) < 3 and abs(sorted_angles[0][0] - sorted_angles[1][0]) <= 1.0:
rot_angle = (sorted_angles[0][0] + sorted_angles[1][0]) / 2.
else:
rot_angle = sorted_angles[0][0]
"""
if rot_angle != 0:
rot_angle += 0.5
"""
if pause_img_sec >= 0:
print("# Rotated angle is {:5.1f} degree.".format(rot_angle))
sz = img_bw.shape[1::-1]
rot_img = ~imutils.rotate(~img,
angle=-rot_angle,
center=(int(sz[0] / 2), int(sz[1] / 2)),
scale=1)
if check_time_:
print(
" # Time for rotation detection and de-rotation if any : {:.2f} sec"
.format(float(time.time() - start_time)))
if 0 <= pause_img_sec:
hp.imshow(np.concatenate((img, rot_img), axis=1),
pause_sec=pause_img_sec,
desc="de-rotation")
if rot_img_fname:
hp.hp_imwrite(rot_img_fname, rot_img, 'RGB')
return rot_img
def erase_lines_in_image(img, check_time_=False, pause_img_sec=-1):
"""
Erase lines.
:param img:
:param check_time_:
:param pause_img_sec:
:return:
"""
erase_window_sz = 9
line_thresh = 32
start_time = None
if check_time_:
start_time = time.time()
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if len(
img.shape) == 3 else np.copy(img)
ret, img_bw = cv2.threshold(img_gray, 128, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
"""
kernel = np.ones((1, 5), np.uint8) # note this is a horizontal kernel
bw = np.copy(img_bw)
for i in range(9):
bw = cv2.dilate(bw, kernel, iterations=1)
bw = cv2.erode(bw, kernel, iterations=1)
hp.hp_imshow(hp.hstack_images((img_bw, bw)))
"""
img_edge = cv2.Canny(img_bw, 50, 150, apertureSize=3)
lines = cv2.HoughLines(img_edge, 1, np.pi / 360, 200)
print(" # Total number of lines detected is {:d}.".format(len(lines)))
hp.hp_imshow(img_bw, pause_sec=pause_img_sec)
img_erase = np.ones((img_bw.shape[:2]), dtype=np.uint8) * 255
for cnt, line in enumerate(lines):
line_pts = draw_line_from_rho_and_theta(img,
line[0][0],
line[0][1],
pause_sec=pause_img_sec)
x0, y0, x1, y1 = line_pts[0][0], line_pts[0][1], line_pts[1][
0], line_pts[1][1]
pnts = []
if (x1 - x0) > (y1 - y0):
for x in range(x0, x1):
y = (y1 - y0) / (x1 - x0) * (x - x0) + y0
pnts.append([int(x), int(y), img_bw[int(y), int(x)]])
else:
for y in range(y0, y1):
x = (x1 - x0) / (y1 - y0) * (y - y0) + x0
pnts.append([int(x), int(y), img_bw[int(y), int(x)]])
cnt = 0
stt_pnt = 0
for i in range(len(pnts)):
if pnts[i][2] == 255:
if cnt == 0:
stt_pnt = i
cnt += 1
else:
if cnt > line_thresh:
# print(" > {:d}-th line: {:d} + {:d} = {:d}".format(cnt, stt_pnt, cnt, stt_pnt+cnt))
for j in range(cnt):
pos = pnts[stt_pnt + j][:2]
if (x1 - x0) > (y1 - y0):
img_erase[pos[1] - erase_window_sz:pos[1] +
erase_window_sz + 1, pos[0]] = 0
else:
img_erase[pos[1], pos[0] - erase_window_sz:pos[0] +
erase_window_sz + 1] = 0
cnt = 0
hp.hp_imshow(img_erase, pause_sec=pause_img_sec)
if check_time_:
print(" # The processing time of erasing line function is {:.3f} sec".
format(float(time.time() - start_time)))
img_bw_erase = ((img_erase == 0) * 0 + (img_erase != 0) * img_bw).astype(
np.uint8)
return img_erase, img_bw_erase
def detect_four_corners_based_on_ref_squares(img,
ref_vertex,
search_margin=0.1,
square_width=50. / 2480,
debug_=False):
"""
Detect four quadrilateral vertices based on reference black squares.
It is assumed that four black squares are located
near the four corners based on reference vertices.
:param img:
:param ref_vertex:
:param search_margin:
:param square_width:
:param debug_:
:return: status, detected vertices, output image
"""
square_ratio_range = [0.8, 1.2]
square_width_margin = 0.5
square_fill_thresh = 0.8
debug_in_ = False
dim = img.shape[1::-1]
square_width = square_width * dim[0]
real_vertices = hp.generate_four_vertices_from_ref_vertex(ref_vertex, dim)
crop_boxes = []
offsets = [int(x * search_margin) for x in dim]
crop_boxes.append([
real_vertices[0][0], real_vertices[0][1],
real_vertices[0][0] + offsets[0], real_vertices[0][1] + offsets[1]
])
crop_boxes.append([
real_vertices[1][0] - offsets[0], real_vertices[1][1],
real_vertices[1][0], real_vertices[1][1] + offsets[1]
])
crop_boxes.append([
real_vertices[2][0], real_vertices[2][1] - offsets[1],
real_vertices[2][0] + offsets[0], real_vertices[2][1]
])
crop_boxes.append([
real_vertices[3][0] - offsets[0], real_vertices[3][1] - offsets[1],
real_vertices[3][0], real_vertices[3][1]
])
detected_vertices = []
kernel = np.ones((5, 5), np.uint8)
for idx in range(4):
crop_img = img[crop_boxes[idx][1]:crop_boxes[idx][3],
crop_boxes[idx][0]:crop_boxes[idx][2]]
gray_img = cv2.cvtColor(crop_img, cv2.COLOR_RGB2GRAY)
ret, thresh_gray = cv2.threshold(
gray_img,
thresh=200,
maxval=255,
# type=cv2.THRESH_BINARY)
type=cv2.THRESH_BINARY + cv2.THRESH_OTSU)
for _ in range(3):
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE,
kernel)
cv2.bitwise_not(thresh_gray, thresh_gray)
thresh_color = cv2.cvtColor(thresh_gray, cv2.COLOR_GRAY2RGB)
ret, contours, hierarchy = cv2.findContours(thresh_gray,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
min_width_ratio = 1
det_vertex = []
for i, cont in enumerate(contours):
x1, y1, w, h = cv2.boundingRect(cont)
ratio = h / w
width_ratio = abs(square_width - w) / square_width
if debug_in_:
if w > 10 and h > 10:
# cv2.drawContours(crop_img, contours, i, hp.GREEN, thickness=4)
cv2.drawContours(thresh_color,
contours,
i,
hp.GREEN,
thickness=4)
print("-------")
print(i)
print(x1, y1, w, h, width_ratio)
print(ratio, cv2.contourArea(cont) / (w * h))
if square_ratio_range[0] < ratio < square_ratio_range[1] and \
width_ratio < square_width_margin and \
cv2.contourArea(cont) / (w*h) > square_fill_thresh:
moments = cv2.moments(cont)
cx = int(moments['m10'] / moments['m00'])
cy = int(moments['m01'] / moments['m00'])
if width_ratio < min_width_ratio:
det_vertex = [
cx + crop_boxes[idx][0], cy + crop_boxes[idx][1]
]
min_width_ratio = width_ratio
# print("****")
if debug_:
disp_img = np.copy(crop_img)
cv2.drawContours(disp_img,
contours,
i,
hp.RED,
thickness=4)
cv2.circle(disp_img, (cx, cy), 8, hp.GREEN, -1)
hp.hp_imshow(disp_img)
if debug_in_:
hp.hp_imshow(thresh_color, desc="thresh_color")
if det_vertex:
detected_vertices.append(det_vertex)
box_img = np.copy(img)
if len(detected_vertices) != 4:
# print(" @ Error: 4 corners are NOT detected!")
return False, detected_vertices, img
cv2.line(box_img, tuple(detected_vertices[0]), tuple(detected_vertices[1]),
hp.RED, 10)
cv2.line(box_img, tuple(detected_vertices[0]), tuple(detected_vertices[2]),
hp.RED, 10)
cv2.line(box_img, tuple(detected_vertices[1]), tuple(detected_vertices[3]),
hp.RED, 10)
cv2.line(box_img, tuple(detected_vertices[2]), tuple(detected_vertices[3]),
hp.RED, 10)
if debug_:
hp.hp_imshow(box_img, desc="four corners")
return True, detected_vertices, img
