def create_label_file(root_dir,
verbose=False,
dst_h=h_for_train,
dst_w=w_for_train):
"""
create a corresponding label file using opencv subpixel
for all the image files in root_dir
:param root_dir:
:return:
"""
exts_allowed = ('JPEG', 'jpg')
root_pre = None
MAX_IMG_SHOWN = 10
count = 0
for root, dirs, files in os.walk(root_dir):
if root != root_pre:
root_pre = root
print('marking files in directory:', root)
for img_file_name in files:
is_img = False
img_file_ext = img_file_name.split('.')[-1]
for ext in exts_allowed:
is_img = is_img or img_file_ext == ext
if is_img:
count += 1
file_name_prefix = '.'.join(img_file_name.split('.')[:-1])
txt_file_name = '.'.join([file_name_prefix, 'txt'])
img_file_path = os.path.join(root, img_file_name)
txt_file_path = os.path.join(root, txt_file_name)
if verbose and count <= MAX_IMG_SHOWN:
my_imshow(img)
img = cv2.imread(img_file_path)
resized_img = np.array(
Image.fromarray(img).resize((dst_w, dst_h)))
centroids = cv_harris(resized_img, thres=harris_thres)
img = cv2.imread(img_file_path)
img_h, img_w = img.shape[:2]
centroids_for_subpix = centroids.copy()
centroids_for_subpix[:, 0] *= (img_w / dst_w)
centroids_for_subpix[:, 1] *= (img_h / dst_h)
corners = cv_subpixel(img, centroids=centroids_for_subpix)
corners[:, 0] *= (dst_w / img_w)
corners[:, 1] *= (dst_h / img_h)
res = np.hstack((corners, centroids))
with open(txt_file_path, 'w') as f:
for corner_x, corner_y, centoird_x, centroid_y in res:
f.write(str(corner_x))
f.write('\t')
f.write(str(corner_y))
f.write('\t')
f.write(str(centoird_x))
f.write('\t')
f.write(str(centroid_y))
f.write('\n')
def __init__(self, img, win_size=20, pars=None, verbose=False):
self.pars = {'common': dict(canny_thres1=50, canny_thres2=200, canny_apertureSize=3, harris_thres=0.1),
'+': dict(max_dist=40, rho_thres=1, theta_thres=5 * PI / 180, parallel_dist=10,
hough_thres=int(1.5 * win_size), noise_thres=10, merge_thres=3.0)}
try:
if pars is not None:
for outer_key in pars:
if outer_key not in self.pars:
print("Unacceptable parameter {}".format(outer_key))
raise IndexError
for inner_key in pars[outer_key]:
if inner_key not in self.pars[outer_key]:
print("Unacceptable parameter {}-{}".format(outer_key, inner_key))
raise IndexError
self.pars[outer_key][inner_key] = pars[outer_key][inner_key]
except IndexError as e:
traceback.print_exc()
raise e
self.img = img
self.win_size = win_size
self.img_edged = cv2.Canny(img, self.pars['common']['canny_thres1'],
self.pars['common']['canny_thres2'],
self.pars['common']['canny_apertureSize']) # edge the img once
self.verbose = verbose
self.centroids = cv_subpixel(img, harris_thres)
self.proc_idx = None
self.proc_centroid = None
if verbose:
self.target_window = dict(left=200, right=300, top=200, bottom=300)
cache_num = 0
while os.path.exists(os.path.join('.', 'cache' + str(cache_num))):
cache_num += 1
cache_dir = os.path.join('.', 'cache' + str(cache_num))
os.makedirs(cache_dir)
self.cache_dir = cache_dir
my_imshow(self.img_edged, title='img-edged')
else:
self.cache_dir = None
cv_time = 0.0
for i, sample in enumerate(dataset):
img = sample['img']
corners = sample['corners']
time_start = time.time() # time in second
my_corners = my_subpixel(img,
model,
win_size,
harris_thres=harris_thres,
use_cuda=use_cuda)
time_end = time.time()
my_time += (time_end - time_start)
time_start = time.time() # time in second
cv_corners = cv_subpixel(img, harris_thres=harris_thres)
time_end = time.time()
cv_time += (time_end - time_start)
my_dist = evaluate_dist(test_corners=my_corners, ref_corners=corners)
my_dist_sum += my_dist
cv_dist = evaluate_dist(test_corners=cv_corners, ref_corners=corners)
cv_dist_sum += cv_dist
num_corners += len(corners)
if i % 100 == 0:
print('%d images processed' % i)
my_dist_mean = my_dist_sum / num_corners
cv_dist_mean = cv_dist_sum / num_corners
print('-' * 40)
print('my subpixel:')
union.union(ii, jj)
components = union.components()
comp_set = []
for component in components:
set_t = np.array(ver_set)[list(component)]
comp_set.append(set_t.mean(axis=0))
return np.array(comp_set).reshape(-1, 2)
if __name__ == "__main__":
thickness = 2
file_name = 'cross.jpg'
img = cv2.imread(os.path.join(data_dir, file_name))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
centroids = cv_subpixel(img, 0.1)
all_centroids = draw_marker(img, centroids, thickness=thickness)
my_imshow(all_centroids, title='all centroids')
print('centroids:')
print(centroids)
thickness = 10
finder = MarkerFinder(img, win_size=20, verbose=False)
corners = finder.find_corners()
print('corners:')
print(corners)
img_drawn = img
img_drawn = draw_marker(img_drawn, corners['+'], thickness=thickness, color=np.array((255, 0, 0)))
# # img_drawn = draw_marker(img_drawn, corners[1], thickness=thickness, color=np.array((0, 255, 0)))
# # img_drawn = draw_marker(img_drawn, corners[2], thickness=thickness, color=np.array((0, 0, 255)))
# # img_drawn = draw_marker(img_drawn, corners[3], thickness=thickness, color=np.array((255, 0, 255)))
groundtruth_corners = []
with open(txt_path, 'r') as f:
for line in f:
corner_x, corner_y, centroid_x, centroid_y = line.split()
groundtruth_corners.append((float(corner_x), float(corner_y)))
my_imshow(ori_img, show_in_func=True)
save_dir = "results"
plt.savefig(osp.join(save_dir, "ori_img.png"))
img = draw_marker(ori_img,
groundtruth_corners,
thickness=1,
color=np.array([255, 0, 255]))
print(len(groundtruth_corners))
print(groundtruth_corners)
corners_cv_sub = cv_subpixel(ori_img, harris_thres=harris_thres)
img_cvsub = draw_marker(img,
corners_cv_sub,
thickness,
color=np.array([0, 0, 255]))
print(len(corners_cv_sub))
print('cv_subpix')
print(corners_cv_sub)
my_imshow(img_cvsub, title='cv subpixel', show_in_func=True)
plt.savefig(osp.join(save_dir, "cv-subpixel.png"))
corners_harris = cv_harris(ori_img, thres=harris_thres)
img_cvharris = draw_marker(img,
corners_harris,
thickness,
color=np.array([255, 0, 0]))