def fit_ellipse_findContours(image, pred):
#all 69.51ms
i_sz = pred.shape
pts = []
#pdb.set_trace()
_, p, hierarchy = cv2.findContours(pred, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for i in range(len(p)):
for j in range(len(p[i])):
pts.append(p[i][j])
pts_ = np.array(pts)
if pts_.shape[0] > 5:
#3.43ms/54.23ms
ellipse_info_ = cv2.fitEllipse(pts_)
if ellipse_info_[2] > 90:
angle_ = 180
else:
angle_ = 0
ellipse_info = (ellipse_info_[0], ellipse_info_[1], angle_)
cv2.ellipse(image, ellipse_info, (0, 255, 0), 1)
#print(ellipse_info)
#11.8ms/57.67ms
#***********************
part_label = ellipse_my(ellipse_info)
c_x = ellipse_info[0][0]
c_y = ellipse_info[0][1]
for i in range(len(part_label)):
x = int(part_label[i][1])
y = int(part_label[i][0])
if y < len(image) and x < len(image[0]) and y >= 0 and x >= 0:
image[y][x][0] = 0
image[y][x][1] = 255
image[y][x][2] = 0
p_i_x, p_i_y, p_o_x, p_o_y = get_nine_half(c_x, c_y, x, y)
#p_i_y,p_i_x,p_o_y,p_o_x=get_nine_half(c_x,c_y,x,y)
cv2.line(image, (p_i_x, p_i_y), (p_o_x, p_o_y), (0, 255, 0), 1)
#**********************
else:
pass
'''
for i in range(pts_.shape[0]):
x=int(pts_[i][1])
y=int(pts_[i][0])
if y<len(image) and x<len(image[0]) and y >= 0 and x>=0:
image[y][x][0]=0
image[y][x][1]=255
image[y][x][2]=0'''
size = (224, 224)
image = cv2.resize(image, size, interpolation=cv2.INTER_AREA)
return image
def fit_ellipse(image, pred):
#all 69.51ms
i_sz = pred.shape
pts = []
#13.4ms/40.75ms
for i in range(i_sz[0]):
for j in range(i_sz[1]):
if pred[i][j] != 0:
pts.append([j, i])
p = np.array(pts)
if p.shape[0] > 5:
#print('p',p)
#3.43ms/54.23ms
ellipse_info_ = cv2.fitEllipse(p)
if ellipse_info_[2] > 90:
angle_ = 180
else:
angle_ = 0
ellipse_info = (ellipse_info_[0], ellipse_info_[1], angle_)
cv2.ellipse(image, ellipse_info, (0, 255, 0), 1)
#print(ellipse_info)
#11.8ms/57.67ms
#***********************
part_label = ellipse_my(ellipse_info)
c_x = ellipse_info[0][0]
c_y = ellipse_info[0][1]
for i in range(len(part_label)):
x = int(part_label[i][1])
y = int(part_label[i][0])
if y < len(image) and x < len(image[0]):
image[y][x][0] = 0
image[y][x][1] = 255
image[y][x][2] = 0
p_i_x, p_i_y, p_o_x, p_o_y = get_nine_half(c_x, c_y, x, y)
#p_i_y,p_i_x,p_o_y,p_o_x=get_nine_half(c_x,c_y,x,y)
cv2.line(image, (p_i_x, p_i_y), (p_o_x, p_o_y), (0, 255, 0), 1)
#**********************
size = (224, 224)
image = cv2.resize(image, size, interpolation=cv2.INTER_AREA)
return image
lx, ly, rx, ry, w, h = _rect(gt[i])
#print('lx:',lx,'ly:',ly,'rx',rx,'ry',ry,'w',w,'h',h)
cx = lx + w / 2
cy = ly + h / 2
img_name_split = gt[i][0].split('/')
str_l = len(img_name_split)
img_name = os.path.join(
img_folder1, img_name_split[str_l - 3] + img_name_split[str_l - 1])
crops = cv2.resize(im, size, interpolation=cv2.INTER_AREA)
cv2.imwrite(img_name, crops)
box = [cx, cy, h / 2, w / 2]
#print('box:',box)
pts = ellipse_my(box, 0)
for j in range(len(pts)):
x = int(pts[j][0])
y = int(pts[j][1])
s_x, e_x, s_y, e_y = get_point(x, y)
gt_im[s_y:e_y, s_x:e_x, :] = 1
gt_img_name = os.path.join(
gt_folder1, img_name_split[str_l - 3] + img_name_split[str_l - 1])
gt_crops = cv2.resize(gt_im, size, interpolation=cv2.INTER_AREA)
#print('gt_crops',gt_crops.shape)
cv2.imwrite(gt_img_name, gt_crops)
if i % 1000 == 0:
print('Now is %d...' % i)
'''line=img_name+' '+gt_img_name+' '+str(c_x)+' '+str(c_y)+' '+str(n_w)+' '+str(n_h)+' '+str(0)+'\n'
#print(line)
count = 1
h_ = h
w_ = w
h_s = h
w_s = w
rate_pixel = 254
for t in range(expand_num):
h_ += count
w_ += count
h_s -= count
w_s -= count
tmp = rate_pixel - 1
box_ = [cx,cy,h_/2,w_/2]
pts_ = ellipse_my(box_,0)
for j in range(len(pts_)):
x=int(pts_[j][0])
y=int(pts_[j][1])
if y >=0 and x>=0 and y < frame_sz[0] and x<frame_sz[1]:
gt_im[y][x][0]=tmp
if h_s>0 and w_s>0:
box_s = [cx,cy,h_s/2,w_s/2]
pts_s = ellipse_my(box_s, 0)
for j in range(len(pts_s)):
x=int(pts_s[j][0])
y=int(pts_s[j][1])
if y >=0 and x>=0 and y < frame_sz[0] and x<frame_sz[1]:
gt_im[y][x][0]=tmp
rate_pixel -= 1
def fit_ellipse_findContours_ori(image, pred):
#all 69.51ms
i_sz = pred.shape
im_sz = image.shape
rate1 = im_sz[0] / i_sz[0]
rate0 = im_sz[1] / i_sz[1]
rate = [rate0, rate1]
pts = []
_, p, hierarchy = cv2.findContours(pred, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for i in range(len(p)):
for j in range(len(p[i])):
pts.append(p[i][j])
pts_ = np.array(pts)
if pts_.shape[0] > 5:
#3.43ms/54.23ms
ellipse_info_ = cv2.fitEllipse(pts_)
if ellipse_info_[2] > 90:
angle_ = 180
else:
angle_ = 0
ellipse_info_resize = (ellipse_info_[0], ellipse_info_[1], angle_)
part_label = ellipse_my(ellipse_info_resize)
ellipse_info_0 = tuple(np.array(ellipse_info_[0]) * rate)
ellipse_info_1 = tuple(np.array(ellipse_info_[1]) * rate)
ellipse_info = (ellipse_info_0, ellipse_info_1, angle_)
cv2.ellipse(image, ellipse_info, (0, 255, 0), 1)
#print(ellipse_info)
#11.8ms/57.67ms
#***********************
#part_label=ellipse_my(ellipse_info)
c_x = ellipse_info[0][0]
c_y = ellipse_info[0][1]
for i in range(len(part_label)):
x = int(part_label[i][1] * rate0)
y = int(part_label[i][0] * rate1)
if y < len(image) and x < len(image[0]) and y >= 0 and x >= 0:
image[y][x][0] = 0
image[y][x][1] = 255
image[y][x][2] = 0
p_i_x, p_i_y, p_o_x, p_o_y = get_nine_half(c_x, c_y, x, y)
#p_i_y,p_i_x,p_o_y,p_o_x=get_nine_half(c_x,c_y,x,y)
cv2.line(image, (p_i_x, p_i_y), (p_o_x, p_o_y), (0, 255, 0), 1)
#**********************
else:
pts_ = np.squeeze(pts_, axis=1)
for i in range(pts_.shape[0]):
x = int(pts_[i][1] * rate0)
y = int(pts_[i][0] * rate1)
if y < len(image) and x < len(image[0]) and y >= 0 and x >= 0:
image[y][x][0] = 0
image[y][x][1] = 255
image[y][x][2] = 0
return image
def divide_shelter_once(self,
im,
filename,
pred,
pred_pro,
gt_ellip,
if_valid=False,
is_save=True):
'''
divide the shelter and not shelter
'''
pts = []
'''
_, p, hierarchy = cv2.findContours(pred, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#tmp draw contours
sz = im.shape
c_im_show = np.zeros((sz[0], sz[1], 3))
cv2.drawContours(c_im_show, p, -1, (0, 255, 0), 1)
for i in range(len(p)):
for j in range(len(p[i])):
pts.append(p[i][j])
'''
sz = im.shape
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
#tmp
fn = filename.strip().decode('utf-8')
#fn_full = '%s%s.bmp' % ('img', fn.split('img')[1])
#im_full = cv2.imread(os.path.join(cfgs.full_im_path, fn_full))
#im = cv2.resize(im_full, (sz[1], sz[0]), interpolation=cv2.INTER_CUBIC)
fn_full = '%s%s%s.bmp' % ('s8', 'img', fn.split('img')[1])
fn_full_path = os.path.join(cfgs.full_im_path, fn_full)
im_full = cv2.imread(fn_full_path)
im = cv2.resize(im_full, (sz[1], sz[0]), interpolation=cv2.INTER_CUBIC)
#pred = remove_small_area(pred)
for ii in range(sz[0]):
for jj in range(sz[1]):
#if pred[ii][jj] > 0:
if pred[ii][jj] == 2:
pts.append([jj, ii])
#im[ii][jj] = 255
pts_ = np.array(pts)
if pts_.shape[0] > 5:
ellipse_info = cv2.fitEllipse(pts_)
ellipse_info_ = ellipse_info
#pred_ellip = np.array([ellipse_info[0][0], ellipse_info[0][1], ellipse_info[1][0], ellipse_info[1][1]])
if ellipse_info[2] > 150 or ellipse_info[2] < 30:
angle = 180
pred_ellip = np.array([
ellipse_info[0][0], ellipse_info[0][1], ellipse_info[1][0],
ellipse_info[1][1]
])
else:
angle = 90
pred_ellip = np.array([
ellipse_info[0][0], ellipse_info[0][1], ellipse_info[1][1],
ellipse_info[1][0]
])
ellipse_info = (tuple(
np.array([
ellipse_info[0][0], ellipse_info[0][1]
])), tuple(np.array([ellipse_info[1][0],
ellipse_info[1][1]])), angle)
#loss = self.haus_loss(pred_ellip, gt_ellip)
else:
pred_ellip = np.array([0, 0, 0, 0])
ellipse_info = (tuple(np.array([0, 0])), tuple(np.array([0,
0])), 0)
#loss = self.ellip_loss(pred_ellip, gt_ellip)
loss = 0
if is_save:
#save worse result
error_path = cfgs.error_path
#im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
cv2.ellipse(im, ellipse_info, (0, 255, 0), 1)
#gt_ellip_info = (tuple(np.array([gt_ellip[0], gt_ellip[1]])), tuple(np.array([gt_ellip[2], gt_ellip[3]])), 0)
#cv2.ellipse(im,gt_ellip_info,(0,0,255),1)
part_label = ellipse_my(ellipse_info)
c_x = ellipse_info[0][0]
c_y = ellipse_info[0][1]
for i in range(len(part_label)):
x = int(part_label[i][1])
y = int(part_label[i][0])
if y < len(im) and x < len(im[0]):
im[y][x][0] = 0
im[y][x][1] = 255
im[y][x][2] = 0
p_i_x, p_i_y, p_o_x, p_o_y = get_nine_half(c_x, c_y, x, y)
#p_i_y,p_i_x,p_o_y,p_o_x=get_nine_half(c_x,c_y,x,y)
cv2.line(im, (p_i_x, p_i_y), (p_o_x, p_o_y), (0, 255, 0),
1)
if loss > cfgs.loss_thresh:
if loss > 500:
loss = 500
path_ = os.path.join(
error_path,
filename.strip().decode('utf-8') + '_' + str(int(loss)) +
'.bmp')
cv2.imwrite(path_, im)
else:
path_ = os.path.join(
error_path + '_better',
filename.strip().decode('utf-8') + '_' + str(int(loss)) +
'.bmp')
cv2.imwrite(path_, im)
return loss