def parse_det(Y, C, score=0.1, down=4, scale='h'):
seman = Y[0][0, :, :, 0]
if scale == 'h':
height = np.exp(Y[1][0, :, :, 0]) * down
width = 0.41 * height
elif scale == 'w':
width = np.exp(Y[1][0, :, :, 0]) * down
height = width / 0.41
elif scale == 'hw':
height = np.exp(Y[1][0, :, :, 0]) * down
width = np.exp(Y[1][0, :, :, 1]) * down
y_c, x_c = np.where(seman > score)
boxs = []
if len(y_c) > 0:
for i in range(len(y_c)):
h = height[y_c[i], x_c[i]]
w = width[y_c[i], x_c[i]]
s = seman[y_c[i], x_c[i]]
x1, y1 = max(0, (x_c[i] + 0.5) * down - w / 2), max(
0, (y_c[i] + 0.5) * down - h / 2)
boxs.append([
x1, y1,
min(x1 + w, C.size_test[1]),
min(y1 + h, C.size_test[0]), s
])
boxs = np.asarray(boxs, dtype=np.float32)
keep = nms(boxs, 0.5, usegpu=False, gpu_id=0)
boxs = boxs[keep, :]
return boxs
def parse_det_offset(Y, C, score=0.1, down=4):
seman = Y[0][0, :, :, 0]
height = Y[1][0, :, :, 0]
offset_y = Y[2][0, :, :, 0]
offset_x = Y[2][0, :, :, 1]
y_c, x_c = np.where(seman > score)
boxs = []
if len(y_c) > 0:
for i in range(len(y_c)):
h = np.exp(height[y_c[i], x_c[i]]) * down
w = 0.41 * h
o_y = offset_y[y_c[i], x_c[i]]
o_x = offset_x[y_c[i], x_c[i]]
s = seman[y_c[i], x_c[i]]
x1, y1 = max(0, (x_c[i] + o_x + 0.5) * down - w / 2), max(
0, (y_c[i] + o_y + 0.5) * down - h / 2)
boxs.append([
x1, y1,
min(x1 + w, C.size_test[1]),
min(y1 + h, C.size_test[0]), s
])
boxs = np.asarray(boxs, dtype=np.float32)
keep = nms(boxs, 0.5, usegpu=False, gpu_id=0)
boxs = boxs[keep, :]
return boxs
def parse_det_bottom(Y, C, score=0.1):
seman = Y[0][0, :, :, 0]
height = Y[1][0, :, :, 0]
y_c, x_c = np.where(seman > score)
boxs = []
if len(y_c) > 0:
for i in range(len(y_c)):
h = np.exp(height[y_c[i], x_c[i]]) * 4
w = 0.41 * h
s = seman[y_c[i], x_c[i]]
x1, y1 = max(0, x_c[i] * 4 + 2 - w / 2), max(0, y_c[i] * 4 + 2 - h)
boxs.append([
x1, y1,
min(x1 + w, C.size_test[1]),
min(y1 + h, C.size_test[0]), s
])
boxs = np.asarray(boxs, dtype=np.float32)
keep = nms(boxs, 0.5, usegpu=False, gpu_id=0)
boxs = boxs[keep, :]
return boxs