while True:
retval, img = vid.read()
if not retval:
print("Done!")
break
img1 = np.copy(img)
img2 = np.zeros_like(img)
# model to predict
x = np.array([preprocess(img, input_size)])
with sl_graph.as_default():
with sl_session.as_default():
y = sl_model.predict(x)
result = prior_util.decode(y[0], confidence_threshold)
if len(result) > 0:
bboxs = result[:, 0:4]
quads = result[:, 4:12]
rboxes = result[:, 12:17]
boxes = np.asarray([rbox3_to_polygon(r) for r in rboxes])
xy = boxes
xy = xy * [vid_w, vid_h]
xy = np.round(xy)
xy = xy.astype(np.int32)
cv2.polylines(img1, tuple(xy), True, (0, 0, 255))
while current_y + crop_h < height:
while current_x + crop_w < width:
crop_img = rot_img[current_y:current_y + crop_h,
current_x:current_x + crop_w]
if do_preprocess:
crop_img = preprocess(crop_img, (512, 512))
model_output = model.predict(np.array([crop_img]),
batch_size=1,
verbose=0)
res = prior_util.decode(model_output[0],
confidence_threshold,
fast_nms=False)
bboxes = res[:, 0:4]
quades = res[:, 4:12]
rboxes = res[:, 12:17]
conf = res[:, 17:]
for j in range(len(rboxes)):
# convert rbox
polygon = rbox3_to_polygon(rboxes[j]) * 512
# translate to full image location
polygon[:, 0] += current_x
polygon[:, 1] += current_y
# rotate to orientation when image is not rotated