img = np.concatenate((img, img, img), axis=2)
img = Image.fromarray(img)
#12-net
#box: xmin, ymin, xmax, ymax, score, cropped_img, scale
neg_box = util.sliding_window(img, param.thr_12, net_12, input_12_node)
#12-calib
neg_db_tmp = np.zeros((len(neg_box), param.img_size_12, param.img_size_12,
param.input_channel), np.float32)
for id_, box in enumerate(neg_box):
neg_db_tmp[id_, :] = util.img2array(box[5], param.img_size_12)
calib_result = net_12_calib.prediction.eval(
feed_dict={input_12_node: neg_db_tmp})
neg_box = util.calib_box(neg_box, calib_result, img)
#NMS for each scale
scale_cur = 0
scale_box = []
suppressed = []
for id_, box in enumerate(neg_box):
if box[6] == scale_cur:
scale_box.append(box)
if box[6] != scale_cur or id_ == len(neg_box) - 1:
suppressed += util.NMS(scale_box)
scale_cur = box[6]
scale_box = [box]
neg_box = suppressed
suppressed = []
#12-net
#xmin, ymin, xmax, ymax, score, cropped_img, scale
result_box = util.sliding_window(img, param.thr_12, net_12,
input_12_node)
#12-calib
result_db_tmp = np.zeros((len(result_box), param.img_size_12,
param.img_size_12, param.input_channel),
np.float32)
for id_, box in enumerate(result_box):
result_db_tmp[id_, :] = util.img2array(box[5], param.img_size_12)
calib_result = net_12_calib.prediction.eval(
feed_dict={input_12_node: result_db_tmp})
result_box = util.calib_box(result_box, calib_result, img)
#NMS for each scale
scale_cur = 0
scale_box = []
suppressed = []
for id_, box in enumerate(result_box):
if box[6] == scale_cur:
scale_box.append(box)
if box[6] != scale_cur or id_ == len(result_box) - 1:
suppressed += util.NMS(scale_box)
scale_cur = box[6]
scale_box = [box]
result_box = suppressed
suppressed = []