def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
initialW=None,
proposal_creator_params={},
):
self.anchor_base = generate_anchors()
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(**proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
super(RegionProposalNetwork, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(in_channels,
mid_channels,
3,
1,
1,
initialW=initialW)
self.score = L.Convolution2D(mid_channels,
n_anchor * 2,
1,
1,
0,
initialW=initialW)
self.loc = L.Convolution2D(mid_channels,
n_anchor * 4,
1,
1,
0,
initialW=initialW)
def visualize_config_anchors(image, gt_anchors, cfg):
# anchor_generator = make_anchor_generator(cfg)
anchor_sizes = cfg.RPN.ANCHOR_SIZES
anchor_ratios = cfg.RPN.ASPECT_RATIOS
stride = cfg.RPN.ANCHOR_STRIDE[0]
anchor_angles = cfg.RPN.ANCHOR_ANGLES
H, W = image.shape[:2]
total_anchors = len(anchor_angles) * len(anchor_ratios) * len(anchor_sizes)
anchors = generate_anchors(anchor_sizes, anchor_ratios, anchor_angles,
height=H // stride,
width=W // stride,
stride=stride)
iou_matrix = rotate_iou(FCT(gt_anchors), FCT(anchors)).cpu().numpy()
# sorted_matrix = np.argsort(iou_matrix, axis=1)[:, ::-1]
fg_iou_thresh = cfg.RPN.FG_IOU_THRESHOLD
nms_thresh = cfg.RPN.NMS_THRESH
a,b = np.nonzero(iou_matrix > fg_iou_thresh)
# gg = iou_matrix > fg_iou_thresh
# b = np.unique([ix for g in gg for ix,b in enumerate(g) if b!=0])
# best_anchors = anchors[sorted_matrix[:,0]]
best_anchors = anchors[b]#[(iou_matrix > 0.8]
# best_anchors = best_anchors[nms_rotate_cpu(best_anchors, nms_thresh, 1000)]
img_best_anchors = draw_anchors(image, best_anchors)
cv2.imshow("img", img)
cv2.imshow("best_anchors", img_best_anchors)
batch = total_anchors
start = 0 # (len(anchors) // total_anchors // 2)
for ix in np.arange(start, len(anchors), batch):
stride_anchors = anchors[ix:ix+batch]
img_stride_anchors = draw_anchors(image, stride_anchors)
valid_idx = b[np.logical_and(ix <= b, b < ix + batch)]
# print("Valids: %d"%(len(valid_idx)))
if len(valid_idx) == 0:
continue
valid_anchors = anchors[valid_idx]
img_valid_stride_anchors = draw_anchors(image, valid_anchors)
post_nms_anchors = valid_anchors[nms_rotate_cpu(valid_anchors, nms_thresh, 100)]
img_valid_stride_anchors_post_nms = draw_anchors(image, post_nms_anchors)
# post_nms_iou_matrix = iou_rotate_cpu(gt_anchors, post_nms_anchors)
# print(post_nms_iou_matrix)
cv2.imshow("stride_anchors", img_stride_anchors)
cv2.imshow("valid_stride_anchors (>%.2f)"%(fg_iou_thresh), img_valid_stride_anchors)
cv2.imshow("valid_stride_anchors (post NMS)", img_valid_stride_anchors_post_nms)
cv2.waitKey(0)
def __init__(self, ):
self.feat_stride = 16
self.anchor_scales = np.array([8, 16, 32])
self.anchors = generate_anchors(scales=self.anchor_scales)
self.num_anchors = self.anchors.shape[0]
self.phase = 'TRAIN'
self.pre_nms_topN = cfg[self.phase].RPN_PRE_NMS_TOP_N
self.post_nms_topN = cfg[self.phase].RPN_POST_NMS_TOP_N
self.nms_thresh = cfg[self.phase].RPN_NMS_THRESH
self.min_size = cfg[self.phase].RPN_MIN_SIZE
from keras.models import model_from_json
from anchor_generator import generate_anchors
from anchor_decode import decode_bbox
from nms import single_class_non_max_suppression
import RPi.GPIO as GPIO
model = model_from_json(open('models/face_mask_detection.json').read())
model.load_weights('models/face_mask_detection.hdf5')
# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
# generate anchors
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
# BOARD编号方式，基于插座引脚编号
GPIO.setmode(GPIO.BOARD)
# 输出模式
GPIO.setup(11, GPIO.OUT) #蜂鸣器
GPIO.setup(13, GPIO.OUT) # 红灯
GPIO.setup(15, GPIO.OUT) # 蓝灯
# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
anchors_exp = np.expand_dims(anchors, axis=0)
id2class = {0: 'Mask', 1: 'NoMask'}
def inference(image,
# import torch
# def FCT(x): return torch.cuda.FloatTensor(x)
# anchor_generator = make_anchor_generator(cfg)
anchor_sizes = cfg.RPN.ANCHOR_SIZES
anchor_ratios = [1., 2.] # cfg.RPN.ASPECT_RATIOS
stride = cfg.RPN.ANCHOR_STRIDE[0]
anchor_angles = cfg.RPN.ANCHOR_ANGLES
H, W = (224, 224)
total_anchors = len(anchor_angles) * len(anchor_ratios) * len(anchor_sizes)
anchors = generate_anchors(anchor_sizes,
anchor_ratios,
anchor_angles,
height=H // stride,
width=W // stride,
stride=stride)
rect1 = np.array([W // 2, H // 2, 80, 50, 30], dtype=np.float32)
rect2 = np.array([W // 2, H // 2, 80, 50, -30], dtype=np.float32)
rect3 = np.array([W // 2, H // 2, 60, 50, 0], dtype=np.float32)
gt_anchors = np.array([rect1, rect2, rect3])[2:]
img = np.zeros((H, W, 3), dtype=np.uint8)
# cv2.imshow("gt", draw_anchors(img, gt_anchors))
# cv2.waitKey(0)
iou_matrix = iou_rotate_cpu(gt_anchors, anchors)
fg_iou_thresh = cfg.RPN.FG_IOU_THRESHOLD
nms_thresh = cfg.RPN.NMS_THRESH