def load_data(pair_infos, discrim, train=True):
img_path1 = pair_infos[0][0]
img_path2 = pair_infos[1][0]
bs1 = pair_infos[0][1] # xmin xmax ymin ymax
bs2 = pair_infos[1][1]
gt1 = Rectangle(bs1[0], bs1[2], bs1[1] - bs1[0], bs1[3] - bs1[2])
gt2 = Rectangle(bs2[0], bs2[2], bs2[1] - bs2[0], bs2[3] - bs2[2])
gt1 = convert_bbox_format(gt1, to='center-based')
gt2 = convert_bbox_format(gt2, to='center-based')
img1 = Image.open(img_path1).convert('RGB')
img2 = Image.open(img_path2).convert('RGB')
zbox1 = get_zbox(gt1, 0.25)
zbox2 = get_zbox(gt2, 0.25)
scales_w = 1.04**(random.random() * 6 - 3)
scales_h = 1.04**(random.random() * 6 - 3)
zbox2_scaled = Rectangle(zbox2.x, zbox2.y, zbox2.width * scales_w,
zbox2.height * scales_h)
dx = 0
dy = 0
xbox2 = get_xbox(zbox2_scaled, dx,
dy) # we assume second is the search region
z = gen_xz(img1, zbox1, to='z')
x = gen_xz(img2, xbox2, to='x')
info = [
dx, dy, gt2.width / scales_w / zbox2.width,
gt2.height / scales_h / zbox2.height
]
gt_box = np.array([np.log(info[2] * 2), np.log(info[3] * 2)])
gt = np.zeros((1, 17, 17))
gt[:, :, :] = -1
gt[0, 8, 8] = 1.
gt[0, 7:10, 7:10] = 1.
gt[0, 8:9, 6:11] = 1.
gt[0, 6:11, 8:9] = 1.
return z, x, gt, gt_box
def load_data(pair_infos, discrim, train=True):
anchors = generate_anchor(8, [
8,
], [0.33, 0.5, 1, 2, 3], 17)
gt = np.zeros((1, 17, 17))
gt[:, :, :] = -1
gt[0, 8, 8] = 1.
#gt[0,7:10,7:10] = 1.
#gt[0,8:9,6:11] = 1.
#gt[0,6:11,8:9] = 1.
#gt = np.zeros((1,28,28))
#gt[0,13:15,13:15] = 1.
if random.random > 0.0:
img_path1 = pair_infos[0][0]
img_path2 = pair_infos[1][0]
bs1 = pair_infos[0][1] #xmin xmax ymin ymax
bs2 = pair_infos[1][1]
gt1 = Rectangle(bs1[0], bs1[2], bs1[1] - bs1[0], bs1[3] - bs1[2])
gt2 = Rectangle(bs2[0], bs2[2], bs2[1] - bs2[0], bs2[3] - bs2[2])
gt1 = convert_bbox_format(gt1, to='center-based')
gt2 = convert_bbox_format(gt2, to='center-based')
img1 = Image.open(img_path1).convert('RGB')
img2 = Image.open(img_path2).convert('RGB')
zbox1 = get_zbox(gt1, 0.25)
zbox2 = get_zbox(gt2, 0.25)
else:
cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
# catIds = coco.getCatIds(nms[0])
# catIds = coco.getCatIds('bicycle')
catIds = coco.getCatIds(nms[np.random.randint(0, len(nms))])
imgIds = coco.getImgIds(catIds=catIds)
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
for ann in anns:
if i['category_id'] == catIds:
break
gt_org = convert_array_to_rec(ann['bbox'])
gt = convert_bbox_format(gt_org, to='center-based')
zbox = get_zbox(gt, 0.25)
xbox = get_xbox(zbox)
img1 = Image.open(dataDir + dataType + '/%012d.jpg' % img['id'])
imgIds = coco.getImgIds(catIds=catIds)
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
for ann in anns:
if i['category_id'] == catIds:
break
gt_org = convert_array_to_rec(ann['bbox'])
gt = convert_bbox_format(gt_org, to='center-based')
zbox = get_zbox(gt, 0.25)
xbox = get_xbox(zbox)
img2 = Image.open(dataDir + dataType + '/%012d.jpg' % img['id'])
gt[:, :, :] = -1
scales_w = 1.04**(random.random() * 6 - 3)
scales_h = 1.04**(random.random() * 6 - 3)
#scales_w = 2
#scales_h = 2
zbox2_scaled = Rectangle(zbox2.x, zbox2.y, zbox2.width * scales_w,
zbox2.height * scales_h)
#dx = 1-2*random.random()
#dy = 1-2*random.random()
dx = 0
dy = 0
#dx = dx/2
#dy = dy/2
# dx = 0
# dy = 0
xbox2 = get_xbox(zbox2_scaled, dx,
dy) # we assume second is the search region
#print zbox2,zbox2_scaled
z = gen_xz(img1, zbox1, to='z')
x = gen_xz(img2, xbox2, to='x')
# dw = 256*(random.random())*0.5
# dh = 256*(random.random())*0.5
# x = x.crop((0+dw,0+dh,128+dw,128+dh))
#segz = gen_xz( img2, zbox2,to = 'z')
#gt = np.ones((1,1,1))
#gt = gt*-1
#if dw + dh < 50:
# gt[0,0,0] = 1
info = [
dx, dy, gt2.width / scales_w / zbox2.width,
gt2.height / scales_h / zbox2.height
]
gt_box = np.array(
[-info[0] * 64, -info[1] * 64, info[2] * 128, info[3] * 128])
anchor_xctr = anchors[:, :1]
anchor_yctr = anchors[:, 1:2]
anchor_w = anchors[:, 2:3]
anchor_h = anchors[:, 3:]
gt_cx, gt_cy, gt_w, gt_h = gt_box
target_x = (gt_cx - anchor_xctr) / anchor_w
target_y = (gt_cy - anchor_yctr) / anchor_h
target_w = np.log(gt_w / anchor_w)
target_h = np.log(gt_h / anchor_h)
regression_target = np.hstack((target_x, target_y, target_w, target_h))
iou = compute_iou(anchors, gt_box).flatten()
# print(np.max(iou))
pos_index = np.where(iou > 0.4)[0]
neg_index = np.where(iou < 0.3)[0]
label = np.ones_like(iou) * -1
label[pos_index] = 1
label[neg_index] = 0
#mask = np.zeros((6,17,17))
#mask[:,:,:] = 0
#mask[:,p[0],p[1]] = 1
#gt = gt*mask
return z, x, gt_box, regression_target, label
def load_data(pair_infos, discrim, train=True):
if random.random > 0.0:
img_path1 = pair_infos[0][0]
img_path2 = pair_infos[1][0]
bs1 = pair_infos[0][1] #xmin xmax ymin ymax
bs2 = pair_infos[1][1]
gt1 = Rectangle(bs1[0], bs1[2], bs1[1] - bs1[0], bs1[3] - bs1[2])
gt2 = Rectangle(bs2[0], bs2[2], bs2[1] - bs2[0], bs2[3] - bs2[2])
gt1 = convert_bbox_format(gt1, to='center-based')
gt2 = convert_bbox_format(gt2, to='center-based')
img1 = Image.open(img_path1).convert('RGB')
img2 = Image.open(img_path2).convert('RGB')
zbox1 = get_zbox(gt1, 0.25)
zbox2 = get_zbox(gt2, 0.25)
else:
cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
# catIds = coco.getCatIds(nms[0])
# catIds = coco.getCatIds('bicycle')
catIds = coco.getCatIds(nms[np.random.randint(0, len(nms))])
imgIds = coco.getImgIds(catIds=catIds)
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
for ann in anns:
if i['category_id'] == catIds:
break
gt_org = convert_array_to_rec(ann['bbox'])
gt = convert_bbox_format(gt_org, to='center-based')
zbox = get_zbox(gt, 0.25)
xbox = get_xbox(zbox)
img1 = Image.open(dataDir + dataType + '/%012d.jpg' % img['id'])
imgIds = coco.getImgIds(catIds=catIds)
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
for ann in anns:
if i['category_id'] == catIds:
break
gt_org = convert_array_to_rec(ann['bbox'])
gt = convert_bbox_format(gt_org, to='center-based')
zbox = get_zbox(gt, 0.25)
xbox = get_xbox(zbox)
img2 = Image.open(dataDir + dataType + '/%012d.jpg' % img['id'])
gt[:, :, :] = -1
scales_w = 1.04**(random.random() * 6 - 3)
scales_h = 1.04**(random.random() * 6 - 3)
#scales_w = 2
#scales_h = 2
zbox2_scaled = Rectangle(zbox2.x, zbox2.y, zbox2.width * scales_w,
zbox2.height * scales_h)
#dx = 1-2*random.random()
#dy = 1-2*random.random()
#dx = 1
#dy = 1
#dx = dx/2
#dy = dy/2
dx = 0
dy = 0
xbox2 = get_xbox(zbox2_scaled, dx,
dy) # we assume second is the search region
#print zbox2,zbox2_scaled
z = gen_xz(img1, zbox1, to='z')
x = gen_xz(img2, xbox2, to='x')
info = [
dx, dy, gt2.width / scales_w / zbox2.width,
gt2.height / scales_h / zbox2.height
]
#gt_box = np.array([-info[0]*64,-info[1]*64,info[2]*128,info[3]*128])
gt_box = np.array([np.log(info[2] * 2), np.log(info[3] * 2)])
gt = np.zeros((1, 17, 17))
gt[:, :, :] = -1
gt[0, 8, 8] = 1.
gt[0, 7:10, 7:10] = 1.
gt[0, 8:9, 6:11] = 1.
gt[0, 6:11, 8:9] = 1.
return z, x, gt, gt_box
def load_data_rpn(pair_infos, discrim, train=True):
anchors = generate_anchor(8, [
8,
], [0.33, 0.5, 1, 2, 3], 17)
gt = np.zeros((1, 17, 17))
gt[:, :, :] = -1
gt[0, 8, 8] = 1.
img_path1 = pair_infos[0][0]
img_path2 = pair_infos[1][0]
bs1 = pair_infos[0][1] # xmin xmax ymin ymax
bs2 = pair_infos[1][1]
gt1 = Rectangle(bs1[0], bs1[2], bs1[1] - bs1[0], bs1[3] - bs1[2])
gt2 = Rectangle(bs2[0], bs2[2], bs2[1] - bs2[0], bs2[3] - bs2[2])
gt1 = convert_bbox_format(gt1, to='center-based')
gt2 = convert_bbox_format(gt2, to='center-based')
img1 = Image.open(img_path1).convert('RGB')
img2 = Image.open(img_path2).convert('RGB')
zbox1 = get_zbox(gt1, 0.25)
zbox2 = get_zbox(gt2, 0.25)
scales_w = 1.04**(random.random() * 6 - 3)
scales_h = 1.04**(random.random() * 6 - 3)
zbox2_scaled = Rectangle(zbox2.x, zbox2.y, zbox2.width * scales_w,
zbox2.height * scales_h)
dx = 0
dy = 0
xbox2 = get_xbox(zbox2_scaled, dx,
dy) # we assume second is the search region
z = gen_xz(img1, zbox1, to='z')
x = gen_xz(img2, xbox2, to='x')
info = [
dx, dy, gt2.width / scales_w / zbox2.width,
gt2.height / scales_h / zbox2.height
]
gt_box = np.array(
[-info[0] * 64, -info[1] * 64, info[2] * 128, info[3] * 128])
anchor_xctr = anchors[:, :1]
anchor_yctr = anchors[:, 1:2]
anchor_w = anchors[:, 2:3]
anchor_h = anchors[:, 3:]
gt_cx, gt_cy, gt_w, gt_h = gt_box
target_x = (gt_cx - anchor_xctr) / anchor_w
target_y = (gt_cy - anchor_yctr) / anchor_h
target_w = np.log(gt_w / anchor_w)
target_h = np.log(gt_h / anchor_h)
regression_target = np.hstack((target_x, target_y, target_w, target_h))
iou = compute_iou(anchors, gt_box).flatten()
# print(np.max(iou))
pos_index = np.where(iou > 0.4)[0]
neg_index = np.where(iou < 0.3)[0]
label = np.ones_like(iou) * -1
label[pos_index] = 1
label[neg_index] = 0
return z, x, gt_box, regression_target, label