def edit_feature(self, feature):
height, width = feature["image"].shape[0], feature["image"].shape[1]
feature["x1"], feature["y1"], feature["x2"], feature["y2"] = feature["x1"]/width, feature["y1"]/height, feature["x2"]/width, feature["y2"]/height
feature["image"] = cv2.resize(feature["image"], (128, 64)) #cv2 requires (width, height)
anchorbox = get_fpn_anchor_box(input_shape=feature["image"].shape)
target_cls, target_loc = get_target(anchorbox, feature["label"], feature["x1"], feature["y1"], feature["x2"], feature["y2"], num_classes=10)
feature["target_cls"], feature["target_loc"] = target_cls, target_loc
return feature
def forward(self, data, state):
label, x1, y1, x2, y2 = data
target_cls, target_loc = get_target(self.anchorbox,
label,
x1,
y1,
x2,
y2,
num_classes=10)
return target_cls, target_loc
def forward(self, data, state):
obj_label, x1, y1, width, height = data
num_example = obj_label.shape[0]
cls_gt = []
x1_gt = []
y1_gt = []
w_gt = []
h_gt = []
for idx in range(num_example):
c, x, y, w, h = get_target(self.anchorbox, obj_label[idx], x1[idx],
y1[idx], width[idx], height[idx])
cls_gt.append(c)
x1_gt.append(x)
y1_gt.append(y)
w_gt.append(w)
h_gt.append(h)
target = [
np.array(cls_gt),
np.array(x1_gt),
np.array(y1_gt),
np.array(w_gt),
np.array(h_gt)
]
return target