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# * Copyright (c) 2022 STMicroelectronics.
# * All rights reserved.
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import numpy as np
def get_sizes_ratios_ssd_v1(input_shape: tuple = None) -> tuple:
"""
Returns a tuple of sizes and ratios based on the input_shape.
Args:
input_shape (tuple): The input shape of the model in the format (height, width, channels).
Returns:
tuple: A tuple of sizes and ratios in the format (sizes, ratios).
Raises:
None
"""
# Define sizes and ratios based on the input_shape
if input_shape[0] == 192:
sizes = [[0.26, 0.33], [0.42, 0.49], [0.58, 0.66], [0.74, 0.82], [0.9, 0.98]]
ratios = [[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3]]
elif input_shape[0] == 224:
sizes = [[0.1, 0.16], [0.26, 0.33], [0.42, 0.49], [0.58, 0.66], [0.74, 0.82], [0.9, 0.98]]
ratios = [[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3]]
else:
sizes = [[0.1, 0.16], [0.26, 0.33], [0.42, 0.49], [0.58, 0.66], [0.74, 0.82], [0.9, 0.98]]
ratios = [[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3],
[1.0, 2.0, 0.5, 1.0 / 3], [1.0, 2.0, 0.5, 1.0 / 3]]
# Return the sizes and ratios as a tuple
return sizes, ratios
def get_sizes_ratios_ssd_v2(input_shape: tuple = None) -> tuple:
def _sizes_creation(target_max_res,base_sizes,t_min_s,res_range,slope):
# target_max_res : must be in the res_range
# base_sizes=[[0.26,0.33], [0.42,0.49], [0.58,0.66], [0.74,0.82], [0.9,0.98]]
# t_min_s = [0.026,0.033]
# slope = 2
# res_range = [24,32,52]
ab_x = _affine_search([base_sizes[0][0],t_min_s[0]],res_range,slope) # [a,b,c]
ab_y = _affine_search([base_sizes[0][1],t_min_s[1]],res_range,slope) # [a,b,c]
arr_res = np.array([1/(target_max_res**slope),1])
target_max_s = np.array(base_sizes[-1])
target_min_s = np.stack([ab_x,ab_y]) @ arr_res
target_len = len(base_sizes)
target_sizes = np.linspace(target_min_s,target_max_s,target_len)
return target_sizes
def _affine_search(s,r,slope):
r = np.array(r)
R = np.stack([1/(r**slope),np.ones_like(r)],axis=1)
# knowing that -> R@x = s then : x = R^-1 @ s
return np.linalg.inv(R) @ s
fmap_sizes_dict = {'192': [24, 12, 6, 3,2],
'224': [28, 14, 7, 4,2],
'256': [32, 16, 8, 4,2],
'288': [36, 18, 9, 5,3],
'320': [40, 20, 10, 5,3],
'352': [44, 22, 11, 6,3],
'384': [48, 24, 12, 6,3],
'416': [52, 26, 13, 7,4]}
# Check that the model input shape is supported
if str(input_shape[0]) not in fmap_sizes_dict or str(input_shape[1]) not in fmap_sizes_dict:
supported_shapes = [(int(k), int(k), 3) for k in fmap_sizes_dict.keys()]
raise ValueError(f"\nInput shape ({input_shape[1]}, {input_shape[0]}, 3) "
"is not supported for `ssd_mobilenet_v2_fpnlite` models.\n"
f"Supported shapes: {supported_shapes}\n"
"Please check the 'training.model' section of your configuration file.")
base_sizes = [[0.26, 0.33],
[0.42, 0.49],
[0.58, 0.66],
[0.74, 0.82],
[0.90, 0.98]]
min_sizes_max_res = [0.06, 0.09]
max_fmap_res = fmap_sizes_dict[str(input_shape[0])][0]
res_range = [fmap_sizes_dict['192'][0],fmap_sizes_dict['416'][0]]
sizes = _sizes_creation(max_fmap_res, base_sizes, min_sizes_max_res, res_range, slope = 5)
ratios = [[1.0, 2.0, 0.5, 1.0 / 3]]*len(sizes)
# Return the sizes and ratios as a tuple
return sizes, ratios
def get_fmap_sizes(model_type, input_shape):
fmap_sizes_dict = {
'st_ssd_mobilenet_v1': {
'192': [24, 12, 6, 3, 1],
'224': [32, 16, 8, 4, 2, 1],
'256': [32, 16, 8, 4, 2, 1]
},
'ssd_mobilenet_v2_fpnlite': {
'192': [24, 12, 6, 3, 2],
'224': [28, 14, 7, 4, 2],
'256': [32, 16, 8, 4, 2],
'288': [36, 18, 9, 5, 3],
'320': [40, 20, 10, 5, 3],
'352': [44, 22, 11, 6, 3],
'384': [48, 24, 12, 6, 3],
'416': [52, 26, 13, 7, 4]
}
}
fmap_widths = np.array(fmap_sizes_dict[model_type][str(input_shape[0])])
fmap_heights = np.array(fmap_sizes_dict[model_type][str(input_shape[1])])
fmap_sizes = None
if model_type == 'st_ssd_mobilenet_v1':
fmap_sizes = np.stack([fmap_widths, fmap_heights],axis=-1)
elif model_type == 'ssd_mobilenet_v2_fpnlite':
fmap_sizes = np.stack([fmap_widths, fmap_heights],axis=-1)
return fmap_sizes
def gen_anchors(fmap, img_width, img_height, sizes, ratios, normalize=True, clip=False):
"""
Generate anchor boxes for a feature map
sizes = [s1, s2, ..., sm], ratios = [r1, r2, ..., rn], n_anchors = n + m - 1, only consider [s1, r1], [s1, r2], ..., [s1, rn], [s2, r1], ..., [sm, r1]
Arguments:
fmap: feature map
img_width: image width
img_height: image height
sizes: [s1, s2, ..., sm]
ratios: [r1, r2, ..., rn]
normalize: normalize to image sizes
Returns:
list of anchor boxes
"""
_, fmap_height, fmap_width, _ = fmap.shape
fmap_height = int(fmap_height)
fmap_width = int(fmap_width)
res_img = min(img_width, img_height)
n_anchors = len(sizes) + len(ratios) - 1
# compute the box widths and heights for all anchor boxes
wh_list = []
for ratio in ratios:
box_w = res_img * sizes[0] * np.sqrt(ratio)
box_h = res_img * sizes[0] / np.sqrt(ratio)
wh_list.append((box_w, box_h))
for i in range(len(sizes)):
if i == 0:
continue
box_w = res_img * sizes[i] * np.sqrt(ratios[0])
box_h = res_img * sizes[i] / np.sqrt(ratios[0])
wh_list.append((box_w, box_h))
wh_list = np.asarray(wh_list)
step_height = img_height / fmap_height
step_width = img_width / fmap_width
offset_height = 0.5
offset_width = 0.5
# compute the grid of anchor box center points
cy = np.linspace(
offset_height * step_height,
(offset_height + fmap_height - 1) * step_height,
fmap_height)
cx = np.linspace(
offset_width * step_width,
(offset_width + fmap_width - 1) * step_width,
fmap_width)
cx_grid, cy_grid = np.meshgrid(cx, cy)
cx_grid = np.expand_dims(cx_grid, -1)
cy_grid = np.expand_dims(cy_grid, -1)
# anchors: (fmap_height, fmap_width, n_anchors, 4), 4 elements including
# (cx, cy, w, h)
anchors = np.zeros((fmap_height, fmap_width, n_anchors, 4))
anchors[:, :, :, 0] = np.tile(cx_grid, (1, 1, n_anchors)) # set cx
anchors[:, :, :, 1] = np.tile(cy_grid, (1, 1, n_anchors)) # set cy
anchors[:, :, :, 2] = wh_list[:, 0] # set w
anchors[:, :, :, 3] = wh_list[:, 1] # set h
# convert (cx, cy, w, h) to (xmin, ymin, xmax, ymax)
anchors1 = np.copy(anchors).astype(np.float32)
anchors1[:, :, :, 0] = anchors[:, :, :, 0] - \
anchors[:, :, :, 2] / 2.0 # set xmin
anchors1[:, :, :, 1] = anchors[:, :, :, 1] - \
anchors[:, :, :, 3] / 2.0 # set ymin
anchors1[:, :, :, 2] = anchors[:, :, :, 0] + \
anchors[:, :, :, 2] / 2.0 # set xmax
anchors1[:, :, :, 3] = anchors[:, :, :, 1] + \
anchors[:, :, :, 3] / 2.0 # set ymax
# clip the coordinates to lie within the image boundaries
if clip:
x_coords = anchors1[:, :, :, [0, 2]]
x_coords[x_coords >= img_width] = img_width - 1
x_coords[x_coords < 0] = 0
anchors1[:, :, :, [0, 2]] = x_coords
y_coords = anchors1[:, :, :, [1, 3]]
y_coords[y_coords >= img_height] = img_height - 1
y_coords[y_coords < 0] = 0
anchors1[:, :, :, [1, 3]] = y_coords
if normalize:
anchors1[:, :, :, [0, 2]] /= img_width
anchors1[:, :, :, [1, 3]] /= img_height
return anchors1
def _gen_anchors_fmap(fmap_size, img_width, img_height,
sizes, ratios, normalize=True, clip=False):
"""
Generate anchor boxes for a given feature map size
Arguments:
fmap_size: feature map size
img_width: image width
img_height: image height
sizes: [s1, s2, ..., sm]
ratios: [r1, r2, ..., rn]
clip: clip to image boundary
normalize: normalize to image sizes
Returns:
list of anchor boxes
"""
fmap_height, fmap_width = fmap_size
fmap_height = int(fmap_height)
fmap_width = int(fmap_width)
res_img = min(img_width, img_height)
n_anchors = len(sizes) + len(ratios) - 1
# compute the box widths and heights for all anchor boxes
wh_list = []
for ratio in ratios:
box_w = res_img * sizes[0] * np.sqrt(ratio)
box_h = res_img * sizes[0] / np.sqrt(ratio)
wh_list.append((box_w, box_h))
for i in range(len(sizes)):
if i == 0:
continue
box_w = res_img * sizes[i] * np.sqrt(ratios[0])
box_h = res_img * sizes[i] / np.sqrt(ratios[0])
wh_list.append((box_w, box_h))
wh_list = np.asarray(wh_list)
step_height = img_height / fmap_height
step_width = img_width / fmap_width
offset_height = 0.5
offset_width = 0.5
# compute the grid of anchor box center points
cy = np.linspace(
offset_height * step_height,
(offset_height + fmap_height - 1) * step_height,
fmap_height)
cx = np.linspace(
offset_width * step_width,
(offset_width + fmap_width - 1) * step_width,
fmap_width)
cx_grid, cy_grid = np.meshgrid(cx, cy)
cx_grid = np.expand_dims(cx_grid, -1)
cy_grid = np.expand_dims(cy_grid, -1)
# anchors: (fmap_height, fmap_width, n_anchors, 4), 4 elements including
# (cx, cy, w, h)
anchors = np.zeros((fmap_height, fmap_width, n_anchors, 4))
anchors[:, :, :, 0] = np.tile(cx_grid, (1, 1, n_anchors)) # set cx
anchors[:, :, :, 1] = np.tile(cy_grid, (1, 1, n_anchors)) # set cy
anchors[:, :, :, 2] = wh_list[:, 0] # set w
anchors[:, :, :, 3] = wh_list[:, 1] # set h
# convert (cx, cy, w, h) to (xmin, ymin, xmax, ymax)
anchors1 = np.copy(anchors).astype(np.float32)
anchors1[:, :, :, 0] = anchors[:, :, :, 0] - \
anchors[:, :, :, 2] / 2.0 # set xmin
anchors1[:, :, :, 1] = anchors[:, :, :, 1] - \
anchors[:, :, :, 3] / 2.0 # set ymin
anchors1[:, :, :, 2] = anchors[:, :, :, 0] + \
anchors[:, :, :, 2] / 2.0 # set xmax
anchors1[:, :, :, 3] = anchors[:, :, :, 1] + \
anchors[:, :, :, 3] / 2.0 # set ymax
# clip the coordinates to lie within the image boundaries
if clip:
x_coords = anchors1[:, :, :, [0, 2]]
x_coords[x_coords >= img_width] = img_width - 1
x_coords[x_coords < 0] = 0
anchors1[:, :, :, [0, 2]] = x_coords
y_coords = anchors1[:, :, :, [1, 3]]
y_coords[y_coords >= img_height] = img_height - 1
y_coords[y_coords < 0] = 0
anchors1[:, :, :, [1, 3]] = y_coords
if normalize:
anchors1[:, :, :, [0, 2]] /= img_width
anchors1[:, :, :, [1, 3]] /= img_height
return anchors1
def get_anchor_boxes(fmap_sizes,
image_size,
sizes=None,
ratios=None,
normalize=True,
clip_boxes=False):
anchor_boxes = []
for i in range(len(fmap_sizes)):
bboxes_fmap = _gen_anchors_fmap(
fmap_sizes[i],
image_size[0],
image_size[1],
sizes[i],
ratios[i],
normalize=normalize,
clip=clip_boxes)
dims = bboxes_fmap.shape
anchor_boxes += np.reshape(bboxes_fmap, (dims[0] * dims[1] * dims[2], 4)).tolist()
return np.array(anchor_boxes, dtype=np.float32)
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