def show_anchors(image,
feature_shape,
anchors,
maxiou_indexes=None,
maxiou_per_gt=None,
labels=None,
show_grids=False):
image_height, image_width, _ = image.shape
_, feature_height, feature_width, _ = feature_shape
fig, ax = plt.subplots(1)
ax.imshow(image)
if show_grids:
grid_height = image_height // feature_height
for i in range(feature_height):
y = i * grid_height
line = Line2D([0, image_width], [y, y])
ax.add_line(line)
grid_width = image_width // feature_width
for i in range(feature_width):
x = i * grid_width
line = Line2D([x, x], [0, image_height])
ax.add_line(line)
# maxiou_indexes is (4, n_gt)
for index in range(maxiou_indexes.shape[1]):
i = maxiou_indexes[1][index]
j = maxiou_indexes[2][index]
k = maxiou_indexes[3][index]
# color = label_utils.get_box_color()
box = anchors[0][i][j][k] #batch, row, col, box
# default anchor box format is xmin, xmax, ymin, ymax
w = box[1] - box[0]
h = box[3] - box[2]
x = box[0]
y = box[2]
# Rectangle ((xmin, ymin), width, height)
rect = Rectangle((x, y),
w,
h,
linewidth=2,
edgecolor='y',
facecolor='none')
ax.add_patch(rect)
if maxiou_per_gt is not None and labels is not None:
# maxiou_per_gt[index] is row w/ max iou
iou = np.amax(maxiou_per_gt[index])
#argmax_index = np.argmax(maxiou_per_gt[index])
#print(maxiou_per_gt[index])
# offset
label = labels[index]
category = int(label[4])
class_name = index2class(category)
color = label_utils.get_box_color(category)
bbox = dict(facecolor=color, color=color, alpha=1.0)
ax.text(label[0],
label[2],
class_name,
color='w',
fontweight='bold',
bbox=bbox,
fontsize=8,
verticalalignment='top')
dxmin = label[0] - box[0]
dxmax = label[1] - box[1]
dymin = label[2] - box[2]
dymax = label[3] - box[3]
print(index, ":", "(", class_name, ")", iou, dxmin, dxmax, dymin,
dymax)
if labels is None:
plt.show()
return fig, ax
def show_boxes(image,
classes,
offsets,
feature_shapes,
show=True,
normalize=False):
# generate all anchors per feature map
anchors = []
n_layers = len(feature_shapes)
for index, shape in enumerate(feature_shapes):
shape = (1, *shape)
anchor = anchor_boxes(shape, image.shape, index=index)
anchor = np.reshape(anchor, [-1, 4])
if index == 0:
anchors = anchor
else:
anchors = np.concatenate((anchors, anchor), axis=0)
# get all non-zero (non-background) objects
# objects = np.argmax(classes, axis=1)
# print(np.unique(objects, return_counts=True))
# nonbg = np.nonzero(objects)[0]
if normalize:
print("Normalize")
anchors_centroid = minmax2centroid(anchors)
offsets[:, 0:2] *= 0.1
offsets[:, 0:2] *= anchors_centroid[:, 2:4]
offsets[:, 0:2] += anchors_centroid[:, 0:2]
offsets[:, 2:4] *= 0.2
offsets[:, 2:4] = np.exp(offsets[:, 2:4])
offsets[:, 2:4] *= anchors_centroid[:, 2:4]
offsets = centroid2minmax(offsets)
# convert fr cx,cy,w,h to real offsets
offsets[:, 0:4] = offsets[:, 0:4] - anchors
objects, indexes, scores = nms(classes, offsets, anchors, is_soft=True)
class_names = []
rects = []
if show:
fig, ax = plt.subplots(1)
ax.imshow(image)
for idx in indexes:
#batch, row, col, box
anchor = anchors[idx]
offset = offsets[idx]
anchor += offset[0:4]
# default anchor box format is
# xmin, xmax, ymin, ymax
w = anchor[1] - anchor[0]
h = anchor[3] - anchor[2]
x = anchor[0]
y = anchor[2]
category = int(objects[idx])
class_name = index2class(category)
class_name = "%s: %0.2f" % (class_name, scores[idx])
class_names.append(class_name)
rect = (x, y, w, h)
print(class_name, rect)
rects.append(rect)
if show:
color = get_box_color(category)
rect = Rectangle((x, y),
w,
h,
linewidth=2,
edgecolor=color,
facecolor='none')
ax.add_patch(rect)
bbox = dict(color='none', alpha=1.0)
ax.text(anchor[0] + 2,
anchor[2] - 16,
class_name,
color=color,
fontweight='bold',
bbox=bbox,
fontsize=8,
verticalalignment='top')
if show:
plt.show()
return class_names, rects
def show_boxes(args, image, classes, offsets, feature_shapes, show=True):
"""Show detected objects on an image. Show bounding boxes
and class names.
Arguments:
image (tensor): Image to show detected objects (0.0 to 1.0)
classes (tensor): Predicted classes
offsets (tensor): Predicted offsets
feature_shapes (tensor): SSD head feature maps
show (bool): Whether to show bounding boxes or not
Returns:
class_names (list): List of object class names
rects (list): Bounding box rectangles of detected objects
class_ids (list): Class ids of detected objects
boxes (list): Anchor boxes of detected objects
"""
# generate all anchor boxes per feature map
anchors = []
n_layers = len(feature_shapes)
for index, feature_shape in enumerate(feature_shapes):
anchor = anchor_boxes(feature_shape, image.shape, index=index)
anchor = np.reshape(anchor, [-1, 4])
if index == 0:
anchors = anchor
else:
anchors = np.concatenate((anchors, anchor), axis=0)
# get all non-zero (non-background) objects
# objects = np.argmax(classes, axis=1)
# print(np.unique(objects, return_counts=True))
# nonbg = np.nonzero(objects)[0]
if args.normalize:
print("Normalize")
anchors_centroid = minmax2centroid(anchors)
offsets[:, 0:2] *= 0.1
offsets[:, 0:2] *= anchors_centroid[:, 2:4]
offsets[:, 0:2] += anchors_centroid[:, 0:2]
offsets[:, 2:4] *= 0.2
offsets[:, 2:4] = np.exp(offsets[:, 2:4])
offsets[:, 2:4] *= anchors_centroid[:, 2:4]
offsets = centroid2minmax(offsets)
# convert fr cx,cy,w,h to real offsets
offsets[:, 0:4] = offsets[:, 0:4] - anchors
objects, indexes, scores = nms(args, classes, offsets, anchors)
class_names = []
rects = []
class_ids = []
boxes = []
if show:
fig, ax = plt.subplots(1)
ax.imshow(image)
yoff = 1
for idx in indexes:
#batch, row, col, box
anchor = anchors[idx]
offset = offsets[idx]
anchor += offset[0:4]
# default anchor box format is
# xmin, xmax, ymin, ymax
boxes.append(anchor)
w = anchor[1] - anchor[0]
h = anchor[3] - anchor[2]
x = anchor[0]
y = anchor[2]
category = int(objects[idx])
class_ids.append(category)
class_name = index2class(category)
class_name = "%s: %0.2f" % (class_name, scores[idx])
class_names.append(class_name)
rect = (x, y, w, h)
print(class_name, rect)
rects.append(rect)
if show:
color = get_box_color(category)
rect = Rectangle((x, y),
w,
h,
linewidth=2,
edgecolor=color,
facecolor='none')
ax.add_patch(rect)
bbox = dict(color='white', alpha=1.0)
ax.text(
anchor[0] + 2,
anchor[2] - 16 + np.random.randint(0, yoff),
class_name,
color=color,
#fontweight='bold',
bbox=bbox,
fontsize=10,
verticalalignment='top')
yoff += 50
#t.set_bbox(dict(facecolor='red', alpha=0.5, edgecolor='red'))
if show:
plt.savefig("detection.png", dpi=600)
plt.show()
return class_names, rects, class_ids, boxes
