def imshow_bboxes_custom(img,
bboxes,
colors='green',
top_k=-1,
thickness=1,
show=True,
win_name='',
wait_time=0,
out_file=None):
"""Draw bboxes on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (list or ndarray): A list of ndarray of shape (k, 4).
colors (list[str or tuple or Color]): A list of colors.
top_k (int): Plot the first k bboxes only if set positive.
thickness (int): Thickness of lines.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str, optional): The filename to write the image.
"""
img = imread(img)
if isinstance(bboxes, np.ndarray):
bboxes = [bboxes]
if not isinstance(colors, list):
colors = [colors for _ in range(len(bboxes))]
colors = [color_val(c) for c in colors]
assert len(bboxes) == len(colors)
for i, _bboxes in enumerate(bboxes):
label_text = _bboxes[:, 4]
color_text = color_val('green')
_bboxes = _bboxes.astype(np.int32)
if top_k <= 0:
_top_k = _bboxes.shape[0]
else:
_top_k = min(top_k, _bboxes.shape[0])
for j in range(_top_k):
left_top = (_bboxes[j, 0], _bboxes[j, 1])
right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
cv2.rectangle(img,
left_top,
right_bottom,
colors[i],
thickness=thickness)
cv2.putText(img, '{:.02f}'.format(label_text[j]),
(_bboxes[j, 0], _bboxes[j, 3] - 2),
cv2.FONT_HERSHEY_COMPLEX, 1, color_text)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
def show_bbox(img,
labels,
bboxes,
class_names=None,
show_threshold=0.1,
bbox_color='green',
text_color='green',
no_name=False):
# labels >= 1
thickness = 2
font_scale = 1
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
if show_threshold > 0 and bboxes.shape[1] == 5:
labels = labels[bboxes[:, -1] > show_threshold]
bboxes = bboxes[bboxes[:, -1] > show_threshold]
for bbox, label in zip(bboxes, labels):
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[2], bbox_int[3])
cv2.rectangle(img,
left_top,
right_bottom,
bbox_color,
thickness=thickness)
if no_name:
if len(bbox) > 4:
if bbox_int[1] > 20:
txt_loac = (bbox_int[0], bbox_int[1])
else:
txt_loac = (bbox_int[0], bbox_int[3])
cv2.putText(img, '{:.02f}'.format(bbox[-1]), txt_loac,
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
else:
assert class_names is not None
label_text = class_names[int(label.item(
))] if class_names is not None else 'cls {}'.format(label)
if len(bbox) > 4:
label_text += '|{:.02f}'.format(bbox[-1])
cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
return img
def show_det_bboxes(img,
bboxes,
labels,
out_file,
gt_bboxes=None,
class_names=None,
score_thr=0.05,
bbox_color='yellow',
gt_color='green',
top_k=5):
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
topinds = np.argsort(bboxes[:, 4])
topinds = topinds[:top_k] if len(topinds) >= top_k else topinds
bboxes = bboxes[topinds, :]
labels = labels[topinds]
bbox_color = color_val(bbox_color)
gt_color = color_val(gt_color)
for bbox, label in zip(bboxes, labels):
addBox(img, bbox, bbox_color, label, class_names=class_names)
if gt_bboxes is not None:
assert len(gt_bboxes) == 1
gt_bboxes = gt_bboxes[0]
for gt_box in gt_bboxes:
gt_box = gt_box.numpy()
addBox(img, gt_box, gt_color, 0, class_names=class_names)
imwrite(img, out_file)
def show_result(img, result, class_names):
global mask_rcnn_flag
img_mask = img.copy()
mask_temp = img.copy()
bbox_result, segm_result = result
bboxes = np.vstack(bbox_result)
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_result)
]
labels = np.concatenate(labels)
bbox_color = 'green'
text_color = 'green'
thickness = 1
font_scale = 3
show = True
win_name = ''
wait_time = 0
out_file = None
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
prev_point = [0, 0]
for i in range(0, len(bboxes)):
label_text = class_names[
labels[i]] if class_names is not None else 'cls {}'.format(
labels[i])
if len(bboxes[i]) > 4:
label_text += '|{:.02f}'.format(bboxes[i][-1])
left_top = (int(bboxes[i][0]), int(bboxes[i][1]))
right_bottom = (int(bboxes[i][2]), int(bboxes[i][3]))
if bboxes[i][-1] < 0.5:
pass
else:
img = cv2.putText(img, str(label_text),
(int(bboxes[i][0]), int(bboxes[i][1])),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
#print(bboxes[i])
img = cv2.rectangle(img, (int(bboxes[i][0]), int(bboxes[i][1])),
(int(bboxes[i][2]), int(bboxes[i][3])),
(255, 0, 0), 1)
mask_temp = np.zeros((720, 1280), dtype=np.uint8)
if segm_result is not None:
segms = mmcv.concat_list(segm_result)
inds = np.where(bboxes[:, -1] > score_thr)[0]
mask_temp = mask_temp * 0
inds_len = 0
for i in inds:
label_text = class_names[
labels[i]] if class_names is not None else 'cls {}'.format(
labels[i])
if label_text == 'banana':
color_mask = np.random.randint(0, 256, (1, 3), dtype=np.uint8)
mask = maskUtils.decode(segms[i]).astype(np.bool)
mask_temp[mask] = 255
#print(color_mask.shape)
#print(color_mask)
img_mask[mask] = img_mask[mask] * 0.5 + color_mask * 0.5
img_mask = cv2.putText(
img_mask, str(label_text),
(int(bboxes[i][0]), int(bboxes[i][1])),
cv2.FONT_HERSHEY_SIMPLEX, 1,
(int(color_mask[0, 0]), int(
color_mask[0, 1]), int(color_mask[0, 2])), 2)
init_rect = [
int(bboxes[i][0]),
int(bboxes[i][1]),
int(bboxes[i][2]) - int(bboxes[i][0]),
int(bboxes[i][3]) - int(bboxes[i][1])
]
#print(init_rect)
tracker.init(color_img, init_rect)
inds_len = inds_len + 1
#print("banana")
continue
mask_rcnn_flag = 0
return (img_mask, img, mask_temp)
def imshow_det_bboxes(img,
bboxes,
labels,
class_names=None,
score_thr=0,
bbox_color='green',
text_color='green',
thickness=1,
font_scale=0.5,
show=True,
win_name='',
wait_time=0,
out_file=None,
ret=False):
"""Draw bboxes and class labels (with scores) on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
(n, 5).
labels (ndarray): Labels of bboxes.
class_names (list[str]): Names of each classes.
score_thr (float): Minimum score of bboxes to be shown.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str or None): The filename to write the image.
"""
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
for bbox, label in zip(bboxes, labels):
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[2], bbox_int[3])
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
if 'person' not in label_text and 'car' not in label_text:
continue
if 'person' in label_text:
bbox_color = color_val('green')
text_color = color_val('green')
else:
bbox_color = color_val('yellow')
text_color = color_val('yellow')
if len(bbox) > 4:
label_text += '|{:.02f}'.format(bbox[-1])
cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
cv2.rectangle(img,
left_top,
right_bottom,
bbox_color,
thickness=thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
if ret:
return img
def imshow_mesh_3d(img,
vertices,
faces,
camera_center,
focal_length,
colors=(76, 76, 204)):
"""Render 3D meshes on background image.
Args:
img(np.ndarray): Background image.
vertices (list of np.ndarray): Vetrex coordinates in camera space.
faces (list of np.ndarray): Faces of meshes.
camera_center ([2]): Center pixel.
focal_length ([2]): Focal length of camera.
colors (list[str or tuple or Color]): A list of mesh colors.
"""
H, W, C = img.shape
if not has_pyrender:
warnings.warn('pyrender package is not installed.')
return img
if not has_trimesh:
warnings.warn('trimesh package is not installed.')
return img
try:
renderer = pyrender.OffscreenRenderer(viewport_width=W,
viewport_height=H)
except (ImportError, RuntimeError):
warnings.warn('pyrender package is not installed correctly.')
return img
if not isinstance(colors, list):
colors = [colors for _ in range(len(vertices))]
colors = [color_val(c) for c in colors]
depth_map = np.ones([H, W]) * np.inf
output_img = img
for idx in range(len(vertices)):
color = colors[idx]
color = [c / 255.0 for c in color]
color.append(1.0)
vert = vertices[idx]
face = faces[idx]
material = pyrender.MetallicRoughnessMaterial(metallicFactor=0.2,
alphaMode='OPAQUE',
baseColorFactor=color)
mesh = trimesh.Trimesh(vert, face)
rot = trimesh.transformations.rotation_matrix(np.radians(180),
[1, 0, 0])
mesh.apply_transform(rot)
mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
scene = pyrender.Scene(ambient_light=(0.5, 0.5, 0.5))
scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
camera = pyrender.IntrinsicsCamera(fx=focal_length[0],
fy=focal_length[1],
cx=camera_center[0],
cy=camera_center[1],
zfar=1e5)
scene.add(camera, pose=camera_pose)
light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=1)
light_pose = np.eye(4)
light_pose[:3, 3] = np.array([0, -1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([0, 1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([1, 1, 2])
scene.add(light, pose=light_pose)
color, rend_depth = renderer.render(scene,
flags=pyrender.RenderFlags.RGBA)
valid_mask = (rend_depth < depth_map) * (rend_depth > 0)
depth_map[valid_mask] = rend_depth[valid_mask]
valid_mask = valid_mask[:, :, None]
output_img = (valid_mask * color[:, :, :3] +
(1 - valid_mask) * output_img)
return output_img
def imshow_det_bboxes(img,
bboxes,
labels,
class_names=None,
score_thr=0.3,
bbox_color='green',
text_color='white',
thickness=2,
font_scale=0.5,
show=True,
win_name='',
wait_time=0,
out_file=None,
image_id=0):
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
# results[image_id] = []
# do tracking
bbox_xyxy = bboxes[:,:4]
cls_conf = bboxes[..., 4]
w = bbox_xyxy[..., 2] - bbox_xyxy[..., 0]
h = bbox_xyxy[..., 3] - bbox_xyxy[..., 1]
bbox_xyxy[:, 2] = w
bbox_xyxy[:, 3] = h
index = 0
outbb = np.copy(bbox_xyxy)
dd = []
for bbox, label in zip(bboxes, labels):
if label != 0:
dd.append(index)
index += 1
outbb = np.delete(outbb, dd, 0)
cls_conf = np.delete(cls_conf, dd, 0)
labels = np.delete(labels, dd, 0)
if image_id ==1:
for i in range(2):
outputs = deepsort.update(outbb, cls_conf, img)
else:
outputs = deepsort.update(outbb, cls_conf, img)
if len(outputs) > 0:
bboxes = outputs[:, :4]
ids = outputs[:, -1]
for bbox, label, id in zip(bboxes, labels, ids):
if label == 0:
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[2], bbox_int[3])
cv2.rectangle(
img, left_top, right_bottom, bbox_color, thickness=thickness)
label_text = 'ID {}'.format(id)
if len(bbox) > 4:
label_text += '|{:.02f}'.format(bbox[-1])
_height_half = int((bbox_int[3]-bbox_int[1])/2)
cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] + _height_half),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
mybbox = bbox.astype(np.float).tolist()
results.append((image_id, mybbox, id))
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
def imshow_det_rbboxes_360(img,
bboxes,
labels,
class_names=None,
score_thr=0,
bbox_color='green',
text_color='red',
thickness=1,
font_scale=0.5,
show=True,
win_name='',
wait_time=0,
out_file=None):
"""Draw bboxes and class labels (with scores) on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (ndarray): Bounding boxes (with scores), shaped (n, 5) or
(n, 6).
labels (ndarray): Labels of bboxes.
class_names (list[str]): Names of each classes.
score_thr (float): Minimum score of bboxes to be shown.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str or None): The filename to write the image.
"""
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 5 or bboxes.shape[1] == 6
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 6
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
for bbox, label in zip(bboxes, labels):
xc, yc, w, h, ag, p = bbox.tolist()
wx, wy = w / 2 * math.cos(ag), w / 2 * math.sin(ag)
hx, hy = -h / 2 * math.sin(ag), h / 2 * math.cos(ag)
p1 = (xc - wx - hx, yc - wy - hy)
p2 = (xc + wx - hx, yc + wy - hy)
p3 = (xc + wx + hx, yc + wy + hy)
p4 = (xc - wx + hx, yc - wy + hy)
ps = np.int0(np.array([p1, p2, p3, p4]))
cv2.drawContours(img, [ps], -1, bbox_color, thickness=thickness)
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
##############
# transfer 4 directions to 360
ag = ag / math.pi * 180
if "RUP" in label_text:
ag += 90
elif "RDOWN" in label_text:
ag += 180
elif "LDOWN" in label_text:
ag += 270
else:
ag += 360
if ag < 0:
ag += 360
print("negative:{},{}".format(ag, out_file))
# Draw direction line
p1_x, p1_y = xc, yc
p2_x, p2_y = xc, yc - 80
rotateMat = cv2.getRotationMatrix2D((p1_x, p1_y), -ag, 1)
[[p1_x], [p1_y]] = np.dot(rotateMat, np.array([[p1_x], [p1_y], [1]]))
[[p2_x], [p2_y]] = np.dot(rotateMat, np.array([[p2_x], [p2_y], [1]]))
cv2.line(img, (int(p1_x), int(p1_y)), (int(p2_x), int(p2_y)),
text_color,
thickness=thickness)
label_text = label_text.split('-')[0]
##############
if len(bbox) > 5:
label_text += '|{:.02f}, {:.02f}'.format(bbox[-1], ag)
cv2.putText(img, label_text, (int(p1[0]), int(p1[1])),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
def imshow_det_rbboxes(img,
bboxes,
labels,
class_names=None,
score_thr=0,
bbox_color='green',
text_color='green',
thickness=1,
font_scale=0.5,
show=True,
win_name='',
wait_time=0,
out_file=None):
"""Draw bboxes and class labels (with scores) on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (ndarray): Bounding boxes (with scores), shaped (n, 5) or
(n, 6).
labels (ndarray): Labels of bboxes.
class_names (list[str]): Names of each classes.
score_thr (float): Minimum score of bboxes to be shown.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str or None): The filename to write the image.
"""
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 5 or bboxes.shape[1] == 6
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 6
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
for bbox, label in zip(bboxes, labels):
xc, yc, w, h, ag, p = bbox.tolist()
wx, wy = w / 2 * math.cos(ag), w / 2 * math.sin(ag)
hx, hy = -h / 2 * math.sin(ag), h / 2 * math.cos(ag)
p1 = (xc - wx - hx, yc - wy - hy)
p2 = (xc + wx - hx, yc + wy - hy)
p3 = (xc + wx + hx, yc + wy + hy)
p4 = (xc - wx + hx, yc - wy + hy)
ps = np.int0(np.array([p1, p2, p3, p4]))
cv2.drawContours(img, [ps], -1, bbox_color, thickness=thickness)
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
if len(bbox) > 5:
label_text += '|{:.02f}'.format(bbox[-1])
cv2.putText(img, label_text, (int(p1[0]), int(p1[1])),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
def imdraw_det_bboxes(img,
bboxes,
class_names,
car_model_dict,
camera_matrix,
trans_pred_world,
euler_angle,
color_lists,
score_thr=0,
bbox_color='green',
text_color='green',
thickness=1,
font_scale=0.5):
"""Draw bboxes and class labels (with scores) on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
(n, 5).
labels (ndarray): Labels of bboxes.
class_names (list[str]): Names of each classes.
score_thr (float): Minimum score of bboxes to be shown.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str or None): The filename to write the image.
"""
assert bboxes.ndim == 2
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img_original = img.copy()
img = img_original[1480:, :, :]
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
trans_pred_world = trans_pred_world[inds, :]
euler_angle = euler_angle[inds, :]
assert len(bboxes) == len(trans_pred_world) == len(euler_angle)
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
for bbox_idx in range(len(bboxes)):
bbox = bboxes[bbox_idx]
label_text = class_names[bbox_idx]
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[2], bbox_int[3])
cv2.rectangle(img,
left_top,
right_bottom,
bbox_color,
thickness=thickness)
if len(bbox) > 4:
label_text += '|{:.02f}'.format(bbox[-1])
cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
# now we draw mesh
vertices = np.array(car_model_dict[class_names[bbox_idx]]['vertices'])
vertices[:, 1] = -vertices[:, 1]
triangles = np.array(
car_model_dict[class_names[bbox_idx]]['faces']) - 1
t = trans_pred_world[bbox_idx]
ea = euler_angle[bbox_idx]
yaw, pitch, roll = ea[0], ea[1], ea[2]
yaw, pitch, roll = -pitch, -yaw, -roll
Rt = np.eye(4)
Rt[:3, 3] = t
Rt[:3, :3] = euler_to_Rot(yaw, pitch, roll).T
Rt = Rt[:3, :]
P = np.ones((vertices.shape[0], vertices.shape[1] + 1))
P[:, :-1] = vertices
P = P.T
img_cor_points = np.dot(camera_matrix, np.dot(Rt, P))
img_cor_points = img_cor_points.T
img_cor_points[:, 0] /= img_cor_points[:, 2]
img_cor_points[:, 1] /= img_cor_points[:, 2]
color_mesh = np.int32(color_lists[bbox_idx][0])
color_tuple = tuple([int(x) for x in color_mesh])
for t in triangles:
coord = np.array([
img_cor_points[t[0]][:2], img_cor_points[t[1]][:2],
img_cor_points[t[2]][:2]
],
dtype=np.int32)
# This will draw the mask for segmenation
# cv2.drawContours(mask_seg, np.int32([coord]), 0, (255, 255, 255), -1)
coord[:, 1] -= 1480
cv2.polylines(img, np.int32([coord]), 1, color=color_tuple)
im_combime = img_original.copy()
im_combime[1480:, :, :] = img
return im_combime
def imshow_det_bboxes(img,
bboxes,
labels,
class_names=None,
score_thr=0.3,
bbox_color='green',
text_color='white',
thickness=2,
font_scale=0.5,
show=True,
win_name='',
wait_time=0,
out_file=None,
image_id=0):
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img = imread(img)
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
# results[image_id] = []
# do tracking
bbox_xyxy = bboxes[:,:4]
cls_conf = bboxes[..., 4]
w = bbox_xyxy[..., 2] - bbox_xyxy[..., 0]
h = bbox_xyxy[..., 3] - bbox_xyxy[..., 1]
bbox_xyxy[:, 2] = w
bbox_xyxy[:, 3] = h
# 去除label不是人的BBox
index = 0
outbb = np.copy(bbox_xyxy)
dd = []
for bbox, label in zip(bboxes, labels):
if label != 0:
dd.append(index)
index += 1
outbb = np.delete(outbb, dd, 0)
cls_conf = np.delete(cls_conf, dd, 0)
labels = np.delete(labels, dd, 0)
# 去除BBox过小
BBox_area_sixze = w * h
index_filter = 0
outbb_filter = np.copy(outbb)
dd_filter = []
for bbox, size in zip(outbb_filter, BBox_area_sixze):
if size <= 200:
dd_filter.append(index_filter)
index_filter += 1
outbb_filter = np.delete(outbb_filter, dd_filter, 0)
cls_conf = np.delete(cls_conf, dd_filter, 0)
labels = np.delete(labels, dd_filter, 0)
# 去除BBox过小
BBox_area_h = h
index_filter = 0
ddd_filter = []
for bbox, size in zip(outbb_filter, BBox_area_h):
if size <= 35:
ddd_filter.append(index_filter)
index_filter += 1
outbb_filter = np.delete(outbb_filter, ddd_filter, 0)
cls_conf = np.delete(cls_conf, ddd_filter, 0)
labels = np.delete(labels, ddd_filter, 0)
# if image_id ==1:
# for i in range(4):
# outputs = tracker.update(img, outbb_filter, cls_conf)
# else:
# outputs = tracker.update(img, outbb_filter, cls_conf)
outputs = tracker.update(img, outbb_filter, cls_conf)
online_tlwhs = []
online_ids = []
for t in outputs:
online_tlwhs.append(t.tlwh)
online_ids.append(t.track_id)
if len(online_ids) > 0:
bboxes = online_tlwhs
ids = online_ids
for bbox, label, id, conf in zip(bboxes, labels, ids, cls_conf):
if label == 0:
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[0]+bbox_int[2], bbox_int[1]+bbox_int[3])
cv2.rectangle(
img, left_top, right_bottom, bbox_color, thickness=thickness)
label_text = 'ID{} cnf{:.2}'.format(id, conf)
if len(bbox) > 4:
label_text += '|{:.02f}'.format(bbox[-1])
_height_half = int((bbox_int[3]-bbox_int[1])/2)
cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] + _height_half),
cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
mybbox = bbox.astype(np.float).tolist()
results.append((image_id, mybbox, id))
# results.append((image_id, mybbox, id, conf))
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
def imshow_det_bboxes(img,
bboxes,
labels,
class_names=None,
score_thr=0,
bbox_color='green',
text_color='black',
thickness=4,
font_scale=1.1,
show=True,
win_name='',
wait_time=0,
out_file=None,
dataset=None):
"""Draw bboxes and class labels (with scores) on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
(n, 5).
labels (ndarray): Labels of bboxes.
class_names (list[str]): Names of each classes.
score_thr (float): Minimum score of bboxes to be shown.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str or None): The filename to write the image.
"""
# import random
# color = "%06x" % random.randint(0, 0xFFFFFF)
# from splits import COCO_ALL_CLASSES
# color_map = {label: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for label in COCO_ALL_CLASSES}
assert bboxes.ndim == 2
assert labels.ndim == 1
assert bboxes.shape[0] == labels.shape[0]
assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
img = imread(img)
unseen_labels = get_unseen_class_labels(dataset, '177_23')
if score_thr > 0:
assert bboxes.shape[1] == 5
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
bbox_color = color_val(bbox_color)
text_color = color_val(text_color)
# import pdb; pdb.set_trace()
for bbox, label in zip(bboxes, labels):
bbox_int = bbox.astype(np.int32)
left_top = (bbox_int[0], bbox_int[1])
right_bottom = (bbox_int[2], bbox_int[3])
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
if label_text in unseen_labels:
bbox_color = color_val('red')
# print(label_text)
else:
bbox_color = color_val('green')
print(bbox_color, label_text)
# bbox_color = color_map[label_text] if color_map is not None else bbox_color
cv2.rectangle(img,
left_top,
right_bottom,
bbox_color,
thickness=thickness)
label_text = label_text.title().replace('_', ' ')
# if len(bbox) > 4:
# label_text += ': {}'.format(int(100*bbox[-1]))
# import pdb; pdb.set_trace()
txt_thickness = 4
font = cv2.FONT_HERSHEY_SIMPLEX
(text_width, text_height) = cv2.getTextSize(label_text,
font,
fontScale=font_scale,
thickness=txt_thickness)[0]
text_offset_x = bbox_int[0]
text_offset_y = bbox_int[1] - 2
box_coords = ((text_offset_x, text_offset_y),
(text_offset_x + text_width + 2,
text_offset_y - text_height - 2))
rectangle_bgr = (255, 255, 255)
cv2.rectangle(img, box_coords[0], box_coords[1], rectangle_bgr,
cv2.FILLED)
cv2.putText(img,
label_text, (bbox_int[0], bbox_int[1] - 2),
font,
font_scale,
text_color,
thickness=txt_thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
