def visualize_heatmaps(self, img, cls_map, reg_map, clusters, prob_thresh=1, nms_thresh=1, iou=None):
"""
Expect cls_map and reg_map to be of the form HxWxC
"""
fy, fx, fc = np.where(cls_map >= prob_thresh)
# print(iou.shape)
# best_iou = iou.max(axis=3)
# print(best_iou.shape)
# fy, fx, fc = np.where(best_iou >= 0.5) # neg thresh
cy, cx = fy*self.sty + self.ofy, fx*self.stx + self.ofx
cw = clusters[fc, 2] - clusters[fc, 0] + 1
ch = clusters[fc, 3] - clusters[fc, 1] + 1
# box_ovlp = best_iou[fc, fy, fx]
num_clusters = clusters.shape[0]
# refine bounding box
tx = reg_map[:, :, 0*num_clusters:1*num_clusters]
ty = reg_map[:, :, 1*num_clusters:2*num_clusters]
tw = reg_map[:, :, 2*num_clusters:3*num_clusters]
th = reg_map[:, :, 3*num_clusters:4*num_clusters]
dcx = cw * tx[fy, fx, fc]
dcy = ch * ty[fy, fx, fc]
rx = cx + dcx
ry = cy + dcy
rw = cw * np.exp(tw[fy, fx, fc])
rh = ch * np.exp(th[fy, fx, fc])
bboxes = np.array([np.abs(rx-rw/2), np.abs(ry-rh/2), rx+rw/2, ry+rh/2]).T
scores = cls_map[fy, fx, fc]
dets = np.hstack((bboxes, scores[:, np.newaxis]))
keep = nms(dets, nms_thresh)
bboxes = dets[keep][:, 0:4]
# bbox_iou = best_iou[fy, fx, fc]
# print("Best bounding box", bboxes)
# print(bboxes.shape)
print("Number of bboxes ", bboxes.shape[0])
for idx, bbox in enumerate(bboxes):
bbox = np.round(np.array(bbox))
print(bbox)
# img = draw_bounding_box(img, bbox, {"name": "car {0}".format(np.around(bbox_iou[idx], decimals=2))})
img = draw_bounding_box(img, bbox, {"name": "car {0}".format(idx)})
# if idx == 20:
# break
img.show(title="Heatmap visualized")
def visualize_bboxes(image, bboxes):
"""
:param image: PIL image
:param bboxes:
:return:
"""
print("Number of GT bboxes", bboxes.shape[0])
for idx, bbox in enumerate(bboxes):
bbox = np.round(np.array(bbox))
# print(bbox)
image = draw_bounding_box(image, bbox, {"name": "car {0}".format(idx)})
image.show(title="GT BBox")
def render_and_save_bboxes(image, image_id, bboxes, scores, scales, directory="qualitative"):
"""
Render the bboxes on the image and save the image
:param image: PIL image
:param image_id:
:param bboxes:
:param scores:
:param scales:
:param directory:
:return:
"""
for idx, bbox in enumerate(bboxes):
bbox = np.round(np.array(bbox))
image = draw_bounding_box(image, bbox, {'score': scores[idx], 'scale': scales[idx]})
image.save("{0}/{1}.jpg".format(directory, image_id))