def post_process(dets_out,
img,
h,
w,
top_k=1,
score_threshold=0.6,
undo_transform=True):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=False,
crop_masks=False,
score_threshold=score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < score_threshold:
num_dets_to_consider = j
break
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
# After this, mask is of size [num_dets, h, w, 1]
final_res = (img_gpu * 255).byte().cpu().numpy()
final_res = cv2.cvtColor(final_res, cv2.COLOR_RGB2RGBA)
if num_dets_to_consider == 0:
return final_res
masks = masks[:num_dets_to_consider, :, :, None]
_mask = (masks * 255).byte().cpu().numpy()[0]
# Then assign the mask to the last channel of the image
final_res[:, :, 3] = _mask.squeeze()
return final_res
def plot_tfboard_figure(cfg, vis_imgs, vis_show=False, show_grad=False, max_vis=3):
num_col = len(vis_imgs.keys())+1 if show_grad else len(vis_imgs.keys())
bs = vis_imgs['gts'].size(0)
vis_idxs = np.random.choice(bs, min(max_vis, bs), replace=False)
fig, ax = plt.subplots(max(2, len(vis_idxs)), num_col)
# show each image in one line
for k, ik in enumerate(vis_idxs):
rgb_img = vis_imgs['rgb'][ik]
tp = vis_imgs['preds'][ik,0].cpu().detach().numpy()
tg = vis_imgs['gts'][ik,0].cpu().detach().numpy()
tw = vis_imgs['wghts'][ik,0].cpu().detach().numpy()
# for multiple gpus
if torch.cuda.device_count()>1:
tp, tg, tw = tp[0], tg[0], tw[0]
rgb_img = rgb_img[0]
ti = undo_image_transformation(cfg.backbone, rgb_img)
ax[k, 0].imshow(ti)
ax[k, 1].imshow(tg)
plt.colorbar(ax[k, 2].imshow(tw), ax=ax[k, 2])
plt.colorbar(ax[k, 3].imshow(tp), ax=ax[k, 3])
if show_grad:
tpg = vis_imgs['grad'][ik].cpu().detach().numpy()
plt.colorbar(ax[k, 5].imshow(tpg), ax=ax[k, 5])
# close axis
for i in range(num_col):
ax[k,i].axis('off')
ax[k,i].tick_params(axis='both', left=False, top=False,
right=False, bottom=False,
labelright=False, labelbottom=False)
if vis_show:
plt.show()
return fig
def prep_display(self, dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, image_header=Header()):
with torch.no_grad():
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
dets = Detections()
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out, w, h, visualize_lincomb = False,
crop_masks = True,
score_threshold = 0.3)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:100]
classes, scores, boxes = [x[:100].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(100, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < 0.3:
num_dets_to_consider = j
break
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score)
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
det = Detection()
det.box.x1 = x1
det.box.y1 = y1
det.box.x2 = x2
det.box.y2 = y2
det.class_name = _class
det.score = score
mask_shape = np.shape(masks[j])
#print("Num dets: ", num_dets_to_consider)
#print("Shape: ", mask_shape)
mask_bb = np.squeeze(masks[j].cpu().numpy(), axis=2)[y1:y2,x1:x2]
#print("Box: ", x1,",",x2,",",y1,",",y2)
#print("Mask in box shape: ", np.shape(mask_bb))
mask_rs = np.reshape(mask_bb, -1)
#print("New shape: ", np.shape(mask_rs))
#print("Mask:\n",mask_bb)
det.mask.height = y2 - y1
det.mask.width = x2 - x1
det.mask.mask = np.array(mask_rs, dtype=bool)
dets.detections.append(det)
dets.header.stamp = image_header.stamp
dets.header.frame_id = image_header.frame_id
self.detections_pub.publish(dets)
return img_numpy
def prep_display(dets_out, img, gt, gt_masks, h, w, undo_transform=True, class_color=False):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
gt and gt_masks are also allowed to be none (until I reimplement that functionality).
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out, w, h, visualize_lincomb=args.display_lincomb, crop_masks=args.crop, score_threshold=args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
masks = t[3][:args.top_k] # We'll need this later
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
if classes.shape[0] == 0:
return (img_gpu * 255).byte().cpu().numpy()
def get_color(j):
color = COLORS[(classes[j] * 5 if class_color else j * 5) % len(COLORS)]
if not undo_transform:
color = (color[2], color[1], color[0])
return color
# Draw masks first on the gpu
if args.display_masks and cfg.eval_mask_branch:
for j in reversed(range(min(args.top_k, classes.shape[0]))):
if scores[j] >= args.score_threshold:
color = get_color(j)
mask = masks[j, :, :, None]
mask_color = mask @ (torch.Tensor(color).view(1, 3) / 255.0)
mask_alpha = 0.45
# Alpha only the region of the image that contains the mask
img_gpu = img_gpu * (1 - mask) \
+ img_gpu * mask * (1-mask_alpha) + mask_color * mask_alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_text or args.display_bboxes:
for j in reversed(range(min(args.top_k, classes.shape[0]))):
score = scores[j]
if scores[j] >= args.score_threshold:
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = COCO_CLASSES[classes[j]]
text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
return img_numpy
def prep_display(dets_out,
img,
h,
w,
args,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].detach().cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
masks = masks[:num_dets_to_consider, :, :, None]
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
inv_alph_masks = masks * (-mask_alpha) + 1
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
img_numpy_mask = (masks_color_summand * 255).byte().cpu().numpy()
cv2.imwrite('results/mask_car_image.jpg', img_numpy_mask)
print("Mask for all visible car is generated")
if args.display_best_masks_only == True and args.top_k == 1:
masks = masks[:num_dets_to_consider, :, :, None]
num_dets_to_consider = min(args.top_k, classes.shape[0])
print('maskshape', (masks.shape))
for i in range(num_dets_to_consider):
msk = masks[i, :, :, None]
mask = msk.view(1, masks.shape[1], masks.shape[2], 1)
print('newmaskshape', (mask.shape))
img_gpu_masked = img_gpu * (mask.sum(dim=0) >= 1).float().expand(
-1, -1, 3)
img_numpy_masked = (img_gpu_masked * 255).byte().cpu().numpy()
cv2.imwrite('results/mask_image' + str(i) + '.jpg',
img_numpy_masked)
print("Mask for the most visible car is generated")
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_best_bboxes_only == 'True':
crop = img_numpy[y1:y2, x1:x2]
cv2.imwrite('results/crop_object.png', crop)
print("crop for the most visible car is generated")
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (
_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
# print(img.shape) # torch.Size([480, 360, 3])
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
# 这里面取了最高分的k个，由传入参数设定
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
# 获取到了最高k个的类别、分数、框，因此可以在这里进行修改
# print(classes) # 类别说明 class 0: person, class 2: car
# print(scores)
# print(boxes)
# 定义变量area_b，框的面积
person_index = (classes == 0)
# person_index表示了第几个框是否是person类别
if person_index.any():
# 存在person这个类别
boxes = boxes[person_index]
scores = scores[person_index]
# 对person的框面积进行计算
area = np.zeros(len(scores))
for i in range(person_index.sum()):
box = boxes[i]
area[i] = (box[2] - box[0]) * (box[3] - box[1])
# 对person的框面积进行筛选
# 假设最小的人的面积: 25*100 像素，并约束阈值
# valid_person_index = ((area >= 2500) and (scores < 0.01))
valid_person_index = (area >= 2500)
boxes = boxes[valid_person_index]
scores = scores[valid_person_index]
if valid_person_index.any():
# 筛选面积和阈值之后还有person
print('----- Person detected -----')
else:
# 筛选面积和阈值之后已经没有person了
print('----- No person -----')
num_dets_to_consider = valid_person_index.sum()
else:
# 直接就没有person类
print('----- No person -----')
num_dets_to_consider = 0
if num_dets_to_consider == 0:
# 没检测到人，返回原图
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
# img_numpy = (masks * 255).byte().cpu().numpy()
# 检测到框并输出文字
for j in reversed(range(num_dets_to_consider)):
# 这个循环中的boxes, scores, classes都要减少一个维度
x1, y1, x2, y2 = boxes[j][:]
color = get_color(classes[j])
score = scores[j]
# 绘制检测框
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
# 显示检测结果的文本
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score
) # if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale,
font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4),
color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
return img_numpy
def prep_display_for_video(dets_out,
img,
h=None,
w=None,
save_folder=None,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str='',
override_args: Config = None):
if undo_transform:
assert w is not None and h is not None, "with undo_transform=True, w,h params must be specified!"
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
img_numpy_ori = (img_gpu * 255).byte().cpu().numpy()
global args
if override_args is not None:
args = override_args
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
masks = t[3][idx]
classes, scores, boxes = [x[idx] for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] if class_color else j) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
global frame_compare
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
if frame_compare != save_folder[4]:
masks = masks[:num_dets_to_consider, :, :, None]
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
inv_alph_masks = masks * (-mask_alpha) + 1
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(
dim=0) + masks_color_summand
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if num_dets_to_consider == 0:
if os.path.isdir(
save_folder[0]) and save_folder[4] % args.video_fps == 0:
file_name = save_folder[1] + "_%05d" % save_folder[4] + '.png'
cv2.imwrite(os.path.join(save_folder[3], file_name), img_numpy)
cv2.imwrite(os.path.join(save_folder[2], file_name), img_numpy_ori)
return [img_numpy, img_numpy_ori]
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
if args.display_text or args.display_bboxes:
if frame_compare != save_folder[4]:
frame_compare = save_folder[4]
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
# text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
if args.display_scores:
text_str_class = f"{_class}"
text_str_score = f": {score:.2f}"
text_w_class, text_h_class = \
cv2.getTextSize(text_str_class, font_face, font_scale, font_thickness)[0]
img_numpy = ps.putBText(img_numpy,
text_str_class,
text_offset_x=x1,
text_offset_y=y1,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
img_numpy = ps.putBText(img_numpy,
text_str_score,
text_offset_x=x1,
text_offset_y=y1 +
text_h_class + 2,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
else:
text_str_class = '%s' % (_class)
img_numpy = ps.putBText(img_numpy,
text_str_class,
text_offset_x=x1,
text_offset_y=y1,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
if save_folder[4] % args.video_fps == 0:
dist = ocr(img_numpy_ori)
result = save_folder[
4], f"{dist}", f"{_class}", f"{score:.2f}", f"{x1}", f"{y1}", f"{x2}", f"{y2}"
result_list.append(result)
if os.path.isdir(
save_folder[0]) and save_folder[4] % args.video_fps == 0:
file_name = save_folder[1] + "_%05d" % save_folder[4] + '.png'
cv2.imwrite(os.path.join(save_folder[3], file_name), img_numpy)
cv2.imwrite(os.path.join(save_folder[2], file_name),
img_numpy_ori)
return [img_numpy, img_numpy_ori, result_list]
return [img_numpy, img_numpy_ori]
def prep_display(self,
dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:args.top_k]
classes, scores, boxes = [
x[:args.top_k].cpu().numpy() for x in t[:3]
]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j *
5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(
dim=0) + masks_color_summand
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_text or args.display_bboxes:
str_ = ""
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (
_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale,
font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
#pub = rospy.Publisher('chatter',String,queue_size=10)
#rate = rospy.Rate(50) #10hz
#str_ += text_str
#rospy.loginfo(str_)
#pub.publish(str_)
#rate.sleep()
return img_numpy
def prep_display(self,
dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
batch_idx=0,
create_mask=False,
return_imgs=False):
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
#print(h, " ", w)
with timer.env('Postprocess'):
t = postprocess(dets_out,
w,
h,
batch_idx,
visualize_lincomb=self.args.display_linecomb,
crop_masks=self.args.crop,
score_threshold=self.args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
masks = t[3][:self.args.top_k]
classes, scores, boxes = [
x[:self.args.top_k].cpu().numpy() for x in t[:3]
]
num_dets_to_consider = min(self.args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < self.args.score_threshold:
num_dets_to_consider = j
break
idx_fil = []
for i in range(num_dets_to_consider):
if cfg.dataset.class_names[
classes[i]] == 'car' or cfg.dataset.class_names[
classes[i]] == 'truck':
idx_fil.append(i)
num_dets_to_consider = len(idx_fil)
if num_dets_to_consider == 0:
# no detection found so just output original image
if not create_mask:
return (img_gpu * 255).byte().cpu().numpy()
elif return_imgs:
return (img_gpu * 255).byte().cpu().numpy(), ImageResult(
None, None, None, np.zeros((h, w, 1), dtype='uint8'), 0)
else:
return ImageResult(None, None, None,
np.zeros((h, w, 1), dtype='uint8'), 0)
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
color_idx = (classes[j] * 5 if class_color else j *
5) % len(COLORS)
if on_gpu is not None and color_idx in self.color_cache[on_gpu]:
return self.color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
self.color_cache[on_gpu][color_idx] = color
return color
if self.args.display_masks and cfg.eval_mask_branch:
# after this, mask is of size [num_dets, h, w, l]
#masks = masks[:num_dets_to_consider, :, :, None]
#classes = classes[:num_dets_to_consider]
#scores = scores[:num_dets_to_consider]
#boxes = boxes[:num_dets_to_consider, :]
masks = masks[idx_fil, :, :, None]
classes = classes[idx_fil]
scores = scores[idx_fil]
boxes = boxes[idx_fil, :]
if create_mask:
mask_img = np.zeros((h, w, 1), dtype='uint8')
for j in range(num_dets_to_consider):
mask_img += 10 * (j + 1) * masks[j].cpu().numpy().astype(
np.uint8)
if not return_imgs:
return ImageResult(classes, scores, boxes, mask_img,
num_dets_to_consider)
# prepare the rgb image for each mask given their color (of size [num_dets, w, h, l])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# this is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(
dim=0) + masks_color_summand
# then draw the stuff that needs to be done on cpu
# note make sure this is a uint8 tensor or opencv will not anti aliaz text for wahtever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if self.args.display_text or self.args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if self.args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if self.args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (
_class, score) if self.args.display_scores else _class
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
return img_numpy, ImageResult(classes, scores, boxes, mask_img,
num_dets_to_consider)
def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
# print("height:", h, "width:", w)
with timer.env('Postprocess'):
t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,
crop_masks = args.crop,
score_threshold = args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:args.top_k]
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_masks and cfg.eval_mask_branch:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
has_stacked = False
if args.display_text or args.display_bboxes:
bboxes = {
'cp': [],
'qp': [],
'op': [],
'tray': []
}
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
print(classes[j], x1, y1, x2, y2)
if (classes[j] == 2): # cp stacked
bboxes['cp'].append([x1, y1, x2, y2])
has_stacked = True
elif (classes[j] == 5): #qp stacked
bboxes['qp'].append([x1, y1, x2, y2])
has_stacked = True
elif (classes[j] == 8): #op stacked
bboxes['op'].append([x1, y1, x2, y2])
has_stacked = True
elif (classes[j] == 9): #tray
bboxes['tray'].append([x1, y1, x2, y2])
# print("Crop_tray:",crop_tray_img)
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_color = [255, 255, 255]
text_pt = (x1, y1 - 3)
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
# counting not visible patties
# 1 - non overlapped, 2- overlapped, 3 - stacked
# 1 - ch, 2 qp, 3 op
n_patties = np.sum(classes!=9)
cp_count = np.sum(classes==0) + np.sum(classes==1)
qp_count = np.sum(classes==3) + np.sum(classes==4)
op_count = np.sum(classes==6) + np.sum(classes==7)
visible_cp = np.sum(classes==0) + np.sum(classes==1) + np.sum(classes==2)
visible_qp = np.sum(classes==3) + np.sum(classes==4) + np.sum(classes==5)
visible_op = np.sum(classes==6) + np.sum(classes==7) + np.sum(classes==8)
# find the maximum of the three by adding count to a list and choose model accordingly
labels = [visible_cp, visible_qp, visible_op]
max_index = labels.index(max(labels))
stacked_boxes = []
if max_index == 0:
model_path = "weights/regressor/cp_stack_regressor"
stacked_boxes = bboxes['cp']
elif max_index == 1:
model_path = "weights/regressor/qp_stack_regressor"
stacked_boxes = bboxes['qp']
else:
model_path = "weights/regressor/op_stack_regressor"
stacked_boxes = bboxes['op']
# sort the bboxes accordingly
stacked_boxes = sorted(stacked_boxes, key=lambda x: x[1])
print('found stacked', len(stacked_boxes))
#stacked_boxes = remove_overlapping(stacked_boxes)
#### calculation of X
if len(bboxes['tray']) > 0:
prediction = 0
tray_height = bboxes['tray'][0][3] - bboxes['tray'][0][1]
loaded_model = pickle.load(open(model_path, 'rb'))
for box in stacked_boxes:
stack_height = box[3] - box[1]
gap = box[1] - bboxes['tray'][0][1]
X = np.array((gap / tray_height, stack_height / tray_height))
this_prediction = round(loaded_model.predict(X.reshape(1, -1))[0])
print("this prediction: ", this_prediction)
prediction = prediction + this_prediction
# load the pickle model in memory, scale the input and feed it into the model
if max_index == 0:
cp_count = cp_count + prediction
elif max_index == 1:
qp_count = qp_count + prediction
else:
op_count = op_count + prediction
# print("model_path:",model_path)
count_text = "VISIBLE: {} ... CP: {}, QP: {}, OP: {}".format(n_patties, cp_count, qp_count, op_count)
count_text_w, count_text_h = cv2.getTextSize(count_text, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
padding = 20
count_text_pt = (w - count_text_w - padding, h - count_text_h)
cv2.putText(img_numpy, count_text, count_text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA )
# print(count_text)
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
# print(img.shape) # torch.Size([480, 360, 3])
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:args.top_k]
# 这里面取了最高分的k个，由传入参数设定
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
# 获取到了最高k个的类别、分数、框，因此可以在这里进行修改
# print(classes) # 类别说明 class 0: person, class 2: car
# print(scores)
# print(boxes)
"""
index_person = 0
person_found = True
# 遍历类别数组，如果遇到person就跳出
while (classes[index_person]):
# 这样当class是0的时候，检测的就是person，就记录下index
index_person += 1
# 如果整个图片都没找到person
if (index_person == args.top_k):
person_found = False
break
if (not person_found):
print('----- No person -----')
num_dets_to_consider = 0
else:
# 这里加入了一个修改，把除了person之外的其他检测结果屏蔽掉
classes_all, scores_all, boxes_all = classes, scores, boxes
classes = classes_all[index_person]
scores = scores_all[index_person]
boxes = boxes_all[index_person]
num_dets_to_consider = 1
# print(masks.shape) # torch.Size([10, 480, 360])
masks_all = masks
masks = masks_all[index_person]
"""
# 之前的方法有个BUG，就是对小person的score大于主要person时，输出错误的结果，修改如下
# 定义变量area_b，框的面积
person_index = (classes == 0)
# person_index表示了第几个框是否是person类别
if person_index.any():
# 存在person这个类别
# 如果只检测到1个人，直接取这个人就可以
classes = classes[person_index]
scores = scores[person_index]
boxes = boxes[person_index]
masks = masks[person_index]
if (person_index.sum() > 1):
# 检测到多个人，需要取最大面积框
# 之前已经把person类过滤出来了，还需要逐个算面积
area = classes
for i in range(person_index.sum()):
box = boxes[i]
area[i] = (box[2] - box[0]) * (box[3] - box[1])
# 最后再从person这类里面的框中挑选出最大面积的那个
person_index = (area == area.max())
classes = classes[person_index]
scores = scores[person_index]
boxes = boxes[person_index]
masks = masks[person_index]
num_dets_to_consider = 1
else:
# 没有person类
print('----- No person -----')
num_dets_to_consider = 0
# raise Exception("Keyboard~")
# 因为只保留一个框，因此不进行阈值测试
"""
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
"""
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch:
# After this, mask is of size [num_dets, h, w, 1]
# 这里需要删掉第一个维度
# masks = masks[:num_dets_to_consider, :, :, None]
# print(masks.shape) # torch.Size([1, 480, 360])
if (num_dets_to_consider):
masks = masks[:, :, :, None]
else:
masks = []
# debug settings
# print(masks.shape) # torch.Size([1, 480, 360, 1])
# mask_img = np.reshape(masks.cpu().numpy(), [480, 360])
# print(np.max(mask_img)) # >>> 1.0
# cv2.namedWindow('Test', cv2.WINDOW_AUTOSIZE)
# cv2.imshow('Test', mask_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# cv2.imwrite('test.png', mask_img)
mask_img = (masks * 255).byte().cpu().numpy()
# print(masks.shape)
# print(mask_img.shape) # (480, 360, 1)
# 这里需要删掉第一个维度
mask_img = mask_img[0, :, :, 0]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
# debug
# print(masks.repeat(1, 1, 1, 3).shape)
# print(colors.shape) # torch.Size([1, 1, 1, 3])
# masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# print(np.max(masks_color.cpu().numpy()))
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
# 这里的benchsize全都改成1了
masks_color = masks.repeat(1, 1, 1,
3) * colors * mask_alpha # 注意这个时候还是float小数
# 这句不用管，反正执行不到，无视就行
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
masks_color_summand = masks_color[0]
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
# debug看一下图像有没有问题，看完再注释掉
# cv2.namedWindow('Debug', cv2.WINDOW_AUTOSIZE)
# cv2.imshow('Debug', img_gpu.cpu().numpy())
# cv2.waitKey(0)
# cv2.destroyAllWindows() # --- 貌似一直到这都没毛病的~
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
# img_numpy = (masks * 255).byte().cpu().numpy()
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
# 这个循环中的boxes, scores, classes都要减少一个维度
x1, y1, x2, y2 = boxes[:]
color = get_color(0)
score = scores
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes]
text_str = '%s: %.2f' % (
_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
img_crop = img.byte().cpu().numpy()
# print(np.max(mask_img))
for i in range(3):
img_crop[:, :, i] = img_crop[:, :, i] * (mask_img // 255)
# debug看一下图像有没有问题，看完再注释掉
# cv2.namedWindow('Debug', cv2.WINDOW_AUTOSIZE)
# cv2.imshow('Debug', img_crop)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return img_numpy, mask_img, img_crop
def prep_display(self,
dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
lineThickness = 2
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=self.display_lincomb,
crop_masks=self.crop,
score_threshold=self.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
# idx = t[1].argsort(0, descending=True)[top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:self.top_k]
classes, scores, boxes = [
x[:self.top_k].cpu().detach().numpy() for x in t[:3]
]
num_dets_to_consider = min(self.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < self.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j *
5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if self.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(
dim=0) + masks_color_summand
if self.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().detach().numpy()
if self.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if self.display_text or self.display_bboxes:
distance_boxes = []
def all_subsets(ss):
return chain(
*map(lambda x: combinations(ss, x), range(0,
len(ss) + 1)))
def draw_distance(boxes):
"""
input : boxes(type=list)
Make all possible combinations between the detected boxes of persons
perform distance measurement between the boxes to measure distancing
"""
red_counter = 0 ## Countting people who are in high risk
green_counter = 0
for subset in all_subsets(boxes):
if len(subset) == 2:
a = np.array((subset[0][2], subset[0][3]))
b = np.array((subset[1][2], subset[1][3]))
dist = np.linalg.norm(
a - b
) ## Eucledian distance if you want differnt ways to measure distance b/w two boxes you can use the following options
# dist = spatial.distance.cosine(a, b)
# # print ('Eucledian distance is version-1', dist)
# # print ('Eucledian distance is', spatial.distance.euclidean(a, b))
# print ('Cosine distance is', dist)
if dist < 250:
red_counter += len(subset)
cv2.line(img_numpy, (subset[0][2], subset[0][3]),
(subset[1][2], subset[1][3]), (0, 0, 255),
lineThickness)
elif dist < 300:
green_counter += len(subset)
cv2.line(img_numpy, (subset[0][2], subset[0][3]),
(subset[1][2], subset[1][3]), (0, 255, 0),
lineThickness)
log["total_person_in_red_zone"] = red_counter // 2
log["total_person_in_green_zone"] = green_counter // 2
# gc.collect()
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if self.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if self.display_text:
_class = cfg.dataset.class_names[classes[j]]
if _class == "person":
log["total_person"] = num_dets_to_consider
distance_boxes.append(boxes[j, :].tolist())
draw_distance(distance_boxes)
text_str = '%s: %.2f' % (
_class, score) if self.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale,
font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
return img_numpy
def prep_display_mod(dets_out,
img,
h,
w,
depth_map,
rel_depth,
undo_transform=True,
mask_alpha=1.0): # was mask_alpha=0.45
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
score_threshold = 0.15
top_k = 15
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out, w, h, score_threshold=score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:top_k] # top_k = 15
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < score_threshold:
num_dets_to_consider = j
break
classes = classes[:num_dets_to_consider] # added
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
# color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS) #original
color_idx = j # black
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if num_dets_to_consider > 0: # was ...>0
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# print("masks_og.shape", masks.shape)
# begin added // filter out the person masks and class indices
people_masks_idxs = []
classes_to_mask = []
x = [
] # save the center points of the boxes in the same order as the masks
y = []
for i, j in enumerate(classes):
if j == 0: # j = 0 for person class # filter out only people's masks
people_masks_idxs.append(i)
classes_to_mask.append(j)
x1, y1, x2, y2 = boxes[i, :]
x.append(int((x1 + x2) / 2))
y.append(int((y1 + y2) / 2))
num_dets_to_consider = len(classes_to_mask)
if num_dets_to_consider == 0: # if no people, return black image
return ((img_gpu * 0).byte().cpu().numpy()
) # make it black before returning
x_arr = np.array(y)
y_arr = np.array(x)
obj_depths = []
for i in range(x_arr.size): # store the depths of the people
obj_depths.append(depth_map[x_arr[i], y_arr[i], 0])
# print("depth at object i: ", x_arr[i], y_arr[i], " : ", obj_depths[i])
obj_depths = np.array(obj_depths)
people_masks_idxs = np.array(people_masks_idxs)
sorted_idx_by_depth = np.array(
np.argsort(-obj_depths)
) # sort the masks and people_loc by depth in Descending order
# x = x[sorted_idx_by_depth]
# y = y[sorted_idx_by_depth]
obj_depths = obj_depths[sorted_idx_by_depth]
people_masks_idxs = people_masks_idxs[sorted_idx_by_depth]
depth_thres = obj_depths[0] * (
1.0 - rel_depth
) # filter out the people within the depth_threshold
people_masks_idxs = people_masks_idxs[[
i for i, v in enumerate(obj_depths) if v >= depth_thres
]]
np.array(people_masks_idxs).T.tolist()
masks = masks[people_masks_idxs]
num_dets_to_consider = len(people_masks_idxs)
colors = torch.cat(
[get_color(0, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)],
dim=0)
tmp = masks[0]
if num_dets_to_consider > 1:
for msk in masks[1:]:
tmp = tmp + msk
# print("masks.shape: ", masks.shape)
# print("tmp.shape: ", (tmp.unsqueeze(0)).shape)
masks = tmp.unsqueeze(0)
masks[masks != 0.0] = 1.0
inv_alph_masks = masks * (-mask_alpha) + 1
masks_color = (inv_alph_masks.repeat(1, 1, 1, 3)) * colors * mask_alpha
inv_alph_masks = masks.repeat(1, 1, 1, 3)
# inv_alph_masks = masks
# inv_alph_masks = masks
# print("masks : ", masks)
# masks = (masks-1.)*-1.
# print("masks : ", masks)
# inv_alph_masks = masks * (-mask_alpha)+1
# masks_color = masks_color*0.5
# end added
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
# masks_color_summand = masks_color[0]
# if num_dets_to_consider > 1:
# inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider - 1)].cumprod(dim=0)
# masks_color_cumul = masks_color[1:] * inv_alph_cumul
# masks_color_summand += masks_color_cumul.sum(dim=0)
# img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand # original
# print("inv_alph_masks.shape: ", (torch.squeeze(inv_alph_masks,0)).shape)
# print("masks_color.shape: ", (torch.squeeze(masks_color,0)).shape)
img_gpu = img_gpu * torch.squeeze(inv_alph_masks, 0) + torch.squeeze(
masks_color, 0) # added
# img_gpu = img_gpu
img_numpy = (img_gpu * 255.0).byte().cpu().numpy()
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
global first_frame, old_obj_info
name = []
mask_img = []
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
#idx = t[1].argsort(0, descending=True)[:args.top_k]
idx1 = t[1].argsort()
idx = idx1.argsort()
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
mask_picture = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
for i in range(len(classes)):
name.append(cfg.dataset.class_names[classes[i]])
mask_img.append(mask_picture[i:i + 1, :, :, None])
#obj_info, obj_num = data_save(mask_img, classes, scores, boxes)
start = time.time()
obj_info, obj_num = sort_info.data_save(mask_img, classes, name,
scores, boxes, first_frame,
old_obj_info)
end = time.time()
print('aaaaaaaaaa', end - start)
first_frame = True
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (obj_info[j][0] * 5 if class_color else j *
5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
#img_gpu = img_gpu * (masks.sum(dim=0) > 0.5).float() #only show mask
#img_gpu = img_gpu * masks[0]
#mike0225
mask_img = img_gpu * (masks.sum(dim=0) > 0.5).float() #0209
global mask_numpy
mask_numpy = (mask_img * 255).byte().cpu().numpy() #0209
mask_numpy = cv2.cvtColor(mask_numpy, cv2.COLOR_BGR2GRAY)
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
global frame_count, state_pre, flag, predict_pos, centerX, centerY, degree, mask_color, mask_flag, pub_Flag
frame_count += 1
pub_array_msg = obj_array()
for j in range(obj_num):
global img_num, temp_x, temp_y, yhat
if obj_info[j][2] != 0:
#0502-------------------------------------------------------------------
mask_image = img_gpu * (obj_info[j][3].sum(dim=0) >
0.5).float()
mask_numpy1 = (mask_image * 255).byte().cpu().numpy()
mask_color = cv2.cvtColor(mask_numpy1, cv2.COLOR_BGR2GRAY)
'''
kernel = np.ones((5,5), np.uint8)
mask_color = cv2.erode(mask_color, kernel, iterations = 1)
mask_color = cv2.dilate(mask_color, kernel, iterations = 1)
'''
mask_flag = False
#-------------------------------------------------------------------------
if frame_count % 20 == 3:
#-----------------------------
obj_info[j][5].append(mask_color)
mask_flag = True
#cv2.imwrite('/home/chien/123/test_{}.jpg'.format(j),mask_numpy1)
if len(obj_info[j][5]) > 2:
'''
for k in range(len(obj_info[j][5])):
cv2.imwrite('/home/chien/123/test_{}.jpg'.format(k),obj_info[j][5][k])
'''
obj_msg = obj_infomsg()
obj_msg.id = obj_info[j][0]
obj_msg.object_name = obj_info[j][1]
imagedata1 = np.array(obj_info[j][5])
imagedata1 = imagedata1.reshape((-1, 3, 480, 640, 1))
imagedata1 = imagedata1 / 255.
start = time.time()
yhat = model.predict(imagedata1, verbose=0)
end = time.time()
'''
print(end-start)
print('---------------')
'''
if obj_info[j][6] == []:
for i in range(5):
x1 = yhat[1][0][i][1] * 320 + 320
y1 = yhat[1][0][i][2] * 240 + 240
degree1 = arctan_recovery(
yhat[1][0][i][3], yhat[1][0][i][4])
temp_x1, temp_y1 = trans_degree(
x1, y1, degree1)
obj_info[j][6].append(
(x1, y1, temp_x1, temp_y1))
else:
for i in range(5):
x1 = yhat[1][0][i][1] * 320 + 320
y1 = yhat[1][0][i][2] * 240 + 240
degree1 = arctan_recovery(
yhat[1][0][i][3], yhat[1][0][i][4])
temp_x1, temp_y1 = trans_degree(
x1, y1, degree1)
obj_info[j][6][i] = (x1, y1, temp_x1, temp_y1)
'''
obj_info[j][6].pop(0)
x1 = yhat[1][0][4][1]*320+320
y1 = yhat[1][0][4][2]*240+240
degree1 = arctan_recovery(yhat[1][0][4][3],yhat[1][0][4][4])
temp_x1,temp_y1=trans_degree(x1,y1,degree1)
obj_info[j][6].append((x1,y1,temp_x1,temp_y1))
'''
obj_msg.x = yhat[1][0][4][
1] * 320 + 320 #yhat[1][0][3][1]*320+320
obj_msg.y = yhat[1][0][4][2] * 240 + 240
obj_msg.degree = arctan_recovery(
yhat[1][0][4][3], yhat[1][0][4][4])
tx1, ty1 = trans_degree(obj_msg.x, obj_msg.y,
obj_msg.degree)
'''
print( obj_msg.degree)
cv2.circle(img_numpy, (int(obj_msg.x),int(obj_msg.y)),5,(0, 0, 255),5)
cv2.line(img_numpy,(int(obj_msg.x+tx1),int(obj_msg.y+ty1)),(int(obj_msg.x-tx1),int(obj_msg.y-ty1)),(0,0,255),5)
'''
#print( obj_msg.degree)
pub_array_msg.Obj_list.append(obj_msg)
pub_Flag = True
obj_info[j][5].pop(0) #0->1
'''
global pointx,pointy,real_pointx,real_pointy, point_count ,use_count
use_count+=1
if use_count >=10:
pointx.append(obj_info[j][6][4][0])
pointy.append(obj_info[j][6][4][1])
point_count += 1
if point_count >= 5:
real_pointx.append(yhat[0][0][2][1]*320+320)
real_pointy.append(yhat[0][0][2][2]*240+240)
'''
if obj_info[j][6] != []:
for i in range(5):
px = obj_info[j][6][i][0]
py = obj_info[j][6][i][1]
temp_px = obj_info[j][6][i][2]
temp_py = obj_info[j][6][i][3]
cv2.circle(img_numpy, (int(px), int(py)), 5,
(0, 0, 255), 5)
cv2.line(img_numpy,
(int(px + temp_px), int(py + temp_py)),
(int(px - temp_px), int(py - temp_py)),
(0, 0, 255), 5)
color = get_color(obj_info[j][0])
score = obj_info[j][3]
if args.display_bboxes:
cv2.rectangle(img_numpy,
(obj_info[j][4][2], obj_info[j][4][4]),
(obj_info[j][4][3], obj_info[j][4][5]),
color, 1)
if args.display_text:
_class = obj_info[j][1]
#text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
text_str = '%s: %s' % (obj_info[j][0], _class
) if args.display_scores else _class
#text_str = '%s: %s' % (_class, obj_info[j][2]) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale,
font_thickness)[0]
text_pt = (obj_info[j][4][2], obj_info[j][4][4] - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy,
(obj_info[j][4][2], obj_info[j][4][4]),
(obj_info[j][4][2] + text_w,
obj_info[j][4][4] - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
if pub_Flag == True:
#print(pub_array_msg)
array_pub.publish(pub_array_msg)
pub_Flag = False
old_obj_info = obj_info
return img_numpy
def prep_display(net,
dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
preds = net.detect(
{
'loc': dets_out[0],
'conf': dets_out[1],
'mask': dets_out[2],
'priors': dets_out[3],
'proto': dets_out[4]
}, net)
t = postprocess(preds,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
img_tmp = torch.zeros(img_gpu.shape)
for i in range(num_dets_to_consider):
cfg.dataset.class_names[classes[i]]
mask = masks[i]
classy = cfg.dataset.class_names[classes[i]]
if args.classes == None or classy in args.classes:
img_tmp[mask == 1] = img_gpu[mask == 1]
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_tmp * 255).byte().cpu().numpy()
if num_dets_to_consider == 0:
return img_numpy
return img_numpy
def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,
crop_masks = args.crop,
score_threshold = args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
for index, val in enumerate(zip(classes, scores, boxes)):
print(classes[index], boxes[index], scores[index],'index', index)
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:1, :, :, None]
img_gpu = (masks.sum(dim=0) >= 1).float().expand(-1, -1, 3).contiguous()
else:
img_gpu *= 0
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]
img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
#cv2.imwrite('images/output/', args.images + '.jpg', img_numpy)
print(args.image, args.images)
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
#idx = t[1].argsort(0, descending=True)[:args.top_k]
idx1 = t[1].argsort()
idx = idx1.argsort()
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
mask_picture = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
obj_info, obj_num = data_save(mask_picture, classes, scores, boxes)
#print(classes)
#print('---------')
#np.save('masks.npy', masks.cpu().numpy())
#print(obj_info[0][4][0], obj_info[0][4][1])
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (obj_info[j][0] * 5 if class_color else j *
5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
#img_gpu = img_gpu * (masks.sum(dim=0) > 0.5).float() #only show mask
#img_gpu = img_gpu * masks[0]
#mike0225
mask_img = img_gpu * (masks.sum(dim=0) > 0.5).float() #0209
global mask_numpy
mask_numpy = (mask_img * 255).byte().cpu().numpy() #0209
mask_numpy = cv2.cvtColor(mask_numpy, cv2.COLOR_BGR2GRAY)
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
#mask_img[0:text_h+8, 0:text_w+8] *= 0.6 #0209
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
global frame_count, state_pre, flag, predict_pos, centerX, centerY, preX, preY, degree
frame_count += 1
for j in range(obj_num):
#mask_info = obj_info[j][5]
global mask_numpy1, img_num, temp_x, temp_y
mask_image = mask_picture[j:j + 1, :, :, None]
mask_image = img_gpu * (mask_image.sum(dim=0) > 0.5).float() #0209
mask_numpy1 = (mask_image * 255).byte().cpu().numpy() #0209
mask_numpy1 = cv2.cvtColor(mask_numpy1, cv2.COLOR_BGR2GRAY)
if obj_info[j][2] == 1:
'''
if frame_count%10 == 3:
centerX.append(obj_info[j][4][0])
centerY.append(obj_info[j][4][1])
predict_pos[j][0].append(obj_info[j][4][0])
predict_pos[j][1].append(obj_info[j][4][1])
if predict_pos[j][0][0] == 0:
predict_pos[j][0].pop(0)
if predict_pos[j][1][0] == 0:
predict_pos[j][1].pop(0)
if len(predict_pos[j][0]) > 2:
#predict_pos[j][2] = predict_next( predict_pos[j][0], predict_pos[j][1])
degree, predict_pos[j][2] = predict1_next( mask_numpy1, predict_pos[j][0], predict_pos[j][1]) # test0227
temp_x,temp_y=trans_degree(predict_pos[j][2][0,4,0],predict_pos[j][2][0,4,1],degree)
predict_pos[j][0].pop(0) #0->1
predict_pos[j][1].pop(0)
if state_pre == True:
if predict_pos[j][2] != []:
for i in range(5):
if (predict_pos[j][2][0,i,0]) > 640 or (predict_pos[j][2][0,i,1]) > 480:
pass
else:
pass
#cv2.circle(img_numpy,(predict_pos[j][2][0,i,0],predict_pos[j][2][0,i,1]),5,(0,0,213),-1)
cv2.line(img_numpy,(int(obj_info[j][4][0]+temp_x),int(obj_info[j][4][1]+temp_y)),(int(obj_info[j][4][0]-temp_x),int(obj_info[j][4][1]-temp_y)),(0,0,255),3)
if flag ==False:
for i in range(5):
preX.append(predict_pos[j][2][0,i,0])
preY.append(predict_pos[j][2][0,i,1])
#preY.append(num)
else:
preX.append(predict_pos[j][2][0,4,0])
preY.append(predict_pos[j][2][0,4,1])
#preY.append(num)
flag = True
'''
color = get_color(obj_info[j][0])
score = obj_info[j][3]
if args.display_bboxes:
cv2.rectangle(img_numpy,
(obj_info[j][4][2], obj_info[j][4][4]),
(obj_info[j][4][3], obj_info[j][4][5]),
color, 1)
if args.display_text:
_class = obj_info[j][1]
#text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
text_str = '%s: %s' % (obj_info[j][0], _class
) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale,
font_thickness)[0]
text_pt = (obj_info[j][4][2], obj_info[j][4][4] - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy,
(obj_info[j][4][2], obj_info[j][4][4]),
(obj_info[j][4][2] + text_w,
obj_info[j][4][4] - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
else:
for i in range(2):
predict_pos[j][i] = [0]
predict_pos[j][2] = []
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
# print("height:", h, "width:", w)
with timer.env('Postprocess'):
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
torch.cuda.synchronize()
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:args.top_k]
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
from skimage.feature import hog
def bin_spatial(img, color_space='RGB', size=(32, 32)):
# Convert image to new color space (if specified)
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else:
feature_image = np.copy(img)
# Use cv2.resize().ravel() to create the feature vector
# small_img = cv2.resize(feature_image, (32, 32))
features = feature_image.ravel() # Remove this line!
# Return the feature vector
return features
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
crop_tray_img = img_numpy.copy()
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if classes[j] == 6: #_class == "tray":
crop_tray_img = crop_tray_img[y1:y2, x1:x2]
# process tray cropped image using regression model to predict hidden patties
height, width = crop_tray_img.shape[0], crop_tray_img.shape[1]
aspect_ratio = height / width
height = int(aspect_ratio * 256)
# print(type(crop_tray_img))
crop_tray_img = np.array(crop_tray_img, dtype='uint8')
crop_tray_img = Image.fromarray(crop_tray_img).resize(
(256, height), Image.BICUBIC)
crop_tray_img = np.asarray(crop_tray_img, dtype=float)
# print("Crop_tray:",crop_tray_img)
# print("Crop_tray shape:",crop_tray_img.shape)
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (
_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale, font_thickness)[0]
text_color = [255, 255, 255]
text_pt = (x1, y1 - 3)
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
# counting not visible patties
n_patties = np.sum(classes != 6)
chicken_count = np.sum(classes == 0) + np.sum(classes == 1)
ham_quarter_count = np.sum(classes == 2) + np.sum(classes == 3)
ham_1by10_count = np.sum(classes == 4) + np.sum(classes == 5)
# find the maximum of the three by adding count to a list and choose model accordingly
labels = [chicken_count, ham_quarter_count, ham_1by10_count]
max_index = labels.index(max(labels))
if max_index == 0:
model_path = "weights/regressor/cp_regressor_crop"
elif max_index == 1:
model_path = "weights/regressor/qp_regressor_crop"
else:
model_path = "weights/regressor/op_regressor_crop"
# load the pickle model in memory, scale the input and feed it into the model
import pickle
from sklearn.preprocessing import StandardScaler
loaded_model = pickle.load(open(model_path, 'rb'))
n_bin = 32
originalFeatures = bin_spatial(crop_tray_img)
originalFeatures, _ = np.histogram(originalFeatures,
n_bin,
density=True)
originalFeatures = np.array([originalFeatures])
sc_X = StandardScaler()
originalFeatures = sc_X.fit_transform(
originalFeatures.reshape(n_bin, 1)).reshape(1, n_bin)
# print("originalFeatures:",originalFeatures)
prediction = round(loaded_model.predict(originalFeatures)[0])
# print("model_path:",model_path)
if max_index == 0 and chicken_count < 4:
prediction = chicken_count
count_text = "Calculated: {} Visible Patties: {} -> Chicken: {}, Ham Quarter: {}, HAM 1by10: {}".format(
prediction, n_patties, chicken_count, ham_quarter_count,
ham_1by10_count)
count_text_w, count_text_h = cv2.getTextSize(count_text, font_face,
font_scale,
font_thickness)[0]
text_pt = (x1, y1 - 3)
count_text_pt = (w - count_text_w, h - count_text_h)
cv2.putText(img_numpy, count_text, count_text_pt, font_face,
font_scale, text_color, font_thickness, cv2.LINE_AA)
# print(count_text)
return img_numpy
def prep_display_for_img(dets_out,
img,
h=None,
w=None,
undo_transform=True,
class_color=False,
mask_alpha=0.45):
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
masks = t[3][idx]
classes, scores, boxes = [x[idx] for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] if class_color else j) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
masks = masks[:num_dets_to_consider, :, :, None]
colors = torch.cat([
get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
inv_alph_masks = masks * (-mask_alpha) + 1
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if num_dets_to_consider == 0:
return img_numpy
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
if args.display_scores:
text_str_class = f"{_class}"
text_str_score = f": {score:.2f}"
text_w_class, text_h_class = cv2.getTextSize(
text_str_class, font_face, font_scale,
font_thickness)[0]
img_numpy = ps.putBText(img_numpy,
text_str_class,
text_offset_x=x1,
text_offset_y=y1,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
img_numpy = ps.putBText(img_numpy,
text_str_score,
text_offset_x=x1,
text_offset_y=y1 + text_h_class +
2,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
else:
text_str_class = '%s' % _class
img_numpy = ps.putBText(img_numpy,
text_str_class,
text_offset_x=x1,
text_offset_y=y1,
vspace=0,
hspace=0,
font=font_face,
font_scale=0.6,
thickness=font_thickness,
alpha=0.7,
background_RGB=color,
text_RGB=(255, 255, 255))
return img_numpy
def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,
crop_masks = args.crop,
score_threshold = args.score_threshold)
torch.cuda.synchronize()
all_pred = []
#print(len(t))
#print(type(t))
#print("classes")
#print(t[0].cpu().numpy())
#print(len(t[0].cpu().numpy()))
#print(t[1].cpu().numpy())
# bbox print(t[2].cpu().numpy())
#print(t[3].cpu().numpy())
# classes, scores, boxes, masks
categories = t[0].cpu().numpy()
scores = t[1].cpu().numpy()
masks = t[3].cpu().numpy()
#print(masks.shape)
n_instances = len(scores) #if len(categories) > 0: # If any objects are detected in this image
for i in range(n_instances): # Loop all instances
# save information of the instance in a dictionary then append on all_pred list
pred = {}
#pred['image_id'] = imgid # this imgid must be same as the key of test.json
pred['category_id'] = int(categories[i]) + 1
pred['segmentation'] = binary_mask_to_rle(masks[i,:,:]) # save binary mask to RLE, e.g. 512x512 -> rle
pred['score'] = float(scores[i])
all_pred.append(pred)
with timer.env('Copy'):
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][:args.top_k]
classes, scores, boxes = [x[:args.top_k].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]
img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy, all_pred
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
return img_numpy, all_pred
def prep_display(self, dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out, w, h, visualize_lincomb = self.args.display_lincomb,
crop_masks = self.args.crop,
score_threshold = self.args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:self.args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
self.classes, self.scores, self.boxes = classes, scores, boxes
num_dets_to_consider = min(self.args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < self.args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in self.color_cache[on_gpu]:
return self.color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
self.color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if self.args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# remove overlapped area of mask results
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
overlapped_list = []
box_size = int((x2-x1)*(y2-y1))
color = get_color(j)
score = scores[j]
for k in reversed(range(num_dets_to_consider)):
if (k != j):
a1, b1, a2, b2 = boxes[k, :]
box_size_sub = int((a2-a1)*(b2-b1))
if ((min(a2, x2) - max(a1, x1) > 0) and (min(b2, y2) - max(b1, y1) > 0)):
# overlapped area
S_jk = (min(a2, x2) - max(a1, x1)) * (min(b2, y2) - max(b1, y1))
if (S_jk / box_size > 0.9):
# included other BBox
pass
elif (S_jk / box_size_sub > 0.3):
# Subtract overlapped area in current bounding box
# Find overlapped Bbox position
x_list = [x1, x2, a1, a2]
y_list = [y1, y2, b1, b2]
x_list.sort()
y_list.sort()
overlapped_list.append([int(x_list[1]), int(y_list[1]), int(x_list[2]), int(y_list[2])])
for ov_bbox in overlapped_list:
masks[j][ov_bbox[1]: ov_bbox[3], ov_bbox[0]: ov_bbox[2]] = 0
self.masks = masks
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
self.masks_color = colors
self.masks_color_2 = masks_color
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
self.img_gpu = img_gpu
if self.args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]
img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if self.args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
self.num_dets_to_consider = num_dets_to_consider
if num_dets_to_consider == 0:
return img_numpy
if self.args.display_text or self.args.display_bboxes:
self.text_str = {}
draw_masks = self.masks.squeeze(-1).to(torch.device("cpu")).detach().numpy().astype(np.float32)
update_masks = self.masks.clone()
overlapped_list = []
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
box_size = int((x2-x1)*(y2-y1))
color = get_color(j)
score = scores[j]
if self.args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if self.args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s:%d_%.2f' % (_class, classes[j], score) if self.args.display_scores else _class
self.text_str[j] = text_str
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
text_pt = (x1, y1 + 15)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 + text_h + 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
return img_numpy
def prep_display_single(dets_out,
img,
pad_h,
pad_w,
img_ids=None,
img_meta=None,
undo_transform=True,
mask_alpha=0.45,
fps_str='',
display_mode=None):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
-- display_model: 'train', 'test', 'None' means groundtruth results
"""
if undo_transform:
img_numpy = undo_image_transformation(img, img_meta, pad_h, pad_w)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
pad_h, pad_w, _ = img.shape
with timer.env('Postprocess'):
cfg.mask_proto_debug = args.mask_proto_debug
cfg.preserve_aspect_ratio = False
dets_out = postprocess_ytbvis(dets_out,
pad_h,
pad_w,
img_meta,
display_mask=True,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=cfg.eval_conf_thresh,
img_ids=img_ids,
mask_det_file=args.mask_det_file)
torch.cuda.synchronize()
scores = dets_out['score'][:args.top_k].detach().cpu().numpy()
boxes = dets_out['box'][:args.top_k].detach().cpu().numpy()
if 'segm' in dets_out:
masks = dets_out['segm'][:args.top_k]
args.display_masks = True
else:
args.display_masks = False
classes = dets_out['class'][:args.top_k].detach().cpu().numpy()
num_dets_to_consider = min(args.top_k, classes.shape[0])
color_type = dets_out['box_ids']
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
if num_dets_to_consider == 0:
# No detections found so just output the original image
return (img_gpu * 255).byte().cpu().numpy()
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([
get_color(j,
color_type,
on_gpu=img_gpu.device.index,
undo_transform=undo_transform).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j, color_type)
# plot priors
h, w, _ = img_meta['img_shape']
priors = dets_out['priors'].detach().cpu().numpy()
if j < dets_out['priors'].size(0):
cpx, cpy, pw, ph = priors[j, :] * [w, h, w, h]
px1, py1 = cpx - pw / 2.0, cpy - ph / 2.0
px2, py2 = cpx + pw / 2.0, cpy + ph / 2.0
px1, py1, px2, py2 = int(px1), int(py1), int(px2), int(py2)
pcolor = [255, 0, 255]
# plot the range of features for classification and regression
pred_scales = [24, 48, 96, 192, 384]
x = torch.clamp(torch.tensor([x1, x2]), min=2, max=638).tolist(),
y = torch.clamp(torch.tensor([y1, y2]), min=2, max=358).tolist(),
x, y = x[0], y[0]
if display_mode is not None:
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x[0], y[0]), (x[1], y[1]), color, 1)
if j < dets_out['priors'].size(0):
cv2.rectangle(img_numpy, (px1, py1), (px2, py2),
pcolor,
2,
lineType=8)
# cv2.rectangle(img_numpy, (x[4], y[4]), (x[5], y[5]), fcolor, 2)
if args.display_text:
if classes[j] - 1 < 0:
_class = 'None'
else:
_class = cfg.classes[classes[j] - 1]
if display_mode == 'test':
# if cfg.use_maskiou and not cfg.rescore_bbox:
train_centerness = False
if train_centerness:
rescore = dets_out['DIoU_score'][j] * score
text_str = '%s: %.2f: %.2f: %s' % (_class, score, rescore, str(color_type[j].cpu().numpy())) \
if args.display_scores else _class
else:
text_str = '%s: %.2f: %s' % (
_class, score, str(color_type[j].cpu().numpy())
) if args.display_scores else _class
else:
text_str = '%s' % _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.5
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
return img_numpy
def prep_display(dets_out,
img,
h,
w,
undo_transform=True,
class_color=False,
mask_alpha=0.45,
fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = jt.array(img_numpy)
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out,
w,
h,
visualize_lincomb=args.display_lincomb,
crop_masks=args.crop,
score_threshold=args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx, _ = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
color = COLORS[color_idx]
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = jt.array(list(color)).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pyjt tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider].unsqueeze(3)
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = jt.contrib.concat([
get_color(j, 0).view(1, 1, 1, 3)
for j in range(num_dets_to_consider)
],
dim=0)
#print(masks.repeat(1,1,1,3).shape,colors.shape,mask_alpha)
masks_color = masks.repeat(1, 1, 1, 3) * colors.repeat(
1, masks.shape[1], masks.shape[2], 1) * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider -
1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale,
font_thickness)[0]
img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).uint8().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale,
text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (
_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(text_str, font_face,
font_scale, font_thickness)[0]
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1),
(x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face,
font_scale, text_color, font_thickness,
cv2.LINE_AA)
return img_numpy
def prep_display(dets_out, img, h, w, undo_transform=True, class_color=True, mask_alpha=0.45, fps_str=''):
"""
Note: If undo_transform=False then im_h and im_w are allowed to be None.
"""
if undo_transform:
img_numpy = undo_image_transformation(img, w, h)
img_gpu = torch.Tensor(img_numpy).cuda()
else:
img_gpu = img / 255.0
h, w, _ = img.shape
with timer.env('Postprocess'):
save = cfg.rescore_bbox
cfg.rescore_bbox = True
t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,
crop_masks = args.crop,
score_threshold = args.score_threshold)
cfg.rescore_bbox = save
with timer.env('Copy'):
idx = t[1].argsort(0, descending=True)[:args.top_k]
if cfg.eval_mask_branch:
# Masks are drawn on the GPU, so don't copy
masks = t[3][idx]
classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
if scores[j] < args.score_threshold:
num_dets_to_consider = j
break
#-----------------------1128n 22:49d---------------------------#
# if cfg.dataset.class_names[classes[j]] == 'tree':
# continue
# #num_dets_to_consider =j
# break
#--------------------------------------------------------------#
# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
global color_cache
color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
if on_gpu is not None and color_idx in color_cache[on_gpu]:
return color_cache[on_gpu][color_idx]
else:
#color = COLORS[color_idx]
color = (100, 149, 237) #rgb light blue for line
# color = (124, 252, 0) #rgb light green for tree
if not undo_transform:
# The image might come in as RGB or BRG, depending
color = (color[2], color[1], color[0])
if on_gpu is not None:
color = torch.Tensor(color).to(on_gpu).float() / 255.
color_cache[on_gpu][color_idx] = color
return color
# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
# After this, mask is of size [num_dets, h, w, 1]
masks = masks[:num_dets_to_consider, :, :, None]
##########################################################################
"""
nzCount=-1
for i in range(num_dets_to_consider):
temp_class_check = cfg.dataset.class_names[classes[i]]
if temp_class_check == 'line':
msk = masks[i,:,:,None]
mask=msk.view(1,masks.shape[1], masks.shape[2], masks.shape[3])
img_gpu=(mask.sum(dim=0)>=1).float().expand(-1,-1,3).contiguous()
img_numpy_aux=(img_gpu * 255).byte().cpu().numpy()
img_numpy_aux = cv2.cvtColor(img_numpy_aux, cv2.COLOR_BGR2GRAY)
if nzCount == -1:
nzCount=0
img_numpy=img_numpy_aux
else:
if cv2.countNonZero(img_numpy_aux) > cv2.countNonZero(img_numpy):
img_numpy=img_numpy_aux
img_gpu=(masks.sum(dim=0)>=1).float().expand(-1,-1,3).contiguous()
else:
img_gpu *- 0
"""
##########################################################################
# Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])
colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)
masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha
# This is 1 everywhere except for 1-mask_alpha where the mask is
inv_alph_masks = masks * (-mask_alpha) + 1
# I did the math for this on pen and paper. This whole block should be equivalent to:
# for j in range(num_dets_to_consider):
# img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]
masks_color_summand = masks_color[0]
if num_dets_to_consider > 1:
inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)
masks_color_cumul = masks_color[1:] * inv_alph_cumul
masks_color_summand += masks_color_cumul.sum(dim=0)
img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand
##########################################################################
if args.display_fps:
# Draw the box for the fps on the GPU
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 0.6
font_thickness = 1
text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]
img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha
# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()
if args.display_fps:
# Draw the text on the CPU
text_pt = (4, text_h + 2)
text_color = [255, 255, 255]
cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
if num_dets_to_consider == 0:
return img_numpy
if args.display_text or args.display_bboxes:
for j in reversed(range(num_dets_to_consider)):
x1, y1, x2, y2 = boxes[j, :]
color = get_color(j)
score = scores[j]
if args.display_bboxes:
cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 3) ###changed: 1->3
if args.display_text:
_class = cfg.dataset.class_names[classes[j]]
text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
font_face = cv2.FONT_HERSHEY_DUPLEX
font_scale = 1 ###changed: 0.6 -> 3
font_thickness = 1 ###changed: 1 -> 3
text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
# if _class == 'line':
# text_pt = (x1, y1 + 6)
# else:
# continue
# #text_pt = (x1, y1 + 6)
text_pt = (x1, y1 - 3)
text_color = [255, 255, 255]
cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
return img_numpy
