def run_one_scn(demo_scn, demo_dir, opt):
Detector = detector_factory[opt.task]
detector = Detector(opt)
basename = os.path.basename(demo_scn)
basename = basename.replace('.scn', '')
basename = basename.replace('.svs', '')
# basename = basename.replace(' ', '-')
working_dir = os.path.join(demo_dir, basename)
xml_file = os.path.join(working_dir, '%s.xml' % (basename))
if os.path.exists(xml_file):
return
patch_2d_dir, simg_big, simg = scn_to_patchs(demo_scn, working_dir, opt)
if os.path.isdir(patch_2d_dir):
image_names = []
ls = os.listdir(patch_2d_dir)
for file_name in sorted(ls):
ext = file_name[file_name.rfind('.') + 1:].lower()
if ext in image_ext:
image_names.append(os.path.join(patch_2d_dir, file_name))
else:
image_names = [patch_2d_dir]
detect_all = None
count = 1
for (image_name) in image_names:
ret = detector.run(image_name)
results = ret['results']
res_strs = os.path.basename(image_name).replace('.png',
'').split('-x-')
lv_str = res_strs[0]
patch_start_x = np.int(res_strs[3])
patch_start_y = np.int(res_strs[4])
if opt.filter_boarder:
output_h = opt.input_h # hard coded
output_w = opt.input_w # hard coded
for j in range(1, opt.num_classes + 1):
for i in range(len(results[j])):
cp = [0, 0]
cp[0] = results[j][i][0]
cp[1] = results[j][i][1]
cr = results[j][i][2]
if cp[0] - cr < 0 or cp[0] + cr > output_w:
results[j][i][3] = 0
continue
if cp[1] - cr < 0 or cp[1] + cr > output_h:
results[j][i][3] = 0
continue
for j in range(1, opt.num_classes + 1):
for circle in results[j]:
if circle[3] > opt.vis_thresh:
circle_out = circle[:]
circle_out[0] = circle[0] + patch_start_x
circle_out[1] = circle[1] + patch_start_y
if detect_all is None:
detect_all = [circle]
else:
detect_all = np.append(detect_all, [circle], axis=0)
time_str = ''
for stat in time_stats:
time_str = time_str + '{} {:.3f}s |'.format(stat, ret[stat])
print(' %d/%d %s' % (count, len(image_names), time_str))
count = count + 1
num_classes = 1
scales = 1
max_per_image = 2000
run_nms = True
results2 = merge_outputs(num_classes, max_per_image, run_nms, detect_all)
detect_all = results2[1]
# detections = []
# det_clss = {}
# det_clss[1] = detect_all
# detections.append(det_clss)
# detect_all = merge_outputs(opt, detections)
if not simg_big is None:
debugger = Debugger(dataset=opt.dataset,
ipynb=(opt.debug == 3),
theme=opt.debugger_theme)
debugger.add_img(simg_big, img_id='')
debugger.save_all_imgs(working_dir,
prefix='%s' % (basename)) # save original image
json_file = os.path.join(working_dir, '%s.json' % (basename))
debugger.save_detect_all_to_json(simg_big, detect_all, json_file, opt,
simg)
for circle in detect_all:
debugger.add_coco_circle(circle[:3],
circle[-1],
circle[3],
img_id='')
debugger.save_all_imgs(working_dir, prefix='%s_overlay' %
(basename)) # save original overlay
# # make open slide file
# with open("/media/huoy1/48EAE4F7EAE4E264/Projects/detection/test_demo/Case 01-3_manual_good.xml") as fd:
# doc = xmltodict.parse(fd.read())
try:
start_x = np.int(simg.properties['openslide.bounds-x'])
start_y = np.int(simg.properties['openslide.bounds-y'])
width_x = np.int(simg.properties['openslide.bounds-width'])
height_y = np.int(simg.properties['openslide.bounds-height'])
except:
start_x = 0
start_y = 0
width_x = np.int(simg.properties['aperio.OriginalWidth'])
height_y = np.int(simg.properties['aperio.OriginalHeight'])
down_rate = simg.level_downsamples[opt.lv]
detect_json = []
doc_out = {}
doc_out['Annotations'] = {}
doc_out['Annotations']['@MicronsPerPixel'] = simg.properties[
'openslide.mpp-x']
doc_out['Annotations']['@Level'] = opt.lv
doc_out['Annotations']['@DownRate'] = down_rate
doc_out['Annotations']['@start_x'] = start_x
doc_out['Annotations']['@start_y'] = start_y
doc_out['Annotations']['@width_x'] = width_x
doc_out['Annotations']['@height_y'] = height_y
if 'leica.device-model' in simg.properties:
doc_out['Annotations']['@Device'] = 'leica.device-model'
else:
doc_out['Annotations']['@Device'] = 'aperio.Filename'
doc_out['Annotations']['Annotation'] = {}
doc_out['Annotations']['Annotation']['@Id'] = '1'
doc_out['Annotations']['Annotation']['@Name'] = ''
doc_out['Annotations']['Annotation']['@ReadOnly'] = '0'
doc_out['Annotations']['Annotation']['@LineColorReadOnly'] = '0'
doc_out['Annotations']['Annotation']['@Incremental'] = '0'
doc_out['Annotations']['Annotation']['@Type'] = '4'
doc_out['Annotations']['Annotation']['@LineColor'] = '65280'
doc_out['Annotations']['Annotation']['@Visible'] = '1'
doc_out['Annotations']['Annotation']['@Selected'] = '1'
doc_out['Annotations']['Annotation']['@MarkupImagePath'] = ''
doc_out['Annotations']['Annotation']['@MacroName'] = ''
doc_out['Annotations']['Annotation']['Attributes'] = {}
doc_out['Annotations']['Annotation']['Attributes']['Attribute'] = {}
doc_out['Annotations']['Annotation']['Attributes']['Attribute'][
'@Name'] = 'glomerulus'
doc_out['Annotations']['Annotation']['Attributes']['Attribute'][
'@Id'] = '0'
doc_out['Annotations']['Annotation']['Attributes']['Attribute'][
'@Value'] = ''
doc_out['Annotations']['Annotation']['Plots'] = None
doc_out['Annotations']['Annotation']['Regions'] = {}
doc_out['Annotations']['Annotation']['Regions'][
'RegionAttributeHeaders'] = {}
doc_out['Annotations']['Annotation']['Regions']['AttributeHeader'] = []
doc_out['Annotations']['Annotation']['Regions']['Region'] = []
for di in range(len(detect_all)):
detect_one = detect_all[di]
detect_dict = {}
detect_dict['@Id'] = str(di + 1)
detect_dict['@Type'] = '2'
detect_dict['@Zoom'] = '0.5'
detect_dict['@ImageLocation'] = ''
detect_dict['@ImageFocus'] = '-1'
detect_dict['@Length'] = '2909.1'
detect_dict['@Area'] = '673460.1'
detect_dict['@LengthMicrons'] = '727.3'
detect_dict['@AreaMicrons'] = '42091.3'
detect_dict['@Text'] = ('%.3f' % detect_one[3])
detect_dict['@NegativeROA'] = '0'
detect_dict['@InputRegionId'] = '0'
detect_dict['@Analyze'] = '0'
detect_dict['@DisplayId'] = str(di + 1)
detect_dict['Attributes'] = None
detect_dict['Vertices'] = '0'
detect_dict['Vertices'] = {}
detect_dict['Vertices']['Vertex'] = []
if 'leica.device-model' in simg.properties: #leica
coord1 = {}
coord1['@X'] = str(height_y -
(detect_one[1] - detect_one[2]) * down_rate)
coord1['@Y'] = str((detect_one[0] - detect_one[2]) * down_rate)
coord1['@Z'] = '0'
coord2 = {}
coord2['@X'] = str(
height_y - (detect_one[1] + detect_one[2]) * down_rate) # 左右
coord2['@Y'] = str(
(detect_one[0] + detect_one[2]) * down_rate) # 上下
coord2['@Z'] = '0'
detect_dict['Vertices']['Vertex'].append(coord1)
detect_dict['Vertices']['Vertex'].append(coord2)
elif 'aperio.Filename' in simg.properties:
coord1 = {}
coord1['@X'] = str((detect_one[0] - detect_one[2]) * down_rate)
coord1['@Y'] = str((detect_one[1] - detect_one[2]) * down_rate)
coord1['@Z'] = '0'
coord2 = {}
coord2['@X'] = str(
(detect_one[0] + detect_one[2]) * down_rate) # 左右
coord2['@Y'] = str(
(detect_one[1] + detect_one[2]) * down_rate) # 上下
coord2['@Z'] = '0'
detect_dict['Vertices']['Vertex'].append(coord1)
detect_dict['Vertices']['Vertex'].append(coord2)
doc_out['Annotations']['Annotation']['Regions']['Region'].append(
detect_dict)
out = xmltodict.unparse(doc_out, pretty=True)
with open(xml_file, 'wb') as file:
file.write(out.encode('utf-8'))
os.system('rm -r "%s"' % (os.path.join(working_dir, '2d_patch')))
return
def debug(self, batch, output, iter_id):
opt = self.opt
reg = output['reg'] if opt.reg_offset else None
# print(output)
dets = circledet_decode(output['hm'],
output['cl'],
reg=reg,
cat_spec_wh=opt.cat_spec_wh,
K=opt.K)
# print(dets)
if opt.filter_boarder:
output_h = self.opt.default_resolution[
0] // self.opt.down_ratio #hard coded
output_w = self.opt.default_resolution[
1] // self.opt.down_ratio #hard coded
for i in range(dets.shape[1]):
cp = [0, 0]
cp[0] = dets[0, i, 0]
cp[1] = dets[0, i, 1]
cr = dets[0, i, 2]
if cp[0] - cr < 0 or cp[0] + cr > output_w:
dets[0, i, 3] = 0
continue
if cp[1] - cr < 0 or cp[1] + cr > output_h:
dets[0, i, 3] = 0
continue
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets[:, :, :3] *= opt.down_ratio
dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2])
dets_gt[:, :, :3] *= opt.down_ratio
for i in range(1):
debugger = Debugger(dataset=opt.dataset,
ipynb=(opt.debug == 3),
theme=opt.debugger_theme)
img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * opt.std + opt.mean) * 255.), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(
output['hm'][i].detach().cpu().numpy())
gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hm')
debugger.add_blend_img(img, gt, 'gt_hm')
debugger.add_img(img, img_id='out_pred')
for k in range(len(dets[i])):
# print('risk = %f' % dets[i, k, 3])
if dets[i, k, 3] > opt.center_thresh:
debugger.add_coco_circle(dets[i, k, :3],
dets[i, k, -1],
dets[i, k, 3],
img_id='out_pred')
debugger.add_img(img, img_id='out_gt')
for k in range(len(dets_gt[i])):
if dets_gt[i, k, 3] > opt.center_thresh:
debugger.add_coco_circle(dets_gt[i, k, :3],
dets_gt[i, k, -1],
dets_gt[i, k, 3],
img_id='out_gt')
if opt.debug == 4:
debugger.save_all_imgs(opt.debug_dir,
prefix='{}'.format(iter_id))
else:
debugger.show_all_imgs(pause=True)
