def segment(index, queue):
"""
细胞分割
:param index: gpu编号
:param queue: 待识别含细胞图像
:return:
"""
seg = DarknetPredict(thresh=cfg.algo.thresh,
hier_thresh=cfg.algo.hier_thresh,
nms=cfg.algo.nms,
gpu=str(index))
print('No.%s Cell Engine Staring...' % index)
return seg.predict(queue)
def run(self):
print("Initial DARKNET and XCEPTION model ...")
total = len(self.tiff_lst)
for index, tiff in enumerate(self.tiff_lst):
# 获取大图文件名，不带后缀
tiff_basename, _ = os.path.splitext(os.path.basename(tiff))
tiff_basename = tiff_basename.replace(" ", "-")
print('Process %s / %s %s ...' % (index + 1, total, tiff_basename))
# 切片文件存储路径
slice_save_path = os.path.join(self.slice_dir_path, tiff_basename)
t0 = datetime.datetime.now()
# 如果路径下切图文件不存在，执行切图
if not os.path.exists(slice_save_path):
# 执行切图
ImageSlice(tiff, self.slice_dir_path).get_slices()
# 获取切图文件路径
tif_images = FilesScanner(slice_save_path, ['.jpg']).get_files()
t1 = datetime.datetime.now()
print('TIFF SLICE COST: %s' % (t1 - t0))
seg_results = DarknetPredict().predict(tif_images)
t2 = datetime.datetime.now()
print('TIFF DARKNET COST: %s' % (t2 - t1))
# save segment result into csv
xcep_pre = XceptionPreprocess(tiff)
seg_csv = os.path.join(self.meta_files_path,
tiff_basename + "_seg.csv")
xcep_pre.write_csv(seg_results, seg_csv)
# generate numpy array, it is the input of second stage classification algorithm
cell_numpy, cell_index = xcep_pre.gen_np_array_csv(seg_csv=seg_csv)
# run classification
predictions = XceptionPredict().predict(np.asarray(cell_numpy))
t3 = datetime.datetime.now()
print('XCEPTION COST: %s' % (t3 - t2))
# summarize two stages' result and generate a final csv
clas = XceptionPostprocess()
clas_dict = clas.convert_all(predictions=predictions,
cell_index=cell_index)
clas_csv = os.path.join(self.meta_files_path,
tiff_basename + "_clas.csv")
clas.write_csv(clas_dict, clas_csv)
############################### 获取审核图像 ######################################################
# GET VIEW CELL IMAGES
clas.cut_cells_p_marked(tiff,
clas_dict,
self.cells_path,
factor=0.2,
N=2)
t4 = datetime.datetime.now()
print("TIFF %s TOTAL COST %s ..." % (tiff_basename, t4 - t0))
def yolo_predict(gpu_index, images_lst):
"""
Yolo 检测细胞位置
:param gpu_index: gpu id
:param images_lst: image 路径列表
:return: dict: <x_y: [label, accuracy, xmin, ymin, xmax, ymax]>
"""
return DarknetPredict(str(gpu_index)).predict(images_lst)
def run(self):
basename = os.path.splitext(os.path.basename(self.tif_name))[0]
# slice slide
slice_res = ImageSlice(self.tif_name, self.file_path).get_slices()
tif_images = FilesScanner(slice_res['done'][0],
postfix=".jpg").get_files()
# print(tif_images)
# run darknet
seg_results = DarknetPredict().predict(tif_images)
# save segment result into csv
xcep_pre = XceptionPreprocess(self.tif_name)
seg_csv = os.path.join(self.file_path, basename + "_seg.csv")
xcep_pre.write_csv(seg_results, seg_csv)
# generate numpy array, it is the input of second stage classification algorithm
cell_numpy, cell_index = xcep_pre.gen_np_array_csv(seg_csv=seg_csv)
# run classification
predictions = XceptionPredict().predict(cell_numpy)
print(len(cell_index), len(predictions))
# summarize two stages' result and generate a final csv
clas = XceptionPostprocess()
clas_dict = clas.convert_all(predictions=predictions,
cell_index=cell_index)
clas_csv = os.path.join(self.file_path, basename + "_clas.csv")
clas.write_csv(clas_dict, clas_csv)
# # generate cell jpgs, based on clas_dict
# clas.cut_cells(self.tif_name, clas_dict, self.cell_path)
# # add classification det probability to jpg name
# clas.cut_cells_p(self.tif_name, clas_dict, self.cell_path)
# add classification det probability to jpg name and add marked class_i if jpg is already is marked
clas.cut_cells_p_marked(self.tif_name,
clas_dict,
self.cell_path,
factor=0.2,
N=2)
def segment(index):
DarknetPredict(gpu=str(index))