def find_slide():
svs_file = request.args.get('svs_file', default="None", type=str)
annotation_project = request.args.get('project', default="None", type=str)
result = manifest_controller.get_project_by_similar_svs_file(svs_file)
if 0 == len(result):
return "未查询到相关切片。"
# elif 1 == len(result):
# return redirect("/slide?slide_id=" + str(result[0][0]))
else:
result_string = ""
count = 0
for item in result:
count += 1
result_string += "<p><a href='" + "/slide?slide_id=" + str(
item[0]) + "'>【" + str(count) + "】</a> "
result_string += str(item)
result_string += " <a target='_blank' href='" + "/freehand_annotation?slide_id=" \
+ str(item[0]) + "&project=" + annotation_project + "'>【区域标注】</a> "
result_string += " <a target='_blank' href='" + "/nuclei_annotation?slide_id=" \
+ str(item[0]) + "&project=" + annotation_project + "'>【细胞核标注】</a> "
if os.path.exists("static/data/analysis_data/" + str(item[1]) +
'/'):
for mask in sorted(
os.listdir("static/data/analysis_data/" +
str(item[1]) + '/')):
result_string += "<p style='text-indent:3em;'> <a target='_blank' href='" + "/slide?slide_id=" \
+ str(item[0]) + "&mask_url=" + mask + "'>" + str(mask) + "</a> </p>"
result_string += " </p>"
return result_string
def save_data(old_files_name, old_mask_region, predict_file_name):
if old_files_name != "":
slide_inform = manifest_controller.get_project_by_similar_svs_file(old_files_name)[0]
analysis_data_folder = Analysis_data_root + slide_inform[1] + '/'
if not os.path.exists(analysis_data_folder):
os.mkdir(analysis_data_folder)
sub = ["Cancer", "Health", ]
result_sub = [0, 0]
for i in range(2):
result_sub[i] = numpy.sum(old_mask_region[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary_region = ""
for i in range(2):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary_region = summary_region + sub[i] + ":" + str(result_sub[i]) + "%_"
summary_region = summary_region[:-1]
cv2.imwrite(analysis_data_folder + predict_file_name + '_' + summary_region + ".png", old_mask_region)
old_mask_region[:, :, 1:] = 0
old_mask_region = cv2.blur(old_mask_region, (20, 20))
old_mask_region = cv2.applyColorMap(old_mask_region[:, :, 0], cv2.COLORMAP_JET)
cv2.imwrite(analysis_data_folder + predict_file_name + '_' + summary_region + "mask_region.png",
old_mask_region)
for index_temp in range(len(Patient_data[Patient_ID_column])):
if old_files_name.find(Patient_data[Patient_ID_column][index_temp]) != -1:
Patient_data["result_" + predict_file_name][index_temp] += summary_region + "; "
Patient_data.to_csv("test_predict_region_only.csv")
print(old_files_name)
def save_data(old_files_name, old_mask_region, old_mask_type, predict_file_name):
if old_files_name != "":
slide_inform = manifest_controller.get_project_by_similar_svs_file(old_files_name)[0]
analysis_data_folder = Analysis_data_root + slide_inform[1] + '/'
if not os.path.exists("test_Data"):
os.mkdir("test_Data")
if not os.path.exists(analysis_data_folder):
os.mkdir(analysis_data_folder)
sub = ["Cancer", "Health", ]
result_sub = [0, 0]
for i in range(2):
result_sub[i] = numpy.sum(old_mask_region[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary_region = ""
for i in range(2):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary_region = summary_region + sub[i] + ":" + str(result_sub[i]) + "%_"
summary_region = summary_region[:-1]
cv2.imwrite(analysis_data_folder + predict_file_name + '_' + summary_region + ".png", old_mask_region)
# alpha = old_mask_region[:, :, 3].copy()
old_mask_region[:, :, 1:] = 0
old_mask_region = cv2.blur(old_mask_region, (20, 20))
old_mask_region = cv2.applyColorMap(old_mask_region[:, :, 0], cv2.COLORMAP_JET)
alpha = numpy.zeros((old_mask_region.shape[0], old_mask_region.shape[1]), dtype=numpy.uint8) + 255
alpha[old_mask_region[:, :, 0] != 0] = 0
old_mask_region[:, :, 0] = 0
b_channel, g_channel, r_channel = cv2.split(old_mask_region)
print(b_channel.shape, g_channel.shape, alpha.shape)
old_mask_region = cv2.merge((b_channel, g_channel, r_channel, alpha))
cv2.imwrite(analysis_data_folder + predict_file_name + '_' + summary_region + '_' + "mask_region.png",
old_mask_region)
sub = ["ccRCC", "pRCC", "chRCC"]
result_sub = [0, 0, 0]
for i in range(3):
result_sub[i] = numpy.sum(old_mask_type[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary_type = ""
for i in range(3):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary_type = summary_type + sub[i] + ":" + str(result_sub[i]) + "%_"
summary_type = summary_type[:-1]
cv2.imwrite(analysis_data_folder + predict_file_name + "_" + summary_type + ".png", old_mask_type)
for index_temp in range(len(Patient_data[Patient_ID_column])):
if old_files_name.find(Patient_data[Patient_ID_column][index_temp]) != -1:
Patient_data["result_" + predict_file_name][index_temp] += sub[numpy.argmax(result_sub)] + " " \
+ summary_region + " " + summary_type + "; "
Patient_data.to_csv("test_predict_wjl.csv")
print(old_files_name)
def save_data(old_files_name, old_mask_region, predict_file_name):
if old_files_name != "":
slide_inform = manifest_controller.get_project_by_similar_svs_file(
old_files_name)[0]
analysis_data_folder = Analysis_data_root + slide_inform[1] + '/'
if not os.path.exists("test_Data"):
os.mkdir("test_Data")
if not os.path.exists(analysis_data_folder):
os.mkdir(analysis_data_folder)
result_sub = numpy.sum(old_mask_region[:, :, 1] != 0)
cv2.imwrite(
analysis_data_folder + predict_file_name + '_' + str(result_sub) +
".png", old_mask_region)
for index_temp in range(len(Patient_data[Patient_ID_column])):
if old_files_name.find(
Patient_data[Patient_ID_column][index_temp]) != -1:
Patient_data["result_" + predict_file_name][
index_temp] += "region_number:" + str(result_sub)
Patient_data.to_csv("test_predict_hby.csv")
print(old_files_name)
x_temp, y_temp, size_temp = item.split("/")[-1].split(
".")[0].split("_")
x.append(int(x_temp))
y.append(int(y_temp))
size.append(int(size_temp))
old_files_name = ""
old_mask_region = None
old_mask_type = None
for index in range(len(files)):
# print(index, old_files_name)
if files[index] != old_files_name:
save_data(old_files_name, old_mask_region, predict_file_name)
old_files_name = files[index]
slide_inform = manifest_controller.get_project_by_similar_svs_file(
files[index])[0]
original_data_folder = Original_data_root + slide_inform[
1] + '/'
svs_file = original_data_folder + slide_inform[2]
oslide = openslide.OpenSlide(svs_file)
w, h = oslide.dimensions
old_mask_region = numpy.zeros((int(h / 100), int(w / 100), 4),
dtype=numpy.uint8)
times = int(size[index] / 100)
old_mask_region[int(y[index] / 100):int(y[index] / 100 + times),
int(x[index] / 100):int(x[index] / 100 + times), 1] \