def export_manifest(manifest_path):
manifest_db = manifest.Manifest()
manifest_txt = open(manifest_path, 'w')
manifest_txt.write("id\tfilename\tmd5\tsize\tstate\n")
for wsi in manifest_db.get_projects():
manifest_txt.write(wsi[1] + "\t" + wsi[2] +
"\tunknown\tunknown\tunknown\n")
def freehand_annotation_get_info():
slide_id = request.args.get('slide_id', default=1, type=int)
annotator_id = request.args.get('annotator_id', default=1, type=int)
annotation_project = request.args.get('project',
default="None",
type=str)
slide_uuid = request.args.get('slide_uuid', default="", type=str)
mani = manifest.Manifest()
wsi = mani.get_project_by_id(slide_id)
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
dimensions = openslide.OpenSlide(svs_file_path).dimensions
MPP = openslide.OpenSlide(svs_file_path).properties.get("aperio.MPP")
properties = dict(openslide.OpenSlide(svs_file_path).properties)
w = dimensions[0]
h = dimensions[1]
return jsonify(
img_width=w,
img_height=h,
um_per_px=MPP if MPP else 0.25,
max_image_zoom=0, # max_image_zoom,
toggle_status=0, # toggle_status
properties=properties)
def nuclei_annotation_v2_update_image():
slide_id = request.args.get('slide_id', default=1, type=int)
annotator_id = request.args.get('annotator_id', default=1, type=int)
annotation_project = request.args.get('project',
default="None",
type=str)
slide_uuid = request.args.get('slide_uuid', default="", type=str)
v1 = request.args.get('var1', 0, type=int) # Top-left x coordinate
v2 = request.args.get('var2', 0, type=int) # Top-left y coordinate
v3 = request.args.get('var3', 0, type=int) # Bottom-right x
v4 = request.args.get('var4', 0, type=int) # Bottom-right y
v5 = request.args.get('var5', 0, type=int) # Viewer width (pixel)
v6 = request.args.get('var6', 0, type=int) # Viewer height (pixel)
v7 = request.args.get('var7', 0, type=int) # Region ID
region_id = v7
region_inform = {}
region_inform["annotator_id"] = annotator_id
region_inform["annotation_project"] = annotation_project
region_inform["slide_uuid"] = slide_uuid
region_inform["region_id"] = region_id
mani = manifest.Manifest()
wsi = mani.get_project_by_id(slide_id)
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
annotation_root_folder = nuclei_annotation_data_root + annotation_project + '/' + wsi[
1] + '/'
if not os.path.exists(annotation_root_folder):
os.mkdir(annotation_root_folder)
loc_tl_coor = (v1, v2)
loc_viewing_size = (v3 - v1, v4 - v2)
mask_file_name = annotation_root_folder + 'a' + str(annotator_id) + '_r' + \
str(region_id) + '_mask' + '.txt'
original_pic_url = annotation_root_folder + 'r' + str(v7) + '.png'
if not os.path.exists(mask_file_name):
if not os.path.exists(original_pic_url):
oslide = openslide.OpenSlide(svs_file_path)
patch = oslide.read_region((v1, v2), 0,
(v3 - v1 + 1, v4 - v2 + 1))
patch.save(annotation_root_folder + 'r' + str(v7) + '.png')
oslide.close()
mask_file_name = nuclei_annotation_v2_controller.boundary_2_mask(
region_inform)
return jsonify(
background_url=original_pic_url + '?a=' + str(uuid.uuid1()),
mask_url=mask_file_name + '?a=' + str(uuid.uuid1()),
top_left_x=loc_tl_coor[0],
top_left_y=loc_tl_coor[1],
viewing_size_x=loc_viewing_size[0],
viewing_size_y=loc_viewing_size[1],
)
def import_manifest(manifest_path):
manifest_db = manifest.Manifest()
manifest_txt = open(manifest_path).readlines()
for wsi in manifest_txt:
info = wsi.split('\t')
if info[0] == "id":
continue
try:
manifest_db.insert(slide_uuid=info[0], svs_file=info[1])
except Exception as e:
print(e)
thread_controller.BackgroundThread(manifest_controller.get_table,
0).start()
def get_available_slide_id():
mani = manifest.Manifest()
data = []
for wsi in mani.get_projects():
temp = {"id": wsi[0], "text": wsi[0]}
icon_file_path = icon_root + wsi[1] + '/' + 'icon.png'
if not os.path.exists(icon_file_path):
# try: # image_size
# svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
# openslide.OpenSlide(svs_file_path)
# except:
continue
data.append(temp)
return data
def export_database(database_file_folder):
manifest_db = manifest.Manifest()
manifest_csv = open(database_file_folder + '/manifest.txt', 'w')
manifest_csv.write("id\tfilename\tmd5\tsize\tstate\n")
for wsi in manifest_db.get_projects():
for temp in wsi:
manifest_csv.write(str(temp) + '\t')
manifest_csv.write("\n")
mission_db = mission.Mission()
mission_csv = open(database_file_folder + '/predict_mask.txt', 'w')
mission_csv.write("id\tfilename\tmd5\tsize\tstate\n")
for wsi in mission_db.get_predict_masks():
for temp in wsi:
mission_csv.write(str(temp) + '\t')
mission_csv.write("\n")
def get_dzi_path(start_no=0, end_no=0):
mani = manifest.Manifest()
data = []
wsis = mani.get_projects()
if end_no <= 0:
end_no = end_no + len(wsis)
for wsi in wsis[start_no:end_no]:
temp = []
temp.append(wsi[0]) # ID
temp.append(wsi[1]) # UUID
temp.append(wsi[2]) # svs_file
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
temp.append("https://pk.zhanghaichuan.com/dzi_online/" +
svs_file_path + ".dzi")
data.append(temp)
return data
def nuclei_annotation_v2_auto_predict():
slide_id = request.args.get('slide_id', default=1, type=int)
annotator_id = request.args.get('annotator_id', default=1, type=int)
annotation_project = request.args.get('project',
default="None",
type=str)
slide_uuid = request.args.get('slide_uuid', default="", type=str)
v7 = request.args.get('var7', 0, type=int) # Region ID
region_id = v7
region_inform = {}
region_inform["annotator_id"] = annotator_id
region_inform["annotation_project"] = annotation_project
region_inform["slide_uuid"] = slide_uuid
region_inform["region_id"] = region_id
mani = manifest.Manifest()
wsi = mani.get_project_by_id(slide_id)
annotation_root_folder = nuclei_annotation_data_root + annotation_project + '/' + wsi[
1] + '/'
if not os.path.exists(annotation_root_folder):
os.mkdir(annotation_root_folder)
original_pic_url = annotation_root_folder + 'r' + str(v7) + '.png'
print(original_pic_url)
boundary_file_name = annotation_root_folder + 'a' + str(annotator_id) + '_r' + \
str(region_id) + '_boundary' + '.txt'
annotation_file_name = annotation_root_folder + 'a' + str(annotator_id) + '_r' + \
str(region_id) + '_annotation' + '.txt'
command = "/home1/zhc/.conda/envs/hovernet/bin/python3.6 " \
"/home1/zhc/singleInfer/demo.py " \
+ original_pic_url + ' ' + boundary_file_name + ' ' + annotation_file_name
print(command)
os.system(command)
nuclei_annotation_v2_controller.boundary_2_point(region_inform)
nuclei_annotation_v2_controller.boundary_2_mask(region_inform)
return jsonify({})
def make_bg(slide_id):
manifest_db = manifest.Manifest()
info = manifest_db.get_project_by_id(slide_id)
original_data_folder = original_data_root + info[1] + '/'
svs_file = original_data_folder + info[2]
analysis_data_folder = analysis_data_root + info[1] + '/'
if not os.path.exists(analysis_data_folder):
os.mkdir(analysis_data_folder)
if info[4] is None or not os.path.exists(analysis_data_folder + info[4]):
if info[3] is None or not os.path.exists(analysis_data_folder +
info[3]):
generate_smaller_image_from_svs_file(
svs_file, analysis_data_folder + 'smaller_image.png')
manifest_db.update_smaller_image_by_id(info[0],
'smaller_image.png')
info = manifest_db.get_project_by_id(slide_id)
smaller_image = analysis_data_folder + info[3]
generate_background_mask_from_smaller_image(
smaller_image, analysis_data_folder + 'background_mask.png')
manifest_db.update_background_mask_by_id(info[0],
'background_mask.png')
def nuclei_annotation_get_info():
slide_id = request.args.get('slide_id', default=1, type=int)
annotator_id = request.args.get('annotator_id', default=1, type=int)
annotation_project = request.args.get('project',
default="None",
type=str)
slide_uuid = request.args.get('slide_uuid', default="", type=str)
mani = manifest.Manifest()
wsi = mani.get_project_by_id(slide_id)
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
dimensions = openslide.OpenSlide(svs_file_path).dimensions
w = dimensions[0]
h = dimensions[1]
return jsonify(
img_width=w,
img_height=h,
um_per_px=0.25,
max_image_zoom=0, # max_image_zoom,
toggle_status=0 # toggle_status
)
def clear_database():
manifest_db = manifest.Manifest()
manifest_db.delete_all_projects()
mission_db = mission.Mission()
mission_db.delete_all_predict()
def continue_slide_id():
mani = manifest.Manifest()
mani.continue_id()
return
def remove_wsi_by_id(slide_id):
mani = manifest.Manifest()
mani.delete_project_by_id(slide_id)
def get_total_number():
mani = manifest.Manifest()
return len(mani.get_projects())
def get_info_by_uuid(slide_uuid):
mani = manifest.Manifest()
return mani.get_project_by_uuid(slide_uuid)
def add_wsi(uuid, svs_file_name):
mani = manifest.Manifest()
mani.insert(slide_uuid=uuid, svs_file=svs_file_name)
def get_table(start_no=0, end_no=0, make_icon_mask=1):
mani = manifest.Manifest()
data = []
wsis = mani.get_projects()
if end_no <= 0:
end_no = end_no + len(wsis)
print(start_no, end_no)
for wsi in wsis[start_no:end_no]:
print(wsi)
temp = []
temp.append(wsi[0]) # ID
temp.append(
'<button type="button" onclick="btn_click(\'remove_wsi\',' +
str(wsi[0]) + ')">remove</button>')
temp.append(wsi[1]) # UUID
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
try: # image_size
icon_file_path = icon_root + wsi[1] + '/' + 'icon.png'
if os.path.exists(icon_file_path):
dimensions = [-1, -1]
else:
# raise Exception("error")
dimensions = openslide.OpenSlide(svs_file_path).dimensions
except Exception as e:
print(e)
if os.path.exists(svs_file_path):
temp.append('<a "target="_blank">' + wsi[2] + '</a>')
temp.append('Not SVS file')
temp.append('Unsupported file')
data.append(temp)
else:
temp.append('<a "target="_blank">' + wsi[2] + '</a>')
temp.append('No SVS file')
data.append(temp)
continue
temp.append('<a href="/slide?slide_id=' + str(wsi[0]) +
'"target="_blank">' + wsi[2] + '</a>')
icon_file_path = icon_root + wsi[1] + '/' + 'icon.png'
if not os.path.exists(icon_file_path) and make_icon_mask != 0:
try:
if not os.path.exists(icon_root + wsi[1] + '/'):
os.mkdir(icon_root + wsi[1] + '/')
image_processing.generate_icon_image_from_svs_file(
svs_file_path, icon_file_path)
except Exception as e:
print(e)
pass
temp.append('<img src="' + icon_file_path + '"/>')
temp.append(
str(dimensions[0]).rjust(6, '_') + ' * ' +
str(dimensions[1]).rjust(6, '_'))
if wsi[4] is None or not os.path.exists(analysis_data_root + wsi[1] +
'/' + wsi[4]):
temp.append(
'<button type="button" onclick="btn_click(\'make_bg_mask\',' +
str(wsi[0]) + ')"> Make BG Mask</button>')
else:
temp.append(
'<a href="/slide?slide_id=' + str(wsi[0]) + '&mask_url=' +
wsi[4] + '"target="_blank">' + 'Background Mask ' + '</a>'
# + '<button type="button" onclick="btn_click(\'make_bg_mask\','
# + str(wsi[0]) + ')"> Remake</button>'
)
temp.append("model_selection")
temp.append('<a href="/mission_table?slide_uuid=' + str(wsi[1]) +
'"target="_blank">' + 'Mission' + '</a>')
data.append(temp)
return data
def predict_mask_with_job_id(slide_id, model_name="0"):
# if str(model_name) == '0':
# model_path = "Model/resnet_34_crd_model_59.pth"
# elif str(model_name) == '1':
# model_path = "Model/resnet_34_transfer_predicted_crd_model_best.pth"
# else:
model_path = "models/" + model_name
make_bg(slide_id)
manifest_db = manifest.Manifest()
info = manifest_db.get_project_by_id(slide_id)
mission_db = mission.Mission()
job_id = mission_db.insert(slide_uuid=info[1],
slide_id=info[0],
job_type=model_name,
total=-1)
original_data_folder = original_data_root + info[1] + '/'
analysis_data_folder = analysis_data_root + info[1] + '/'
svs_file = original_data_folder + info[2]
# if not os.path.exists(analysis_data_folder + 'patch/'):
# os.mkdir(analysis_data_folder + 'patch/')
mask = cv2.imread(analysis_data_folder + info[4], cv2.IMREAD_GRAYSCALE)
oslide = openslide.OpenSlide(svs_file)
w, h = oslide.dimensions
pre_result = numpy.zeros((mask.shape[0], mask.shape[1], 4),
dtype=numpy.uint8)
times = mask.shape[1] / w * 2000
mission_db.update_total_by_id(job_id=job_id, total=w // 2000 * h // 2000)
if "subtype" not in str(model_name):
myModule = predict_module.ResNetClassification(model_path=model_path,
num_classes=2,
batch_size=64,
num_workers=0)
for x in range(w // 2000):
for y in range(h // 2000):
if available_region(mask[int(y * times):int(y * times + times),
int(x * times):int(x * times +
times)]):
# if os.path.exists(analysis_data_folder + 'patch/' + str(x) + '_' + str(y) + '.png'):
# patch = Image.open(analysis_data_folder + 'patch/' + str(x) + '_' + str(y) + '.png')
# else:
# patch = oslide.read_region((x * 2000, y * 2000), 0, (2000, 2000))
# patch.save(analysis_data_folder + 'patch/' + str(x) + '_' + str(y) + '.png')
patch = oslide.read_region((x * 2000, y * 2000), 0,
(2000, 2000))
patch = numpy.array(patch.convert('RGB'))
result = myModule.predict(
numpy.resize(patch, tuple([1, 2000, 2000, 3])))
# result = myModule.predict(total_image)
# probablity_list.append((x, y, result[0]))
pre_result[int(y * times):int(y * times + times),
int(x * times):int(x * times + times),
numpy.argmax(result[0])] = result[
0, numpy.argmax(result[0])] * 255
mission_db.update_finished_by_id(job_id=job_id,
finished=x * h // 2000 + y +
1)
pre_result = post_processing(pre_result)
mask_file_name = 'mission' + str(job_id) + '_' + str(
model_name) + '.png'
cv2.imwrite(analysis_data_folder + mask_file_name, pre_result)
mission_db.update_predict_mask_by_id(job_id=job_id,
predict_mask=mask_file_name)
mask_file = cv2.imread(analysis_data_folder + mask_file_name)
original_file_name = "smaller_image.png"
original_file = cv2.imread(analysis_data_folder + original_file_name)
alpha = numpy.zeros(mask_file.shape)
alpha[:, :, 0] = mask_file[:, :, 0]
alpha[:, :, 1] = mask_file[:, :, 0]
alpha[:, :, 2] = mask_file[:, :, 0]
alpha = alpha.astype(float) / 128
mask_file = mask_file.astype(float)
original_file = original_file.astype(float)
mask_file = cv2.multiply((1 - alpha) * 0.7, mask_file)
original_file = cv2.multiply(0.3 + alpha * 0.7, original_file)
out_image = original_file + mask_file
result_file_name = 'mission' + str(job_id) + '_result.png'
cv2.imwrite(analysis_data_folder + result_file_name, out_image)
elif "subtype" in str(model_name) and "hybrid" in str(model_name):
myModule = predict_module.ResNetClassification(model_path=model_path,
num_classes=4,
batch_size=64,
num_workers=0)
for x in range(w // 2000):
for y in range(h // 2000):
if available_region(mask[int(y * times):int(y * times + times),
int(x * times):int(x * times +
times)]):
patch = oslide.read_region((x * 2000, y * 2000), 0,
(2000, 2000))
patch = numpy.array(patch.convert('RGB'))
result = myModule.predict(
numpy.resize(patch, tuple([1, 2000, 2000, 3])))
pre_result[int(y * times):int(y * times + times),
int(x * times):int(x * times + times),
numpy.argmax(result[0])] = result[
0, numpy.argmax(result[0])] * 255
mission_db.update_finished_by_id(job_id=job_id,
finished=x * h // 2000 + y +
1)
sub = ["health", "ccRCC", "pRCC", "chRCC"]
result_sub = [0, 0, 0, 0]
for i in range(4):
result_sub[i] = numpy.sum(pre_result[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary = ""
for i in range(4):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary = summary + sub[i] + ": " + str(result_sub[i]) + "%, "
result = numpy.zeros((pre_result.shape[0], pre_result.shape[1], 3))
for i in range(3):
result[:, :, i] = pre_result[:, :, i + 1]
mask_file_name = 'result_mission' + str(job_id) + '_' + str(
model_name) + '.png'
cv2.imwrite(analysis_data_folder + mask_file_name, result)
result = numpy.zeros((pre_result.shape[0], pre_result.shape[1], 3))
i = 1
result[:, :, 0] = pre_result[:, :, i + 1] * [15] / 255
result[:, :, 1] = pre_result[:, :, i + 1] * [147] / 255
result[:, :, 2] = pre_result[:, :, i + 1] * [254] / 255
i = 0
result[:, :, i] += pre_result[:, :, i + 1]
i = 2
result[:, :, i] += pre_result[:, :, i + 1]
mask_file_name = 'mission' + str(job_id) + '_' + str(
model_name) + '.png'
cv2.imwrite(analysis_data_folder + mask_file_name, result)
mission_db.update_predict_mask_by_id(job_id=job_id,
predict_mask=summary)
elif "_subtype" in str(model_name) and "hybrid" not in str(model_name):
myModule = predict_module.ResNetClassification(model_path=model_path,
num_classes=3,
batch_size=64,
num_workers=0)
for x in range(w // 2000):
for y in range(h // 2000):
if available_region(mask[int(y * times):int(y * times + times),
int(x * times):int(x * times +
times)]):
patch = oslide.read_region((x * 2000, y * 2000), 0,
(2000, 2000))
patch = numpy.array(patch.convert('RGB'))
result = myModule.predict(
numpy.resize(patch, tuple([1, 2000, 2000, 3])))
pre_result[int(y * times):int(y * times + times),
int(x * times):int(x * times + times),
numpy.argmax(result[0])] = result[
0, numpy.argmax(result[0])] * 255
mission_db.update_finished_by_id(job_id=job_id,
finished=x * h // 2000 + y +
1)
sub = ["ccRCC", "pRCC", "chRCC"]
result_sub = [0, 0, 0]
for i in range(3):
result_sub[i] = numpy.sum(pre_result[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary = ""
for i in range(3):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary = summary + sub[i] + ": " + str(result_sub[i]) + "%, "
result = numpy.zeros((pre_result.shape[0], pre_result.shape[1], 3))
for i in range(3):
result[:, :, i] = pre_result[:, :, i]
mask_file_name = 'mission' + str(job_id) + '_' + str(
model_name) + '.png'
cv2.imwrite(analysis_data_folder + mask_file_name, result)
mission_db.update_predict_mask_by_id(job_id=job_id,
predict_mask=summary)
elif "MixMatch-subtype" in str(model_name):
myModule = predict_module2.ResNetClassification(model_path=model_path,
batch_size=64,
num_workers=0)
for x in range(w // 2000):
for y in range(h // 2000):
if available_region(mask[int(y * times):int(y * times + times),
int(x * times):int(x * times +
times)]):
patch = oslide.read_region((x * 2000, y * 2000), 0,
(2000, 2000))
patch = numpy.array(patch.convert('RGB'))
result1, result2 = myModule.predict(
numpy.resize(patch, tuple([1, 2000, 2000, 3])))
if result1[0, 1] > 0.5:
pre_result[int(y * times):int(y * times + times),
int(x * times):int(x * times + times),
0] = result1[0, 1] * 255
elif result1[0, 0] > 0.5:
pre_result[int(y * times):int(y * times + times),
int(x * times):int(x * times + times),
numpy.argmax(result2[0]) +
1] = result2[0,
numpy.argmax(result2[0])] * 255
mission_db.update_finished_by_id(job_id=job_id,
finished=x * h // 2000 + y +
1)
sub = ["health", "ccRCC", "pRCC", "chRCC"]
result_sub = [0, 0, 0, 0]
for i in range(4):
result_sub[i] = numpy.sum(pre_result[:, :, i] != 0)
result_sub_sum = numpy.sum(result_sub)
summary = ""
for i in range(4):
result_sub[i] = round(result_sub[i] / result_sub_sum * 100, 2)
summary = summary + sub[i] + ": " + str(result_sub[i]) + "%, "
result = numpy.zeros((pre_result.shape[0], pre_result.shape[1], 3))
for i in range(3):
result[:, :, i] = pre_result[:, :, i + 1]
mask_file_name = 'mission' + str(job_id) + '_' + str(
model_name) + '.png'
cv2.imwrite(analysis_data_folder + mask_file_name, result)
mission_db.update_predict_mask_by_id(job_id=job_id,
predict_mask=summary)
def get_project_by_similar_svs_file(svs_file):
mani = manifest.Manifest()
result = mani.get_project_by_similar_svs_file(svs_file)
return result
def nuclei_annotation_update_image():
slide_id = request.args.get('slide_id', default=1, type=int)
annotator_id = request.args.get('annotator_id', default=1, type=int)
annotation_project = request.args.get('project',
default="None",
type=str)
slide_uuid = request.args.get('slide_uuid', default="", type=str)
mani = manifest.Manifest()
wsi = mani.get_project_by_id(slide_id)
svs_file_path = original_data_root + wsi[1] + '/' + wsi[2]
annotation_root_folder = nuclei_annotation_data_root + annotation_project + '/' + wsi[
1] + '/'
if not os.path.exists(annotation_root_folder):
os.mkdir(annotation_root_folder)
error_message = 'N/A'
request_status = 0
v1 = request.args.get('var1', 0, type=int) # Top-left x coordinate
v2 = request.args.get('var2', 0, type=int) # Top-left y coordinate
v3 = request.args.get('var3', 0, type=int) # Bottom-right x
v4 = request.args.get('var4', 0, type=int) # Bottom-right y
v5 = request.args.get('var5', 0, type=int) # Viewer width (pixel)
v6 = request.args.get('var6', 0, type=int) # Viewer height (pixel)
v7 = request.args.get('var7', 0, type=int) # Region ID
loc_tl_coor = (v1, v2)
loc_viewing_size = (v3 - v1, v4 - v2)
loc_viewer_size = (v5, v6)
result_pic_url = annotation_root_folder + 'a' + str(
annotator_id) + '_r' + str(v7) + '.png'
if not os.path.exists(result_pic_url):
original_pic_url = annotation_root_folder + 'r' + str(v7) + '.png'
if not os.path.exists(original_pic_url):
oslide = openslide.OpenSlide(svs_file_path)
patch = oslide.read_region((v1, v2), 0,
(v3 - v1 + 1, v4 - v2 + 1))
patch.save(annotation_root_folder + 'r' + str(v7) + '.png')
oslide.close()
original_pic = cv2.imread(original_pic_url)
region_image_url = annotation_root_folder + 'r' + str(v7) + '.txt'
if not os.path.exists(region_image_url):
# with graph.as_default():
# mask = model.predict(original_pic_url)
# print(mask)
# region_image = model.water_image(mask)
region_image = np.zeros([512, 512], dtype=np.uint8)
np.savetxt(region_image_url,
region_image,
fmt="%d",
delimiter=",")
else:
region_image = np.loadtxt(region_image_url,
delimiter=",",
dtype=int)
annotator_data_url = annotation_root_folder + 'a' + str(
annotator_id) + '_r' + str(v7) + '.txt'
if not os.path.exists(annotator_data_url):
annotator_data = np.zeros(np.max(region_image) + 1)
print(annotator_data)
np.savetxt(annotator_data_url,
annotator_data,
fmt="%d",
delimiter=",")
else:
annotator_data = np.loadtxt(annotator_data_url,
delimiter=",",
dtype=int)
colour = [
tuple([124, 252, 0]),
tuple([0, 255, 255]),
tuple([137, 43, 224]),
tuple([255 * 0.82, 255 * 0.41, 255 * 0.12]),
tuple([255, 0, 0]),
tuple([0, 128, 255])
]
color_scheme = [[0.49, 0.99, 0], [0, 1, 1], [0.54, 0.17, 0.88],
[0.82, 0.41, 0.12], [1, 0, 0], [0, 0.5, 1]]
mask = np.zeros(original_pic.shape)
mask[region_image == -1] = tuple([0, 0, 0])
for i, val in enumerate(annotator_data):
if i != 1 and val != 0:
# mask[region_image == i] = colour[val - 1]
mask[region_image ==
i] = (original_pic[region_image == i] * 2.7 +
colour[val - 1]) / 3.3
# mask[region_image == i][0] = original_pic[region_image == i][0] *color_scheme[val - 1][0]
# mask[region_image == i][1] = original_pic[region_image == i][1] *color_scheme[val - 1][1]
# mask[region_image == i][2] = original_pic[region_image == i][2] *color_scheme[val - 1][2]
else:
mask[region_image == i] = original_pic[
region_image == i] # web.rm_file(last_url)
mask[region_image == -1] = tuple([255, 0, 0])
bound_size = 4
mask[:, 0:bound_size] = tuple([255, 0, 0])
mask[:, 511 - bound_size:511] = tuple([255, 0, 0])
mask[0:bound_size, :] = tuple([255, 0, 0])
mask[511 - bound_size:511, :] = tuple([255, 0, 0])
cv2.imwrite(result_pic_url, mask)
# Update URL configuration
slide_url = 'static/data/nuclei_annotation_data/' + annotation_project + '/' \
+ wsi[1] + '/a' + str(annotator_id) + '_r' + str(v7) + '.png'
result_pic = cv2.imread(result_pic_url)
cv2.imwrite(slide_url, result_pic)
return jsonify(slide_url=slide_url + '?a=' + str(uuid.uuid1()),
top_left_x=loc_tl_coor[0],
top_left_y=loc_tl_coor[1],
viewing_size_x=loc_viewing_size[0],
viewing_size_y=loc_viewing_size[1],
exit_code=request_status,
error_message=error_message)
def refresh_npy():
global annotation_project_table
result = []
available_slide_id = []
for item in manifest_controller.get_available_slide_id():
available_slide_id.append(item['id'])
file_list = os.listdir(manifest_root)
file_list.sort()
mani = manifest.Manifest()
for file in file_list:
print("start import manifest:", file)
temp = []
if file[-4:] != ".txt":
continue
slide_table_file = open('export/' + file[:-4] + '_slide_table.txt',
'w')
slide_id = []
missing_slide_uuid = []
project_name = file[:-4]
temp.append(project_name)
manifest_txt = open(manifest_root + file).readlines()
for wsi in manifest_txt:
info = wsi.split('\t')
if info[0] == "id":
continue
try:
info[0] = '[*] ' + info[0]
wsi = mani.get_project_by_uuid(info[0][4:])
if int(wsi[0]) not in available_slide_id:
info[0] = '[' + str(wsi[0]) + '] ' + wsi[1]
raise Exception
if int(wsi[0]) in slide_id:
continue
slide_table_file.write(
str(wsi[0]) + '\t' + str(wsi[1]) + '\t' + str(wsi[2]) +
'\n')
slide_id.append(int(wsi[0]))
except:
missing_slide_uuid.append(info[0])
slide_table_file.close()
# print(missing_slide_uuid)
# print(slide_id)
missing_slide_id_str = ""
slide_id_str = ""
if not slide_id:
# temp.append('Empty Manifest!')
# result.append(temp)
print(project_name, "Empty Manifest!")
continue
min_slide_id = min(slide_id)
for i in range(max(slide_id) + 1):
if i not in slide_id:
if i >= min_slide_id:
missing_slide_id_str = missing_slide_id_str + str(i) + ', '
elif i - 1 not in slide_id and i in slide_id:
slide_id_str = slide_id_str + str(i)
if i + 1 in slide_id:
slide_id_str = slide_id_str + '-'
else:
slide_id_str = slide_id_str + ', '
elif i - 1 in slide_id and i in slide_id and i + 1 not in slide_id:
slide_id_str = slide_id_str + str(i) + ', '
temp.append(slide_id_str)
temp.append(missing_slide_id_str)
temp.append('<button type="button" onclick="alert(' +
str(missing_slide_uuid) +
'.join(\'\\n\'))">see more</button>')
annotation_project_root = nuclei_annotation_root + project_name + '/'
if not os.path.exists(annotation_project_root):
os.mkdir(annotation_project_root)
temp.append("Unavailable")
annotation_project_root = freehand_annotation_root + project_name + '/'
if not os.path.exists(annotation_project_root):
os.mkdir(annotation_project_root)
temp.append("Unavailable")
temp.append('<a href="/nuclei_annotation_v2?' + 'project=' +
str(project_name) # '&slide_id=' + str(slide_id[0])
+ '" target="_Blank">nuclei annotate </a>' + '(' +
'<a href="/nuclei_annotation?' + 'project=' +
str(project_name) # '&slide_id=' + str(slide_id[0])
+ '" target="_Blank">v1 </a>' + ')')
temp.append('<a href="/freehand_annotation?' + 'project=' +
str(project_name) # '&slide_id=' + str(slide_id[0])
+ '" target="_Blank">freehand annotate </a>' + '(' +
'<a href="/freehand_stomache_annotation?' + 'project=' +
str(project_name) # '&slide_id=' + str(slide_id[0])
+ '" target="_Blank">stomach</a>' + ')')
temp.append("Unavailable")
temp.append("Unavailable")
temp.append('<a href="' + '/static/export/' + file[:-4] + '_slide_table.txt" ' + \
'download="' + file[:-4] + '_slide_table.txt' + '">Available Slides </a>' +
'<a href="' + '/available_slide_file/' + file[:-4] + '" ' + \
'download="' + file[:-4] + '.txt' + '">Slide List </a>'
)
result.append(temp)
filename = manifest_root + 'table.npy'
result = numpy.array(result)
annotation_project_table = result.copy()
numpy.save(filename, result) # 保存为.npy格式
refresh_freehand_annotation_progress()
refresh_nuclei_annotation_progress()
return
