def test_save_superpixels(self):
test_list = range(50, 131)
for id in test_list:
x1, y1, x2, y2 = 0, 0, 0, 0
slice_id = "Test_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Testing/images/%s.tif" % slice_id,
None, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
seg = Segmentation(c, imgCone)
label_map = detector.genrating_superpixels(seg, x1, y1, x2, y2, 1.25, superpixel_area=1000)
save_filename = "{}/data/Segmentations/{}_labelmap.npz".format(c.PROJECT_ROOT, imgCone.slice_id)
np.savez_compressed(save_filename, x1=x1, y1=y1, x2=x2, y2=y2, scale=1.25, labelmap=label_map)
print(">>> >>> ", save_filename, " saved!")
def test_adaptive_detect_region(self):
# # train set
# test_set = {1: (1, 2100, 3800, 2400, 4000),
# 2: (3, 2400, 4700, 2600, 4850), # 小的局部150 x 200
# 3: (3, 2000, 4300, 2800, 4900), # 600 x 800
# 4: (3, 721, 3244, 3044, 5851), # 全切片范围
# 5: (44, 410, 2895, 2813, 6019), #
# 6: (47, 391, 2402, 2891, 4280), #
# }
#
# id = 3
# roi = test_set[id]
# id = roi[0]
# x1 = roi[1]
# y1 = roi[2]
# x2 = roi[3]
# y2 = roi[4]
x1, y1, x2, y2 = 0, 0, 0, 0
id = 2
slice_id = "Tumor_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Train_Tumor/%s.tif" % slice_id,
'Train_Tumor/%s.xml' % slice_id, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
eff_zone = imgCone.get_effective_zone(1.25)
x1, y1, x2, y2 = imgCone.get_mask_min_rect(eff_zone)
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.process(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=100, batch_size=100,
limit_sampling_density=3, enhanced=True)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
def test_adaptive_detect_region_train_slice_normal_part(self):
# test set
test_list = range(1, 161)
# test_list = [3,5,6,7,9,12,14]
result = {}
# 如果输出癌变概率图，并进行评估
enable_evaluate = False
for id in test_list:
x1, y1, x2, y2 = 0, 0, 0, 0
slice_id = "Test_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Train_Normal/%s.tif" % slice_id,
None, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.adaptive_detect(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=20, batch_size=100,
limit_sampling_density=1, enhanced=True,
superpixel_area=1000, superpixels_boundaries_spacing=120)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
def test_adaptive_detect_region_test_slice_normal_part(self):
# test set
# test_list = [3, 5, 6, 7, 9, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 31, 32, 34, 35, 36, 37, 39, 41, 42, 43, 44, 45,
# 47, 49, 50, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 67, 70, 72, 76, 77, 78, 80, 81, 83, 85, 86, 87, 88, 89, 91,
# 92, 93, 95, 96, 98, 100, 101, 103, 106, 107, 109, 111, 112, 114, 115, 118, 119, 120, 123, 124, 125, 126, 127,
# 128, 129, 130]
test_list = [111, 112, 114, 115, 118, 119, 120, 123, 124, 125, 126, 127,
128, 129, 130]
# test_list = [3,5,6,7,9,12,14]
result = {}
# 如果输出癌变概率图，并进行评估
enable_evaluate = False
for id in test_list:
x1, y1, x2, y2 = 0, 0, 0, 0
slice_id = "Test_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Testing/images/%s.tif" % slice_id,
None, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.adaptive_detect(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=10, batch_size=100,
limit_sampling_density=1, enhanced=False,
superpixel_area=1000, superpixels_boundaries_spacing=120)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
if enable_evaluate:
cmb = CancerMapBuilder(c, extract_scale=40, patch_size=256)
cancer_map = cmb.generating_probability_map(history, x1, y1, x2, y2, 1.25)
src_img = detector.get_img_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
if imgCone.ano_filename is None:
mask_img = np.zeros(src_img.shape, dtype=np.bool)
levels = [0.3, 0.5, 0.6, 0.8]
false_positive_rate, true_positive_rate, roc_auc, dice = Evaluation.evaluate_slice_map(cancer_map,
mask_img,
levels)
result[slice_id] = (roc_auc, dice)
# 存盘输出部分
# self.show_results(cancer_map, dice, false_positive_rate, history, levels, mask_img, roc_auc, slice_id,
# src_img, true_positive_rate)
save_path = "{}/results/cancer_pic".format(c.PROJECT_ROOT)
Evaluation.save_result_picture(slice_id, src_img, mask_img, cancer_map, history, roc_auc, levels,
save_path)
# detector.save_result_xml(x1, y1, 1.25, cancer_map, levels)
for slice, (auc, dices) in result.items():
print("#################{}###################".format(slice))
for t, value in dices:
print("threshold = {:.3f}, dice coef = {:.6f}".format(t, value))
print("ROC auc: {:.6f}".format(auc))
print("#################{}###################".format(slice))
def test_adaptive_detect_region_test_slice(self):
# test set
# test_list = [1,2,4,8,10,11,13,16,21,26,27,29,30,33,38,40,46,48,51,52,61,64,65,66,68,69,71,73,74,75,79,
# 82,84,90,94,97,99,102,104,105,108,110,113,116,117,121,122]
# test_list = [4,10,29,30,33,38,48,66,79,84,99,102,116,117,122]
test_list = [117]
# test_list = [16,21,26,27,29,30,33,38,40,46,48,51,52,61,64,65,66,68,69,71,73,74,75,79,
# 82,84,90,94,97,99,102,104,105,108,110,113,116,117,121,122]
result = {}
# 如果输出癌变概率图，并进行评估
enable_evaluate = True
for id in test_list:
x1, y1, x2, y2 = 0, 0, 0, 0
slice_id = "Test_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Testing/images/%s.tif" % slice_id,
'Testing/images/%s.xml' % slice_id, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.process(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=20, batch_size=100,
limit_sampling_density=2, enhanced=True)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
if enable_evaluate:
cmb = CancerMapBuilder(c, extract_scale=40, patch_size=256)
cancer_map = cmb.generating_probability_map(history, x1, y1, x2, y2, 1.25)
src_img = detector.get_img_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
mask_img = detector.get_true_mask_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
levels = [0.3, 0.5, 0.6, 0.8]
false_positive_rate, true_positive_rate, roc_auc, dice = Evaluation.evaluate_slice_map(cancer_map,
mask_img,
levels)
result[slice_id] = (roc_auc, dice)
# 存盘输出部分
# self.show_results(cancer_map, dice, false_positive_rate, history, levels, mask_img, roc_auc, slice_id,
# src_img, true_positive_rate)
save_path = "{}/results/cancer_pic".format(c.PROJECT_ROOT)
Evaluation.save_result_picture(slice_id, src_img, mask_img, cancer_map, history, roc_auc, levels,
save_path)
# detector.save_result_xml(x1, y1, 1.25, cancer_map, levels)
for slice, (auc, dices) in result.items():
print("#################{}###################".format(slice))
for t, value in dices:
print("threshold = {:.3f}, dice coef = {:.6f}".format(t, value))
print("ROC auc: {:.6f}".format(auc))
print("#################{}###################".format(slice))
def test_adaptive_detect_region_test(self):
# test test
test_set = {1: ("001", 100, 100, 2600, 2700), # 检测 dice =0.7811
1.1: ("001", 800, 1600, 1600, 2300), # dice = 0.76084
16: ("016", 0, 200, 3250, 2900), # dice = 0.92056
21: ("021", 0, 0, 0, 0), # dice = 0.93743
26: ("026", 0, 0, 0, 0), # 检测，dice c3= 0.7601
61: ("061", 0, 0, 0, 0), # 检测, c3 = 0.75468
4: ("004", 0, 0, 0, 0), # 检测，c3 = 2.5917e-05， 检测区域太小
8: ("008", 0, 0, 0, 0), # 检测，c3 = 0.003159
10: ("010", 0, 0, 0, 0), # 检测,c3 = 3.7647e-05
11: ("011", 0, 0, 0, 0), # 检测,c3 = 0.0005543
13: ("013", 0, 0, 0, 0), # 检测,c3 = 0.003278
27: ("027", 0, 0, 0, 0), # 检测,c3 = 0.9601, 0.3540
29: ("029", 0, 0, 0, 0), # 检测,c3 =
30: ("030", 0, 0, 0, 0), # 检测,c3 = 0.02823
33: ("033", 0, 0, 0, 0), # 检测,c3 =
38: ("038", 0, 0, 0, 0), # 检测,c3 = 0.000214
40: ("040", 0, 0, 0, 0), # 检测,c3 = 0.06608, 检测错误
46: ("046", 0, 0, 0, 0), # 检测,c3 = 0.0005763
48: ("048", 0, 0, 0, 0), # 检测,c3 =
51: ("051", 0, 0, 0, 0), # 检测,c3 = 0.1478
52: ("052", 0, 0, 0, 0), # 检测,c3 =
71: ("071", 0, 0, 0, 0), # 检测,c3 =
64: ("064", 0, 0, 0, 0), # 检测,c3 =
65: ("065", 0, 0, 0, 0), # 检测,c3 =
66: ("066", 0, 0, 0, 0), # 检测,c3 =
68: ("068", 0, 0, 0, 0), # 检测,c3 =
69: ("069", 0, 0, 0, 0), # 检测,c3 =
73: ("073", 0, 0, 0, 0), # 检测,c3 =
}
id = 39
roi = test_set[id]
slice_id = roi[0]
x1 = roi[1]
y1 = roi[2]
x2 = roi[3]
y2 = roi[4]
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
# tag = imgCone.open_slide("Tumor/Tumor_%s.tif" % slice_id,
# 'Tumor/tumor_%s.xml' % slice_id, "Tumor_%s" % slice_id)
tag = imgCone.open_slide("Testing/images/test_%s.tif" % slice_id,
'Testing/images/test_%s.xml' % slice_id, "test_%s" % slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
eff_zone = imgCone.get_effective_zone(1.25)
x1, y1, x2, y2 = imgCone.get_mask_min_rect(eff_zone)
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
cancer_map, history = detector.process(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=100, batch_size=100,
limit_sampling_density=1, )
src_img = detector.get_img_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
mask_img = detector.get_true_mask_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
levels = [0.2, 0.3, 0.5, 0.6, 0.8]
false_positive_rate, true_positive_rate, roc_auc, dice = Evaluation.evaluate_slice_map(cancer_map, mask_img,
levels)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
enable_show = False
# 存盘输出部分
if enable_show:
save_path = "{}/results/cancer_pic".format(c.PROJECT_ROOT)
Evaluation.save_result_picture(slice_id, src_img, mask_img, cancer_map, history, roc_auc, levels, save_path)
detector.save_result_xml(x1, y1, 1.25, cancer_map, levels)
def test_adaptive_detect_region_train_slice(self):
# train set
# train_list = [9, 11, 16, 26, 39, 47, 58, 68, 72, 76]
# train_list = [11, 16, 26, 39, 47, 58, 68, 72, 76]
train_list = range(1,112) # [29,33,61,89,95]range(27,39)
result = {}
# 如果输出癌变概率图，并进行评估
enable_evaluate = False
for id in train_list:
x1, y1, x2, y2 = 0, 0, 0, 0
slice_id = "Tumor_{:0>3d}".format(id)
c = Params()
c.load_config_file(JSON_PATH)
imgCone = ImageCone(c, Open_Slide())
# 读取数字全扫描切片图像
tag = imgCone.open_slide("Train_Tumor/%s.tif" % slice_id,
'Train_Tumor/%s.xml' % slice_id, slice_id)
detector = AdaptiveDetector(c, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.adaptive_detect(x1, y1, x2, y2, 1.25, extract_scale=40, patch_size=256,
max_iter_nums=20, batch_size=100,
limit_sampling_density=1, enhanced=True,
superpixel_area=1000, superpixels_boundaries_spacing=120)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
if enable_evaluate:
cmb = CancerMapBuilder(c, extract_scale=40, patch_size=256)
cancer_map = cmb.generating_probability_map(history, x1, y1, x2, y2, 1.25)
src_img = detector.get_img_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
mask_img = detector.get_true_mask_in_detect_area(x1, y1, x2, y2, 1.25, 1.25)
levels = [0.3, 0.5, 0.6, 0.8]
false_positive_rate, true_positive_rate, roc_auc, dice = Evaluation.evaluate_slice_map(cancer_map,
mask_img,
levels)
result[slice_id] = (roc_auc, dice)
# 存盘输出部分
# self.show_results(cancer_map, dice, false_positive_rate, history, levels, mask_img, roc_auc, slice_id,
# src_img, true_positive_rate)
# save_path = "{}/results/cancer_pic".format(c.PROJECT_ROOT)
# Evaluation.save_result_picture(slice_id, src_img, mask_img, cancer_map, history, roc_auc, levels,
# save_path)
# detector.save_result_xml(x1, y1, 1.25, cancer_map, levels)
for slice, (auc, dices) in result.items():
print("#################{}###################".format(slice))
for t, value in dices:
print("threshold = {:.3f}, dice coef = {:.6f}".format(t, value))
print("ROC auc: {:.6f}".format(auc))
print("#################{}###################".format(slice))
def test_detect(self):
slide_list = [1]
result = {}
Train_Tag = True
# 如果输出癌变概率图，并进行评估
enable_evaluate = False
for id in slide_list:
x1, y1, x2, y2 = 0, 0, 0, 0
if Train_Tag:
slice_id = "Tumor_{:0>3d}".format(id)
else:
slice_id = "Test_{:0>3d}".format(id)
imgCone = ImageCone(self._params, Open_Slide())
# 读取数字全扫描切片图像
if Train_Tag:
tag = imgCone.open_slide("Train_Tumor/%s.tif" % slice_id,
'Train_Tumor/%s.xml' % slice_id,
slice_id)
else:
tag = imgCone.open_slide("Testing/images/%s.tif" % slice_id,
'Testing/images/%s.xml' % slice_id,
slice_id)
detector = AdaptiveDetector(self._params, imgCone)
if x2 * y2 == 0:
x1, y1, x2, y2 = detector.get_detection_rectangle()
print("x1, y1, x2, y2: ", x1, y1, x2, y2)
history = detector.adaptive_detect(
x1,
y1,
x2,
y2,
1.25,
extract_scale=40,
patch_size=256,
max_iter_nums=20,
batch_size=10,
limit_sampling_density=1,
enhanced=True,
superpixel_area=1000,
superpixels_boundaries_spacing=120)
detector.save_result_history(x1, y1, x2, y2, 1.25, history)
if enable_evaluate:
cmb = CancerMapBuilder(self._params,
extract_scale=40,
patch_size=256)
cancer_map = cmb.generating_probability_map(
history, x1, y1, x2, y2, 1.25)
src_img = detector.get_img_in_detect_area(
x1, y1, x2, y2, 1.25, 1.25)
mask_img = detector.get_true_mask_in_detect_area(
x1, y1, x2, y2, 1.25, 1.25)
levels = [0.3, 0.5, 0.8]
false_positive_rate, true_positive_rate, roc_auc, dice = Evaluation.evaluate_slice_map(
cancer_map, mask_img, levels)
result[slice_id] = (roc_auc, dice)
# save results
# self.show_results(cancer_map, dice, false_positive_rate, history, levels, mask_img, roc_auc, slice_id,
# src_img, true_positive_rate)
save_path = "{}/results/cancer_pic".format(
self._params.PROJECT_ROOT)
Evaluation.save_result_picture(slice_id, src_img, mask_img,
cancer_map, history, roc_auc,
levels, save_path)
# detector.save_result_xml(x1, y1, 1.25, cancer_map, levels)
for slice, (auc, dices) in result.items():
print("#################{}###################".format(slice))
for t, value in dices:
print("threshold = {:.3f}, dice coef = {:.6f}".format(
t, value))
print("ROC auc: {:.6f}".format(auc))
print("#################{}###################".format(slice))