prepare data
training
predict
"C:\Program Files\Python36\pythonw.exe" C:/Users/88304/Desktop/Retina-Unet-1/retinaNN_predict.py
Using TensorFlow backend.
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:521: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:522: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Program Files\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
the side H is not compatible with the selected stride of 5
img_h 584, patch_h 48, stride_h 5
(img_h - patch_h) MOD stride_h: 1
So the H dim will be padded with additional 4 pixels
the side W is not compatible with the selected stride of 5
img_w 565, patch_w 48, stride_w 5
(img_w - patch_w) MOD stride_w: 2
So the W dim will be padded with additional 3 pixels
new full images shape:
(20, 1, 588, 568)
test images shape:
(20, 1, 588, 568)
test mask shape:
(20, 1, 584, 565)
test images range (min-max): 0.0 - 1.0
test masks are within 0-1
Number of patches on h : 109
Number of patches on w : 105
number of patches per image: 11445, totally for this dataset: 228900
test PATCHES images shape:
(228900, 1, 48, 48)
test PATCHES images range (min-max): 0.0 - 1.0
6···predicted images size :
(228900, 2304, 2)
N_patches_h: 109
N_patches_w: 105
N_patches_img: 11445
According to the dimension inserted, there are 20 full images (of 588x568 each)
(20, 1, 588, 568)
Orig imgs shape: (20, 1, 584, 565)
pred imgs shape: (20, 1, 584, 565)
Gtruth imgs shape: (20, 1, 584, 565)
======================= Evaluate the results =======================
Calculating results only inside the FOV:
y scores pixels: 4538143 (radius 270: 270*270*3.14==228906), including background around retina: 6599200 (584*565==329960)
y true pixels: 4538143 (radius 270: 270*270*3.14==228906), including background around retina: 6599200 (584*565==329960)
Area under the ROC curve: 0.9384105978375957
Area under Precision-Recall curve: 0.8165986520530656
Confusion matrix: Costum threshold (for positive) of 0.5
[[3910924 49570]
[ 232658 344991]]
Global Accuracy: 0.9378098045830641
Specificity: 0.9874838845861148
Sensitivity: 0.5972329217223609
Precision: 0.8743667012198367
C:\Program Files\Python36\lib\site-packages\sklearn\metrics\_classification.py:660: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.
FutureWarning)
Jaccard similarity score: 0.9378098045830641
F1 score (F-measure): 0.7097046934304317
进程已结束,退出代码0
