def count_falseNegativeCase_to_pkl(bbox_conf_threshold = 0.95):
#*************retreive mask RCNN******************
# retreive path to model and config class
model_path = 'pneumonia20181020T2012_0165/mask_rcnn_pneumonia_0032.h5' # find_lastest_model(ROOT_DIR)
# retrieve DetectorConfig from pickle file
bbox_conf_threshold = 0.95
class inference_config(Config):
NAME = 'pneumonia'
GPU_COUNT = 1
IMAGES_PER_GPU = 1
BACKBONE = "resnet50" # 'resnet50'
NUM_CLASSES = 2
IMAGE_MAX_DIM = 256
IMAGE_MIN_DIM = 256
RPN_ANCHOR_SCALES = (32, 64, 128, 256)
TRAIN_ROIS_PER_IMAGE = 32
MAX_GT_INSTANCES = 4
DETECTION_MAX_INSTANCES = 3
DETECTION_MIN_CONFIDENCE = 0.78
DETECTION_NMS_THRESHOLD = 0.01
inference_config = inference_config()
# Recreate the model in inference mode
model = modellib.MaskRCNN(mode='inference',
config=inference_config,
model_dir=ROOT_DIR)
# Load trained weights (fill in path to trained weights here)
print('Retrieving mask RCNN...')
assert model_path != "", "Provide path to trained weights for mask RCNN"
print("Loading weights from ", model_path)
model.load_weights(model_path, by_name=True)
# *************retreive classifier******************
DROPOUT = 0.2
DENSE_COUNT = 128
classifier_path = 'classifier/ResNet50/384_384_lr_1e-4_balanced_orig_data_prtrained_imagenet_20epoch_all_layers/RESNET50_full_model.h5'
#LEARN_RATE = 1e-4
SHAPE = 2 # t_y.shape[1] in lung_opacity_classifier.py
print('Retrieving classifier...')
assert classifier_path != "", "Provide path to trained weights for classifier"
from keras.applications.resnet50 import ResNet50 as PTModel, preprocess_input
base_pretrained_model = PTModel(input_shape=(384, 384, 3),
include_top=False)
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, Conv2D, multiply, \
LocallyConnected2D, Lambda, AvgPool2D
from keras.models import Model
from keras.optimizers import Adam
# catch the output as feature from picked pre-trained model
pt_features = Input(base_pretrained_model.get_output_shape_at(0)[1:], name='feature_input')
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
# Normalize the activations of the previous layer at each batch,
# i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
from keras.layers import BatchNormalization
bn_features = BatchNormalization()(pt_features)
# Global average pooling operation for spatial data
gap = GlobalAveragePooling2D()(bn_features)
# Dropout layer
gap_dr = Dropout(DROPOUT)(gap)
# Another Dropout after a fully connected network after first dropout layer
dr_steps = Dropout(DROPOUT)(Dense(DENSE_COUNT, activation='elu')(gap_dr))
out_layer = Dense(SHAPE, activation='softmax')(dr_steps)
attn_model = Model(inputs=[pt_features],
outputs=[out_layer], name='trained_model')
from keras.models import Sequential
from keras.optimizers import Adam
classifier = Sequential(name='combined_model')
classifier.add(base_pretrained_model)
classifier.add(attn_model)
# load pre-trained weights
print("Loading weights from ", classifier_path)
classifier.load_weights(classifier_path)
dataset = read_training_data_from_pkl()
pn = []
fn = []
for image_id in dataset.image_ids:
print(image_id)
#load_image_gt() resizes image according to config already
original_image, image_meta, gt_class_id, gt_bbox, gt_mask = \
modellib.load_image_gt(dataset, inference_config,
image_id, use_mini_mask=False)
results = model.detect([original_image]) # , verbose=1)
r = results[0]
#find negative only: goal-to find classifier threshold to reduce false negative
index2keep = r['scores'] > bbox_conf_threshold
if len(index2keep[index2keep == True]) > 0:
continue #has bbox
#resize for classifier (may be changed to the same size later)
image = dataset.load_image(image_id)
image, window, scale, padding, crop = utils.resize_image(
image,
min_dim=384,
min_scale=0,
max_dim=384,
mode=inference_config.IMAGE_RESIZE_MODE)
image = np.expand_dims(image, axis=0)
pred = classifier.predict(image) #[no opacity, opacity]
#print('GT: ', ('Opacity' if len(gt_bbox) > 0 else 'No opacity'))
#print('Prediction: ', ('No opacity' if pred[0][0] > pred[0][1] else 'Opacity'))
#print(pred)
if len(gt_bbox) == 0: #pn
pn.append(pred[0][1])
else: #fn
fn.append(pred[0][1])
print('tn______________')
print(len(pn))
print('fn______________')
print(len(fn))
#save result to pkl
import pickle
output = 'classifier_threshold_analysis_n.pkl'
f = open(output, 'wb')
print('Dumping data to ' + output + '...')
pickle.dump((pn, fn), f)
print('Done.')
def count_falsePositiveCase_to_pkl(bbox_conf_threshold = 0.95):
#*************retreive mask RCNN******************
# retreive path to model and config class
model_path = 'pneumonia20181020T2012_0165/mask_rcnn_pneumonia_0032.h5' # find_lastest_model(ROOT_DIR)
# retrieve DetectorConfig from pickle file
bbox_conf_threshold = 0.95
class inference_config(Config):
NAME = 'pneumonia'
GPU_COUNT = 1
IMAGES_PER_GPU = 1
BACKBONE = "resnet50" # 'resnet50'
NUM_CLASSES = 2
IMAGE_MAX_DIM = 256
IMAGE_MIN_DIM = 256
RPN_ANCHOR_SCALES = (32, 64, 128, 256)
TRAIN_ROIS_PER_IMAGE = 32
MAX_GT_INSTANCES = 4
DETECTION_MAX_INSTANCES = 3
DETECTION_MIN_CONFIDENCE = 0.78
DETECTION_NMS_THRESHOLD = 0.01
inference_config = inference_config()
# Recreate the model in inference mode
model = modellib.MaskRCNN(mode='inference',
config=inference_config,
model_dir=ROOT_DIR)
resize_factor = ORIG_SIZE / inference_config.IMAGE_SHAPE[0]
# Load trained weights (fill in path to trained weights here)
print('Retrieving mask RCNN...')
assert model_path != "", "Provide path to trained weights for mask RCNN"
print("Loading weights from ", model_path)
model.load_weights(model_path, by_name=True)
# *************retreive classifier******************
DROPOUT = 0.2
DENSE_COUNT = 128
classifier_path = 'bbox_classifier/Xception_200_200_lr1e-4/Xception_full_model.h5'
#LEARN_RATE = 1e-4
SHAPE = 2 # t_y.shape[1] in lung_opacity_classifier.py
print('Retrieving bbox classifier...')
assert classifier_path != "", "Provide path to trained weights for bbox classifier"
from keras.applications.xception import Xception as PTModel, preprocess_input
base_pretrained_model = PTModel(input_shape=(200, 200, 3),
include_top=False)
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, Conv2D, multiply, \
LocallyConnected2D, Lambda, AvgPool2D
from keras.models import Model
from keras.optimizers import Adam
# catch the output as feature from picked pre-trained model
pt_features = Input(base_pretrained_model.get_output_shape_at(0)[1:], name='feature_input')
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
# Normalize the activations of the previous layer at each batch,
# i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
from keras.layers import BatchNormalization
bn_features = BatchNormalization()(pt_features)
# Global average pooling operation for spatial data
gap = GlobalAveragePooling2D()(bn_features)
# Dropout layer
gap_dr = Dropout(DROPOUT)(gap)
# Another Dropout after a fully connected network after first dropout layer
dr_steps = Dropout(DROPOUT)(Dense(DENSE_COUNT, activation='elu')(gap_dr))
out_layer = Dense(SHAPE, activation='softmax')(dr_steps)
attn_model = Model(inputs=[pt_features],
outputs=[out_layer], name='trained_model')
from keras.models import Sequential
from keras.optimizers import Adam
classifier = Sequential(name='combined_model')
classifier.add(base_pretrained_model)
classifier.add(attn_model)
# load pre-trained weights
print("Loading weights from ", classifier_path)
classifier.load_weights(classifier_path)
dataset = read_training_data_from_pkl()
tp = []
fp = []
from scipy import misc
for image_id in dataset.image_ids:
#image_id = random.choice(dataset.image_ids)
print(image_id)
#load_image_gt() resizes image according to config already
image_256, image_meta, gt_class_id, gt_bbox, gt_mask = \
modellib.load_image_gt(dataset, inference_config,
image_id, use_mini_mask=False)
results = model.detect([image_256]) # , verbose=1)
r = results[0]
#find positive only: goal-to find classifier threshold to reduce false positive
index2remove = r['scores'] <= bbox_conf_threshold
if len(index2remove[index2remove == True]) == len(r['scores']):
continue #no bbox
#resize for classifier (may be changed to the same size later)
original_image = dataset.load_image(image_id)
num_instances = len(r['rois'])
for i in range(num_instances):
if r['scores'][i] > bbox_conf_threshold: # threshold and view_position != 'PA':
# prediction on bbox
x = r['rois'][i][1]
y = r['rois'][i][0]
x1 = int(x * resize_factor)
y1 = int(y * resize_factor)
width1 = int((r['rois'][i][3] - x) * resize_factor)
height1 = int((r['rois'][i][2] - y) * resize_factor)
masked = np.array(original_image[y1:y1 + height1, x1:x1 + width1, :], dtype=np.int32)
resized_patch = misc.imresize(masked, (200, 200), 'nearest')
#print(resized_patch.shape)
#print(resized_patch)
image = np.expand_dims(resized_patch.astype(np.float32), axis=0)
image = preprocess_input(image)
#image = preprocess_input(image)
pred = classifier.predict(image) # [bbox as FP, bbox as TP]
#print('GT: ', ('Opacity' if len(gt_bbox) > 0 else 'No opacity'))
#print('Prediction: ', ('No opacity' if pred[0][0] > pred[0][1] else 'Opacity'))
print(pred)
if len(gt_bbox): #pred[0][0] probability of being false positive bbox
tp.append(pred[0][0])
else: #fp
fp.append(pred[0][0])
print('tp______________')
print(len(tp))
print('fp______________')
print(len(fp))
#save result to pkl
import pickle
output = 'classifier_threshold_analysis_bbox_classifier.pkl'
f = open(output, 'wb')
print('Dumping data to ' + output + '...')
pickle.dump((tp, fp), f)
print('Done.')
