def predict_image(im_path):
plankton = Segmentation(im_path, target_shape=(75, 75, 3))
plankton.segment()
padded = plankton.get_padded()
feat = plankton.get_features()
feat = np.array(feat)
padded = preprocess_input(np.array(padded, dtype=np.float32))
x_img = padded.reshape(1, padded.shape[0], padded.shape[1], padded.shape[2])
x_feat = feat.reshape(1, feat.shape[0])
x_feat = mms.transform(x_feat)
y_hat = model.predict([x_img, x_feat])
valid = False
labels = {}
results = []
for i, y in enumerate(y_hat.flatten()):
if y > 0.5:
valid = True
labels[i] = y
if valid:
#sort by value. lower prob to higher prob. (given it is above threshold prob)
labels = sorted(labels.items(), key=lambda x: x[1], reverse=False)
save_image(im_path, labels)
label_hat = class_map[labels[0][0]]
y_hat = [str(x) for x in y_hat.flatten()]
results.append(im_path)
results.append(label_hat)
results.extend(y_hat)
return results
return None
def predict_image(im_path):
plankton = Segmentation(im_path, target_shape=(75, 75, 3))
plankton.segment()
padded = plankton.get_padded()
feat = plankton.get_features()
feat = np.array(feat)
padded = preprocess_input(np.array(padded, dtype=np.float32))
x_img = padded.reshape(1, padded.shape[0], padded.shape[1],
padded.shape[2])
x_feat = feat.reshape(1, feat.shape[0])
x_feat = mms.transform(x_feat)
y_hat = model.predict([x_img, x_feat])
label_hat = class_map[np.argmax(y_hat)]
save_image(im_path, y_hat)
y_hat = [str(x) for x in y_hat.flatten()]
results = []
results.append(im_path)
results.append(label_hat)
results.extend(y_hat)
return results
mms = pickle.load(handle)
X_img = np.empty((df.shape[0], input_shape[0], input_shape[1], input_shape[2]), dtype=np.uint8)
X_feat = np.empty((df.shape[0], feat_shape[0]))
y = np.empty((df.shape[0], n_classes), dtype=int)
data_path = os.path.join(os.getcwd(), 'data')
for i, (im_name, label) in tqdm(enumerate(zip(df.im_name, df.label))):
im_dir = os.path.join(data_path, class_map[label])
im_path = os.path.join(im_dir, im_name)
plankton = Segmentation(im_path, target_shape=input_shape)
plankton.segment()
padded = plankton.get_padded()
aug = seq.augment_image(padded)
X_img[i,] = preprocess_input(padded)
X_feat[i,] = plankton.get_features()
y[i,] = to_categorical(label, num_classes=n_classes)
X_feat = mms.transform(X_feat)
X_img, X_feat, y = shuffle(X_img, X_feat, y, random_state=0)
print(X_img.shape)
print(X_feat.shape)
print(y.shape)
X = [X_img, X_feat]
checkpoint = ModelCheckpoint('./models/inception_v3_3k_cached.model', monitor='val_acc', verbose=1, mode='max',
save_best_only=True, save_weights_only=False, period=1)
reduceLROnPlato = ReduceLROnPlateau(monitor='val_loss', factor=0.5,
for c in classes:
im_dir = os.path.join(data_path, c)
exists = os.path.join(pad_path, c)
if os.path.isdir(exists) is False:
os.mkdir(exists)
images = os.listdir(im_dir)
for im_name in images:
im_path = os.path.join(im_dir, im_name)
im_pad_path = os.path.join(exists, im_name)
plankton = Segmentation(im_path, target_shape=(75, 75, 3))
plankton.segment()
padded = plankton.get_padded()
padded = cv2.cvtColor(padded, cv2.COLOR_RGB2BGR)
cv2.imwrite(im_pad_path, padded)
feats[im_name] = plankton.get_features()
feat_list.append(plankton.get_features())
with open('features.p', 'wb') as handle:
pickle.dump(feats, handle, protocol=pickle.HIGHEST_PROTOCOL)
df_feats = pd.DataFrame(data=feat_list, columns=plankton.get_columns())
df_feats.to_csv('extracted_features.csv', index=False)
mms = MinMaxScaler()
mms.fit(df_feats.values)
with open('normalizer.p', 'wb') as handle:
pickle.dump(mms, handle, protocol=pickle.HIGHEST_PROTOCOL)
