from skimage import img_as_float, img_as_ubyte
from skimage.color import rgb2hed
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import RandomizedPCA, SparsePCA, IncrementalPCA
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import normalize
from hed_bilateral import HedBilateralFilter
from segment_hist import SegmentStage
from cellcounting import common as com
from cellcounting.features.hog import HOGFeature
from cellcounting.features.lbp import LBP
# Create samples and its label
preprocessor = HedBilateralFilter()
filter_kernel = (7, 7)
preprocessor.set_param("bilateral_kernel", filter_kernel)
preprocessor.set_param("sigma_color", 9)
segment = SegmentStage(5)
hog = HOGFeature()
lbp = LBP(2, 16, "var")
labels = []
def load_list_files(filename):
"""
load all data form the file of list data
"""
files = []
data = []
hist_data = []
from hed_bilateral import HedBilateralFilter
from segment_hist import SegmentStage
from cellcounting import common as com
try:
filename = sys.argv[1]
except:
filename = "allidb2.txt"
files = []
with open(filename, "r") as f:
files = [line.strip() for line in f.readlines()]
# Create samples and its label
preprocessor = HedBilateralFilter()
filter_kernel = (7, 7)
preprocessor.set_param("bilateral_kernel", filter_kernel)
preprocessor.set_param("sigma_color", 9)
segment = SegmentStage(5)
labels = []
data = []
for img in files:
label = img[-5:-4]
labels.append(float(label))
image = cv2.imread(img, flags=1)
canny, gray = preprocessor.run(image)
# com.debug_im(image)
conts = segment.run(canny, gray, image)
try:
