def analyze_image(in_file,
out_dir,
stats_file=False,
scale=1,
debug_dir=False,
fix_seed=False):
from lib.dir import ensure_dir_exists
from lib.debug import Debug
from lib.stats_recorder import Record
if debug_dir:
Debug.set_directory(debug_dir)
if fix_seed:
Debug.set_seed(1234567890)
if stats_file:
Record.activate()
ensure_dir_exists(out_dir)
from lib.timer import timeStart, timeEnd
from lib.load_image import get_grayscale_image, image_as_float
from skimage.morphology import medial_axis
from lib.roi_detection import get_roi, corners_to_geojson
from lib.polygon_mask import mask_image
from lib.meanline_detection import detect_meanlines, meanlines_to_geojson
from lib.threshold import flatten_background
from lib.ridge_detection import find_ridges
from lib.binarization import binary_image
from lib.intersection_detection import find_intersections
from lib.trace_segmentation import get_segments, segments_to_geojson
from lib.geojson_io import save_features, save_json
from lib.utilities import encode_labeled_image_as_rgb
from scipy import misc
import numpy as np
paths = {
"roi": out_dir + "/roi.json",
"meanlines": out_dir + "/meanlines.json",
"intersections": out_dir + "/intersections.json",
"intersections_raster": out_dir + "/intersections_raster.png",
"segments": out_dir + "/segments.json",
"segment_regions": out_dir + "/segment_regions.png",
"segment_assignments": out_dir + "/segment_assignments.json"
}
timeStart("get all metadata")
timeStart("read image")
img_gray = image_as_float(get_grayscale_image(in_file))
timeEnd("read image")
print "\n--ROI--"
timeStart("get region of interest")
corners = get_roi(img_gray, scale=scale)
timeEnd("get region of interest")
timeStart("convert roi to geojson")
corners_as_geojson = corners_to_geojson(corners)
timeEnd("convert roi to geojson")
timeStart("saving roi as geojson")
save_features(corners_as_geojson, paths["roi"])
timeEnd("saving roi as geojson")
print "\n--MASK IMAGE--"
roi_polygon = corners_as_geojson["geometry"]["coordinates"][0]
timeStart("mask image")
masked_image = mask_image(img_gray, roi_polygon)
timeEnd("mask image")
Debug.save_image("main", "masked_image", masked_image.filled(0))
if Record.active:
non_masked_values = 255 * masked_image.compressed()
bins = np.arange(257)
image_hist, _ = np.histogram(non_masked_values, bins=bins)
Record.record("roi_intensity_hist", image_hist.tolist())
print "\n--MEANLINES--"
meanlines = detect_meanlines(masked_image, corners, scale=scale)
timeStart("convert meanlines to geojson")
meanlines_as_geojson = meanlines_to_geojson(meanlines)
timeEnd("convert meanlines to geojson")
timeStart("saving meanlines as geojson")
save_features(meanlines_as_geojson, paths["meanlines"])
timeEnd("saving meanlines as geojson")
print "\n--FLATTEN BACKGROUND--"
img_dark_removed, background = \
flatten_background(masked_image, prob_background=0.95,
return_background=True, img_gray=img_gray)
Debug.save_image("main", "flattened_background", img_dark_removed)
masked_image = None
print "\n--RIDGES--"
timeStart("get horizontal and vertical ridges")
ridges_h, ridges_v = find_ridges(img_dark_removed, background)
ridges = ridges_h | ridges_v
timeEnd("get horizontal and vertical ridges")
print "\n--THRESHOLDING--"
timeStart("get binary image")
img_bin = binary_image(img_dark_removed,
markers_trace=ridges,
markers_background=background)
timeEnd("get binary image")
img_dark_removed = None
background = None
print "\n--SKELETONIZE--"
timeStart("get medial axis skeleton and distance transform")
img_skel, dist = medial_axis(img_bin, return_distance=True)
timeEnd("get medial axis skeleton and distance transform")
Debug.save_image("skeletonize", "skeleton", img_skel)
print "\n--INTERSECTIONS--"
intersections = find_intersections(img_bin, img_skel, dist, figure=False)
timeStart("convert to geojson")
intersection_json = intersections.asGeoJSON()
timeEnd("convert to geojson")
timeStart("saving intersections as geojson")
save_features(intersection_json, paths["intersections"])
timeEnd("saving intersections as geojson")
timeStart("convert to image")
intersection_image = intersections.asImage()
timeEnd("convert to image")
Debug.save_image("intersections", "intersections", intersection_image)
timeStart("save intersections raster")
misc.imsave(paths["intersections_raster"], intersection_image)
timeEnd("save intersections raster")
print "\n--SEGMENTS--"
timeStart("get segments")
segments, labeled_regions = \
get_segments(img_gray, img_bin, img_skel, dist, intersection_image,
ridges_h, ridges_v, figure=True)
timeEnd("get segments")
timeStart("encode labels as rgb values")
rgb_segments = encode_labeled_image_as_rgb(labeled_regions)
timeEnd("encode labels as rgb values")
timeStart("save segment regions")
misc.imsave(paths["segment_regions"], rgb_segments)
timeEnd("save segment regions")
timeStart("convert centerlines to geojson")
segments_as_geojson = segments_to_geojson(segments)
timeEnd("convert centerlines to geojson")
timeStart("saving centerlines as geojson")
save_features(segments_as_geojson, paths["segments"])
timeEnd("saving centerlines as geojson")
#return (img_gray, ridges, img_bin, intersections, img_seg)
# return segments
# detect center lines
# connect segments
# output data
time_elapsed = timeEnd("get all metadata")
Record.record("time_elapsed", float("%.2f" % time_elapsed))
if (stats_file):
Record.export_as_json(stats_file)
# TODO: refactor this into some sort of status module.
# For now, since this is our only problematic status,
# it's hard to know what to generalize. Eventually
# we might want to flag several different statuses
# for specific conditions.
max_segments_reasonable = 11000
if (len(segments) > max_segments_reasonable):
print "STATUS>>>problematic<<<"
else:
print "STATUS>>>complete<<<"
def analyze_image(in_file,
out_dir,
stats_file=False,
scale=1,
debug_dir=False,
fix_seed=False):
from lib.dir import ensure_dir_exists
from lib.debug import Debug
from lib.stats_recorder import Record
if debug_dir:
Debug.set_directory(debug_dir)
if fix_seed:
Debug.set_seed(1234567890)
if stats_file:
Record.activate()
ensure_dir_exists(out_dir)
from lib.timer import timeStart, timeEnd
from lib.load_image import get_grayscale_image, image_as_float
from lib.roi_detection import get_roi, corners_to_geojson
from lib.polygon_mask import mask_image
from lib.meanline_detection import detect_meanlines, meanlines_to_geojson
from lib.geojson_io import save_features
paths = {
"roi": out_dir + "/roi.json",
"meanlines": out_dir + "/meanlines.json",
"intersections": out_dir + "/intersections.json",
"segments": out_dir + "/segments.json",
"segment_assignments": out_dir + "/segment_assignments.json"
}
timeStart("get roi and meanlines")
timeStart("read image")
img_gray = image_as_float(get_grayscale_image(in_file))
timeEnd("read image")
print "\n--ROI--"
timeStart("get region of interest")
corners = get_roi(img_gray, scale=scale)
timeEnd("get region of interest")
timeStart("convert roi to geojson")
corners_as_geojson = corners_to_geojson(corners)
timeEnd("convert roi to geojson")
timeStart("saving roi as geojson")
save_features(corners_as_geojson, paths["roi"])
timeEnd("saving roi as geojson")
print "\n--MASK IMAGE--"
roi_polygon = corners_as_geojson["geometry"]["coordinates"][0]
timeStart("mask image")
masked_image = mask_image(img_gray, roi_polygon)
timeEnd("mask image")
Debug.save_image("main", "masked_image", masked_image.filled(0))
print "\n--MEANLINES--"
meanlines = detect_meanlines(masked_image, corners, scale=scale)
timeStart("convert meanlines to geojson")
meanlines_as_geojson = meanlines_to_geojson(meanlines)
timeEnd("convert meanlines to geojson")
timeStart("saving meanlines as geojson")
save_features(meanlines_as_geojson, paths["meanlines"])
timeEnd("saving meanlines as geojson")
timeEnd("get roi and meanlines")
if (stats_file):
Record.export_as_json(stats_file)
