def enhance(sol, dataset):
for scan in dataset.scans():
print(scan.id())
scan3D = scan.scan3D()
sol3D = make_sol3d(sol, scan)
enhance_scan(sol3D, scan3D)
for idx, slice in enumerate(sol3D):
cnts = findContours(slice)
for cnt in cnts:
coords = flatten(
pixelToMM(scan.aux[idx + 1], x, y)
for (x, y) in cv2tolist(cnt))
yield [scan.id(), idx + 1] + ["%.4f" % xy for xy in coords]
def compute(params):
scan_id, slice, nbslices, aux, scan3D, lung = params
img = scan3D[slice - 1]
print(scan_id, slice)
cnts = collect_contours(img, slice, nbslices)
res = []
if cnts:
# Keep best candidate
merged = merge_and_filter(cnts, slice, scan3D, lung)
corrected_cnts = findContours(merged)
for cnt in corrected_cnts:
# cnt = expand_blob(scan3D[slice-1], cnt)
coords = flatten(pixelToMM(aux, x, y) for (x, y) in cv2tolist(cnt))
res.append([scan_id, slice] + ["%.4f" % xy for xy in coords])
return res
def compute(params):
scan_id, slice, nbslices, img, aux = params
print(scan_id, slice)
res = []
for idx, contour_extractor in enumerate(contour_extractors):
for cnt in contour_extractor(img):
if (len(cnt) >= 5): # needed for fitEllipse
features = [idx, slice, slice / nbslices
] + features_from_contour(cnt, img)
features = [features] #single sample
precision = precision_clf.predict(features)
if precision > 0.01:
recall = recall_clf.predict(features)
if recall > 0.01:
coords = flatten(
pixelToMM(aux[slice], x, y)
for (x, y) in cv2tolist(cnt))
res.append([scan_id, slice] +
["%.4f" % xy for xy in coords])
return res
def compute(params):
scan_id, slice, nbslices, img, aux = params
print(scan_id, slice)
res = []
for idx, contour_extractor in enumerate(contour_extractors):
for cnt in contour_extractor(img):
if (len(cnt) >= 5): # needed for fitEllipse
features = [idx, slice, slice / nbslices
] + features_from_contour(cnt, img)
features = [features] #single sample
precision = precision_clf.predict(features)
if precision > 0.01:
recall = recall_clf.predict(features)
if recall > 0.01:
coords = flatten(
pixelToMM(aux[slice], x, y)
for (x, y) in cv2tolist(cnt))
res.append([scan_id, slice] +
["%.4f" % xy for xy in coords])
return res
pool = multiprocessing.Pool(40)
solutions = flatten(pool.imap(compute, dataset.images_and_aux()))
with open("/home/gerey/hms_lung/example_predictions_valid12.csv",
"w") as f:
# with open("/home/gerey/hms_lung/example_predictions8.csv","w") as f:
for s in solutions:
f.write(",".join(map(str, s)))
f.write("\n")
if __name__ == '__main__':
#dataset = Dataset("/home/gerey/hms_lung/data/provisional_extracted_no_gt", withGT=False)
dataset = Dataset("/home/gerey/hms_lung/data/example_extracted_valid",
withGT=False)
#dataset = Dataset("/home/gerey/hms_lung/data/extract", withGT=False)
scan_index = 1
solutions = []
for scan in dataset.scans():
for id, img, aux in scan.images_aux():
scan_id = scan.id()
# only process middle slices
# TODO: expend once false negatives are cleared
if scan.nb_slices() / 4 < id < scan.nb_slices() * 1.7 / 3:
print(scan_id, id, len(slices))
contours = contouring_binsym(img)
for contour in contours:
coords = flatten(
pixelToMM(aux[id], x, y) for (x, y) in contour)
solutions.append([scan_id, id] +
["%.4f" % xy for xy in coords])
with open("/home/gerey/hms_lung/predictions_valid8.csv", "w") as f:
# with open("/home/gerey/hms_lung/example_predictions8.csv","w") as f:
for s in solutions:
f.write(",".join(map(str, s)))
f.write("\n")
def generate_features(iter):
pool = multiprocessing.Pool(10)
return flatten(pool.imap(compute_features, iter))
def gen_params():
for scan in dataset.scans():
scan3D = scan.scan3D()
if USE_LUNG:
lung = segment_lung_mask(scan3D, fill_lung_structures=True)
if MAX_MASK:
lung = np.any(lung, axis=0)
else:
lung = None
for scan_id, slice_idx, aux in scan.gen_aux():
yield scan_id, slice_idx, scan.nb_slices(), aux, scan3D, lung
if __name__ == '__main__':
# dataset = Dataset("/home/gerey/hms_lung/data/provisional_extracted_no_gt", withGT=False)
dataset = Dataset(
"/home/gerey/hms_lung/data/example_extracted_valid_small2",
withGT=False)
pool = multiprocessing.Pool(30)
solutions = flatten(pool.imap(compute, gen_params()))
# solutions = flatten(map( compute, gen_params() ))
with open("/home/gerey/hms_lung/exemple_predictions_sample2_33.csv",
"w") as f:
# with open("/home/gerey/hms_lung/predictions31.csv","w") as f:
for s in solutions:
f.write(",".join(map(str, s)))
f.write("\n")