fn = result_dir + '/' + testn + '_boxes.pkl'
pickle.dump(boxes, open(fn, "wb"))
img=add_boxes(boxes, img)
img.save(result_dir +'/' + testn +'_boxes.jpg')
# img = Image.fromarray(arr)
# boxes = non_max_suppression_merge(boxes, overlapThresh=0.1)
# img = add_boxes(boxes, img)
# img.save(result_dir + '/' + testn + '_boxes_nms.jpg')
#
# img = Image.fromarray(arr)
# fn = result_dir + '/' + testn + '_half_boxes.pkl'
# pickle.dump(half_boxes, open(fn, "wb"))
# img=add_boxes(half_boxes, img)
# img.save(result_dir +'/' + testn +'_half_boxes.jpg')
m, s = divmod(int( time.time() - t0), 60)
h, m = divmod(m, 60)
report.write("--- time elapsed for classification: %d:%02d:%02d " %(h, m, s) + " ---\n")
if step2 is True:
#### here goes canny, hough and GVF, snake
pts = []
img = Image.fromarray(arr)
pts = edge_detect(boxes, arr, show=True)
img = add_points(pts, img)
img.show()
report.close()
img3 =Image.open(rawfn)
add_v_list = search_up(edges=ret, folder=folder, n=4, v = v_list[0], debug=DEBUG, raw =img3)
for v in add_v_list:
v_list.insert(0, v)
folder= '/Users/ruhansa/Desktop/result/xray/search_down4'
img2 =Image.open(rawfn)
add_v_list = search_down(edges=ret, folder=folder, n=4, v = v_list[-1], raw=img2, debug=DEBUG)
for v in add_v_list:
v_list.append(v)
from src.utils.util import batch_extend
batch_extend(v_list, canny1)
from src.utils.io import add_lines, add_points
img1 =Image.open(rawfn)
for v in v_list:
if v.topline is not None:
print v.topline.line
add_points(v.topline.extend_pts_left, img1)
add_points(v.topline.extend_pts_right, img1)
add_lines([v.topline.line], img1)
if v.bottomline is not None:
print v.bottomline.line
add_points(v.bottomline.extend_pts_left, img1)
add_points(v.bottomline.extend_pts_right, img1)
add_lines([v.bottomline.line], img1)
img1.show()
fn = '/Users/ruhansa/Desktop/result/xray/search_up_down'+ datetime.datetime.now().strftime("%Y%m%d%H%M") + '.jpg'
img1.save(fn, 'JPEG')
fn = prev_halves_folder + testn + '_half_boxes.pkl'
halves = pickle.load(open(fn, "rb"))
img3 = add_boxes(halves, img3)
img3.show()
if debug is True:
raw_input("rough detection")
img3 = Image.fromarray(arr)
halves_b, halves_c, halves_wh= non_max_suppression_center(halves)
pts=get_spline(halves_b, 0.2*arr.shape[0], 0.75*arr.shape[0])
if pts is not None:
img3 = add_boxes(halves_b, img3)
img3.show()
if debug is True:
raw_input("clear out the clutter")
img3 = add_points(pts, img3)
img3.show()
if debug is True:
raw_input("rough curvature line")
fn = prev_whole_folder + testn + '_boxes.pkl'
wholes = pickle.load(open(fn, "rb"))
wholes_b, wholes_c, wholes_wh= non_max_suppression_center(wholes, overlapThresh=0.3)
img3 = Image.fromarray(arr)
if pts is not None:
img3 = add_boxes(wholes_b, img3)
img3 = add_points(pts, img3)
img3.show()
if debug is True:
raw_input("finer detect")
