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change panel disconnect

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This commit is contained in:
Daniel Saavedra
2020-04-03 13:15:34 -03:00
parent 7cf0c577a1
commit 4351f33609
7 changed files with 118 additions and 212 deletions
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97
panel_disconnect.py
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@@ -8,34 +8,107 @@ Created on Tue Mar 17 13:55:42 2020
import numpy as np
def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
xmin = box.xmin + merge
xmax = box.xmax - merge
ymin = box.ymin + merge
ymax = box.ymax - merge
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
area = (ymax - ymin)*(xmax - xmin)
z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
if area > area_min:
box.z_score = z_score
new_boxes.append(box)
#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
mean_score = np.mean([box.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
z_score = (box.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
return new_boxes
box.score = -1
return new_boxes
def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
xmin = box.xmin + merge
xmax = box.xmax - merge
ymin = box.ymin + merge
ymax = box.ymax - merge
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
area = (ymax - ymin)*(xmax - xmin)
z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
if area > area_min:
box.z_score = z_score
new_boxes.append(box)
#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
mean_score = np.mean([box.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
normal_score = ([box.z_score for box in new_boxes] - mean_score)/sd_score
# plt.figure()
# _ = plt.hist(normal_score, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram with 'auto' bins")
# plt.show()
#
# plt.figure()
# mean = np.mean([boxes_area_score[i]['area'] for i in boxes_area_score])
# sd = np.std([boxes_area_score[i]['area'] for i in boxes_area_score])
# normal = ([boxes_area_score[i]['area'] for i in boxes_area_score] - mean)/sd
# _ = plt.hist(normal, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram with 'auto' bins")
# plt.show()
new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
z_score = (box.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()
plt.figure(figsize=(10,6))
plt.imshow(I,cmap = 'gray')
current_axis = plt.gca()
for box in new_boxes:
color = colors[2]
#boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd
#z_score = (box.score - mean_score) / sd_score
#z_score = (boxes_area_score[key]['area'] )
### Escribe el z-score
#if z_score > 1:
current_axis.text((box.xmin + box.xmax)/2,
(box.ymin+ box.ymax)/2,
'%.2f' % box.classes[0], size='x-large',
color='white', bbox={'facecolor':color, 'alpha':1.0})
return new_boxes