115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 17 13:55:42 2020
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@author: dlsaavedra
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"""
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import numpy as np
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def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
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new_boxes = []
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for num, box in enumerate(boxes):
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xmin = box.xmin + merge
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xmax = box.xmax - merge
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ymin = box.ymin + merge
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ymax = box.ymax - merge
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if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
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area = (ymax - ymin)*(xmax - xmin)
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z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
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if area > area_min:
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box.z_score = z_score
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new_boxes.append(box)
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#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
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mean_score = np.mean([box.z_score for box in new_boxes])
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sd_score = np.std([box.z_score for box in new_boxes])
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new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
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for box in new_boxes:
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z_score = (box.z_score - mean_score)/sd_score
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box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
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box.score = -1
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return new_boxes
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def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
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new_boxes = []
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for num, box in enumerate(boxes):
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xmin = box.xmin + merge
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xmax = box.xmax - merge
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ymin = box.ymin + merge
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ymax = box.ymax - merge
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if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
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area = (ymax - ymin)*(xmax - xmin)
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z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
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if area > area_min:
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box.z_score = z_score
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new_boxes.append(box)
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#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
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mean_score = np.mean([box.z_score for box in new_boxes])
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sd_score = np.std([box.z_score for box in new_boxes])
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normal_score = ([box.z_score for box in new_boxes] - mean_score)/sd_score
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# plt.figure()
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# _ = plt.hist(normal_score, bins='auto') # arguments are passed to np.histogram
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# plt.title("Histogram with 'auto' bins")
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# plt.show()
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#
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# plt.figure()
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# mean = np.mean([boxes_area_score[i]['area'] for i in boxes_area_score])
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# sd = np.std([boxes_area_score[i]['area'] for i in boxes_area_score])
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# normal = ([boxes_area_score[i]['area'] for i in boxes_area_score] - mean)/sd
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# _ = plt.hist(normal, bins='auto') # arguments are passed to np.histogram
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# plt.title("Histogram with 'auto' bins")
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# plt.show()
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new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
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for box in new_boxes:
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z_score = (box.z_score - mean_score)/sd_score
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box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
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colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()
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plt.figure(figsize=(10,6))
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plt.imshow(I,cmap = 'gray')
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current_axis = plt.gca()
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for box in new_boxes:
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color = colors[2]
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#boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd
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#z_score = (box.score - mean_score) / sd_score
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#z_score = (boxes_area_score[key]['area'] )
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### Escribe el z-score
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#if z_score > 1:
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current_axis.text((box.xmin + box.xmax)/2,
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(box.ymin+ box.ymax)/2,
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'%.2f' % box.classes[0], size='x-large',
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color='white', bbox={'facecolor':color, 'alpha':1.0})
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return new_boxes
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