116 lines
3.3 KiB
Python
116 lines
3.3 KiB
Python
from __future__ import division
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import torch
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import random
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import numpy as np
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import cv2
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def confidence_filter(result, confidence):
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conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
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result = result*conf_mask
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return result
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def confidence_filter_cls(result, confidence):
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max_scores = torch.max(result[:,:,5:25], 2)[0]
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res = torch.cat((result, max_scores),2)
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print(res.shape)
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cond_1 = (res[:,:,4] > confidence).float()
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cond_2 = (res[:,:,25] > 0.995).float()
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conf = cond_1 + cond_2
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conf = torch.clamp(conf, 0.0, 1.0)
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conf = conf.unsqueeze(2)
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result = result*conf
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return result
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def get_abs_coord(box):
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box[2], box[3] = abs(box[2]), abs(box[3])
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x1 = (box[0] - box[2]/2) - 1
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y1 = (box[1] - box[3]/2) - 1
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x2 = (box[0] + box[2]/2) - 1
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y2 = (box[1] + box[3]/2) - 1
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return x1, y1, x2, y2
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def sanity_fix(box):
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if (box[0] > box[2]):
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box[0], box[2] = box[2], box[0]
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if (box[1] > box[3]):
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box[1], box[3] = box[3], box[1]
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return box
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def bbox_iou(box1, box2):
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"""
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Returns the IoU of two bounding boxes
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"""
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#Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]
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#get the corrdinates of the intersection rectangle
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inter_rect_x1 = torch.max(b1_x1, b2_x1)
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inter_rect_y1 = torch.max(b1_y1, b2_y1)
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inter_rect_x2 = torch.min(b1_x2, b2_x2)
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inter_rect_y2 = torch.min(b1_y2, b2_y2)
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#Intersection area
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if torch.cuda.is_available():
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inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape).cuda())*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape).cuda())
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else:
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inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape))*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape))
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#Union Area
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b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
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b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
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iou = inter_area / (b1_area + b2_area - inter_area)
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return iou
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def pred_corner_coord(prediction):
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#Get indices of non-zero confidence bboxes
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ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
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box = prediction[ind_nz[0], ind_nz[1]]
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box_a = box.new(box.shape)
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box_a[:,0] = (box[:,0] - box[:,2]/2)
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box_a[:,1] = (box[:,1] - box[:,3]/2)
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box_a[:,2] = (box[:,0] + box[:,2]/2)
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box_a[:,3] = (box[:,1] + box[:,3]/2)
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box[:,:4] = box_a[:,:4]
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prediction[ind_nz[0], ind_nz[1]] = box
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return prediction
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def write(x, batches, results, colors, classes):
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c1 = tuple(x[1:3].int())
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c2 = tuple(x[3:5].int())
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img = results[int(x[0])]
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cls = int(x[-1])
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label = "{0}".format(classes[cls])
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color = random.choice(colors)
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cv2.rectangle(img, c1, c2,color, 1)
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t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
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c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
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cv2.rectangle(img, c1, c2,color, -1)
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cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
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return img
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