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utils.py
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utils.py
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import cv2
import os
import pdb
import torch
import torch.nn as nn
import numpy as np
import sys
import itertools
def labels_to_text(labels,alphabet):
ret = []
labels = list(labels)
labels = [k for k, g in itertools.groupby(labels)]
for c in labels:
if c ==0:# len(alphabet): # CTC Blank
ret.append("")
else:
ret.append(alphabet[c-1])
ret = "".join(ret)
return ret
def get_transcript_txt(csv_file):
csv_file = open(csv_file,'r')
reader = csv.reader(csv_file,delimiter=',')
gt_files = {}
for annot in reader:
gt_txt_file = annot[0].split('.')[0]+'.txt'
if gt_files.get(gt_txt_file) is not None:
gt_files[gt_txt_file].append(annot[-1])
else: gt_files[gt_txt_file]=[annot[-1]]
for k,v in gt_files.iteritems():
f=open(k,'w')
f.write(" ".join(v))
f.close()
def choose_optimal_anchor_ratios(boxes_gt_file):
f = open(boxes_gt_file,'r')
flines = f.readlines()
all_wh=np.zeros((len(flines),2))
for line_idx in range(len(flines)):
line = flines[line_idx]
vals = line.split(',')
x0 =int(vals[1])
x1 =int(vals[3])
y0 =int(vals[2])
y1 =int(vals[4])
all_wh[line_idx,0]=x1-x0
all_wh[line_idx,1]=y1-y0
kmeans = KMeans(n_clusters=n_centers, random_state=0).fit(all_wh)
ratios=kmeans.cluster_centers_[:,1]/kmeans.cluster_centers_[:,0]
print ("Optimal",n_centers,"ratios",ratios)
def view_feature(pooled_feature,probs_sizes,feat_id):
max_features_vis=min(pooled_feature.shape[1],10)
for i in range(max_features_vis):#range(pooled_feature.shape[1]):
img = pooled_feature[0,i].detach().cpu()
img = np.array(255*img)
img[img<0] = 0
img[img>255] = 255
#img = img.transpose(1,2,0)
#img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)
img = img[:,:probs_sizes]
if not os.path.exists('visualized_feats'):
os.mkdir('visualized_feats')
cv2.imwrite('visualized_feats/feat_id'+str(feat_id)+'_'+str(i)+'.jpg',img)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def calc_iou(a, b):
area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])
iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])
ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1])
iw = torch.clamp(iw, min=0)
ih = torch.clamp(ih, min=0)
ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih
ua = torch.clamp(ua, min=1e-8)
intersection = iw * ih
IoU = intersection / ua
return IoU
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class BBoxTransform(nn.Module):
def __init__(self, mean=None, std=None):
super(BBoxTransform, self).__init__()
if mean is None:
self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32)).cuda()
else:
self.mean = mean
if std is None:
self.std = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32)).cuda()
else:
self.std = std
def forward(self, boxes, deltas):
widths = boxes[:, :, 2] - boxes[:, :, 0]
heights = boxes[:, :, 3] - boxes[:, :, 1]
ctr_x = boxes[:, :, 0] + 0.5 * widths
ctr_y = boxes[:, :, 1] + 0.5 * heights
dx = deltas[:, :, 0] * self.std[0] + self.mean[0]
dy = deltas[:, :, 1] * self.std[1] + self.mean[1]
dw = deltas[:, :, 2] * self.std[2] + self.mean[2]
dh = deltas[:, :, 3] * self.std[3] + self.mean[3]
pred_ctr_x = ctr_x + dx * widths
pred_ctr_y = ctr_y + dy * heights
pred_w = torch.exp(dw) * widths
pred_h = torch.exp(dh) * heights
pred_boxes_x1 = pred_ctr_x - 0.5 * pred_w
pred_boxes_y1 = pred_ctr_y - 0.5 * pred_h
pred_boxes_x2 = pred_ctr_x + 0.5 * pred_w
pred_boxes_y2 = pred_ctr_y + 0.5 * pred_h
pred_boxes = torch.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], dim=2)
return pred_boxes
class ClipBoxes(nn.Module):
def __init__(self, width=None, height=None):
super(ClipBoxes, self).__init__()
def forward(self, boxes, img):
batch_size, num_channels, height, width = img.shape
boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0)
boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0)
boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width)
boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height)
return boxes