utils.py

import sys
import os
import time
import math
import shutil
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from torch.autograd import Variable

import struct # get_image_size
import imghdr # get_image_size


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0
 
    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def save_checkpoint(state, is_best, directory, dataset, clip_duration):
    torch.save(state, '%s/%s_checkpoint.pth' % (directory, 'yowo_' + dataset + '_' + str(clip_duration) + 'f'))
    if is_best:
        shutil.copyfile('%s/%s_checkpoint.pth' % (directory, 'yowo_' + dataset + '_' + str(clip_duration) + 'f'),
                        '%s/%s_best.pth' % (directory, 'yowo_' + dataset + '_' + str(clip_duration) + 'f'))



def sigmoid(x):
    return 1.0/(math.exp(-x)+1.)

def softmax(x):
    x = torch.exp(x - torch.max(x))
    x = x/x.sum()
    return x


def bbox_iou(box1, box2, x1y1x2y2=True):
    if x1y1x2y2:
        mx = min(box1[0], box2[0])
        Mx = max(box1[2], box2[2])
        my = min(box1[1], box2[1])
        My = max(box1[3], box2[3])
        w1 = box1[2] - box1[0]
        h1 = box1[3] - box1[1]
        w2 = box2[2] - box2[0]
        h2 = box2[3] - box2[1]
    else:
        mx = min(float(box1[0]-box1[2]/2.0), float(box2[0]-box2[2]/2.0))
        Mx = max(float(box1[0]+box1[2]/2.0), float(box2[0]+box2[2]/2.0))
        my = min(float(box1[1]-box1[3]/2.0), float(box2[1]-box2[3]/2.0))
        My = max(float(box1[1]+box1[3]/2.0), float(box2[1]+box2[3]/2.0))
        w1 = box1[2]
        h1 = box1[3]
        w2 = box2[2]
        h2 = box2[3]
    uw = Mx - mx
    uh = My - my
    cw = w1 + w2 - uw
    ch = h1 + h2 - uh
    carea = 0
    if cw <= 0 or ch <= 0:
        return 0.0

    area1 = w1 * h1
    area2 = w2 * h2
    carea = cw * ch
    uarea = area1 + area2 - carea
    return carea/uarea

# in order to calculate video-mAP, iou-values need to be added together separately
def video_iou(box1, box2, x1y1x2y2=True):
    if x1y1x2y2:
        mx = min(box1[0], box2[0])
        Mx = max(box1[2], box2[2])
        my = min(box1[1], box2[1])
        My = max(box1[3], box2[3])
        w1 = box1[2] - box1[0]
        h1 = box1[3] - box1[1]
        w2 = box2[2] - box2[0]
        h2 = box2[3] - box2[1]
    else:
        mx = min(float(box1[0]-box1[2]/2.0), float(box2[0]-box2[2]/2.0))
        Mx = max(float(box1[0]+box1[2]/2.0), float(box2[0]+box2[2]/2.0))
        my = min(float(box1[1]-box1[3]/2.0), float(box2[1]-box2[3]/2.0))
        My = max(float(box1[1]+box1[3]/2.0), float(box2[1]+box2[3]/2.0))
        w1 = box1[2]
        h1 = box1[3]
        w2 = box2[2]
        h2 = box2[3]
    uw = Mx - mx
    uh = My - my
    cw = w1 + w2 - uw
    ch = h1 + h2 - uh
    carea = 0
    if cw <= 0 or ch <= 0:
        return 0.0

    area1 = w1 * h1
    area2 = w2 * h2
    carea = cw * ch
    uarea = area1 + area2 - carea
    return carea, uarea

def bbox_ious(boxes1, boxes2, x1y1x2y2=True):
    if x1y1x2y2:
        mx = torch.min(boxes1[0], boxes2[0])
        Mx = torch.max(boxes1[2], boxes2[2])
        my = torch.min(boxes1[1], boxes2[1])
        My = torch.max(boxes1[3], boxes2[3])
        w1 = boxes1[2] - boxes1[0]
        h1 = boxes1[3] - boxes1[1]
        w2 = boxes2[2] - boxes2[0]
        h2 = boxes2[3] - boxes2[1]
    else:
        mx = torch.min(boxes1[0]-boxes1[2]/2.0, boxes2[0]-boxes2[2]/2.0)
        Mx = torch.max(boxes1[0]+boxes1[2]/2.0, boxes2[0]+boxes2[2]/2.0)
        my = torch.min(boxes1[1]-boxes1[3]/2.0, boxes2[1]-boxes2[3]/2.0)
        My = torch.max(boxes1[1]+boxes1[3]/2.0, boxes2[1]+boxes2[3]/2.0)
        w1 = boxes1[2]
        h1 = boxes1[3]
        w2 = boxes2[2]
        h2 = boxes2[3]
    uw = Mx - mx
    uh = My - my
    cw = w1 + w2 - uw
    ch = h1 + h2 - uh
    mask = ((cw <= 0) + (ch <= 0) > 0)
    area1 = w1 * h1
    area2 = w2 * h2
    carea = cw * ch
    carea[mask] = 0
    uarea = area1 + area2 - carea
    return carea/uarea

def nms(boxes, nms_thresh):
    if len(boxes) == 0:
        return boxes

    det_confs = torch.zeros(len(boxes))
    for i in range(len(boxes)):
        det_confs[i] = 1-boxes[i][4]                

    _,sortIds = torch.sort(det_confs)
    out_boxes = []
    for i in range(len(boxes)):
        box_i = boxes[sortIds[i]]
        if box_i[4] > 0:
            out_boxes.append(box_i)
            for j in range(i+1, len(boxes)):
                box_j = boxes[sortIds[j]]
                if bbox_iou(box_i, box_j, x1y1x2y2=False) > nms_thresh:
                    #print(box_i, box_j, bbox_iou(box_i, box_j, x1y1x2y2=False))
                    box_j[4] = 0
    return out_boxes


def area2d(b):
    return (b[:,2]-b[:,0]+1)*(b[:,3]-b[:,1]+1)

def overlap2d(b1, b2):
    xmin = np.maximum( b1[:,0], b2[:,0] )
    xmax = np.minimum( b1[:,2]+1, b2[:,2]+1)
    width = np.maximum(0, xmax-xmin)
    ymin = np.maximum( b1[:,1], b2[:,1] )
    ymax = np.minimum( b1[:,3]+1, b2[:,3]+1)
    height = np.maximum(0, ymax-ymin)   
    return width*height

def iou3d(b1, b2):
    assert b1.shape[0] == b2.shape[0]
    assert np.all(b1[:,0] == b2[:,0])
    o = overlap2d(b1[:,1:5],b2[:,1:5])
    return np.mean( o/(area2d(b1[:,1:5])+area2d(b2[:,1:5])-o) )

def iou3dt(b1, b2):
    tmin = max(b1[0,0], b2[0,0])
    tmax = min(b1[-1,0], b2[-1,0])
    if tmax <= tmin: return 0.0    
    temporal_inter = tmax-tmin+1
    temporal_union = max(b1[-1,0], b2[-1,0]) - min(b1[0,0], b2[0,0]) + 1 
    return iou3d( b1[np.where(b1[:,0]==tmin)[0][0]:np.where(b1[:,0]==tmax)[0][0]+1,:] , b2[np.where(b2[:,0]==tmin)[0][0]:np.where(b2[:,0]==tmax)[0][0]+1,:]  ) * temporal_inter / temporal_union

def nms_3d(detections, overlap=0.5):
    # detections: [(tube1, score1), (tube2, score2)]
    if len(detections) == 0:
        return np.array([], dtype=np.int32)
    I = np.argsort([d[1] for d in detections])
    indices = np.zeros(I.size, dtype=np.int32)
    counter = 0
    while I.size>0:
        i = I[-1]
        indices[counter] = i
        counter += 1
        ious = np.array([ iou3dt(detections[ii][0],detections[i][0]) for ii in I[:-1] ])
        I  = I[np.where(ious<=overlap)[0]]
    return indices[:counter]

def voc_ap(pr, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    rec, prec = pr[:,1], pr[:,0]
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [0.]))

        # compute the precision envelope
        for i in range(mpre.size - 1, 0, -1):
            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

        # to calculate area under PR curve, look for points
        # where X axis (recall) changes value
        i = np.where(mrec[1:] != mrec[:-1])[0]

        # and sum (\Delta recall) * prec
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap


def convert2cpu(gpu_matrix):
    return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix)

def convert2cpu_long(gpu_matrix):
    return torch.LongTensor(gpu_matrix.size()).copy_(gpu_matrix)

def get_region_boxes(output, conf_thresh, num_classes, anchors, num_anchors, only_objectness=1, validation=False):
    anchor_step = len(anchors)//num_anchors
    if output.dim() == 3:
        output = output.unsqueeze(0)
    batch = output.size(0)
    assert(output.size(1) == (5+num_classes)*num_anchors)
    h = output.size(2)
    w = output.size(3)

    t0 = time.time()
    all_boxes = []
    output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w)

    grid_x = torch.linspace(0, w-1, w).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).cuda()
    grid_y = torch.linspace(0, h-1, h).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).cuda()
    xs = torch.sigmoid(output[0]) + grid_x
    ys = torch.sigmoid(output[1]) + grid_y

    anchor_w = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([0]))
    anchor_h = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([1]))
    anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).cuda()
    anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).cuda()
    ws = torch.exp(output[2]) * anchor_w
    hs = torch.exp(output[3]) * anchor_h

    det_confs = torch.sigmoid(output[4])

    cls_confs = torch.nn.Softmax()(Variable(output[5:5+num_classes].transpose(0,1))).data
    cls_max_confs, cls_max_ids = torch.max(cls_confs, 1)
    cls_max_confs = cls_max_confs.view(-1)
    cls_max_ids = cls_max_ids.view(-1)
    t1 = time.time()
    
    sz_hw = h*w
    sz_hwa = sz_hw*num_anchors
    det_confs = convert2cpu(det_confs)
    cls_max_confs = convert2cpu(cls_max_confs)
    cls_max_ids = convert2cpu_long(cls_max_ids)
    xs = convert2cpu(xs)
    ys = convert2cpu(ys)
    ws = convert2cpu(ws)
    hs = convert2cpu(hs)
    if validation:
        cls_confs = convert2cpu(cls_confs.view(-1, num_classes))
    t2 = time.time()
    for b in range(batch):
        boxes = []
        for cy in range(h):
            for cx in range(w):
                for i in range(num_anchors):
                    ind = b*sz_hwa + i*sz_hw + cy*w + cx
                    det_conf =  det_confs[ind]
                    if only_objectness:
                        conf =  det_confs[ind]
                    else:
                        conf = det_confs[ind] * cls_max_confs[ind]
    
                    if conf > conf_thresh:
                        bcx = xs[ind]
                        bcy = ys[ind]
                        bw = ws[ind]
                        bh = hs[ind]
                        cls_max_conf = cls_max_confs[ind]
                        cls_max_id = cls_max_ids[ind]
                        box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id]
                        if (not only_objectness) and validation:
                            for c in range(num_classes):
                                tmp_conf = cls_confs[ind][c]
                                if c != cls_max_id and det_confs[ind]*tmp_conf > conf_thresh:
                                    box.append(tmp_conf)
                                    box.append(c)
                        boxes.append(box)
        all_boxes.append(boxes)
    t3 = time.time()
    if False:
        print('---------------------------------')
        print('matrix computation : %f' % (t1-t0))
        print('        gpu to cpu : %f' % (t2-t1))
        print('      boxes filter : %f' % (t3-t2))
        print('---------------------------------')
    return all_boxes

def get_region_boxes_video(output, conf_thresh, num_classes, anchors, num_anchors, only_objectness=1, validation=False):
    anchor_step = len(anchors)//num_anchors
    if output.dim() == 3:
        output = output.unsqueeze(0)
    batch = output.size(0)
    assert(output.size(1) == (5+num_classes)*num_anchors)
    h = output.size(2)
    w = output.size(3)

    t0 = time.time()
    all_boxes = []
    output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w)

    grid_x = torch.linspace(0, w-1, w).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).cuda()
    grid_y = torch.linspace(0, h-1, h).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).cuda()
    xs = torch.sigmoid(output[0]) + grid_x
    ys = torch.sigmoid(output[1]) + grid_y

    anchor_w = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([0]))
    anchor_h = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([1]))
    anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).cuda()
    anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).cuda()
    ws = torch.exp(output[2]) * anchor_w
    hs = torch.exp(output[3]) * anchor_h

    det_confs = torch.sigmoid(output[4])

    cls_confs = torch.nn.Softmax()(Variable(output[5:5+num_classes].transpose(0,1))).data
    cls_max_confs, cls_max_ids = torch.max(cls_confs, 1)
    cls_max_confs = cls_max_confs.view(-1)
    cls_max_ids = cls_max_ids.view(-1)
    t1 = time.time()
    
    sz_hw = h*w
    sz_hwa = sz_hw*num_anchors
    det_confs = convert2cpu(det_confs)
    cls_max_confs = convert2cpu(cls_max_confs)
    cls_max_ids = convert2cpu_long(cls_max_ids)
    xs = convert2cpu(xs)
    ys = convert2cpu(ys)
    ws = convert2cpu(ws)
    hs = convert2cpu(hs)
    if validation:
        cls_confs = convert2cpu(cls_confs.view(-1, num_classes))
    t2 = time.time()
    for b in range(batch):
        boxes = []
        for cy in range(h):
            for cx in range(w):
                for i in range(num_anchors):
                    ind = b*sz_hwa + i*sz_hw + cy*w + cx
                    det_conf =  det_confs[ind]
                    if only_objectness:
                        conf =  det_confs[ind]
                    else:
                        conf = det_confs[ind] * cls_max_confs[ind]
    
                    if conf > conf_thresh:
                        bcx = xs[ind]
                        bcy = ys[ind]
                        bw = ws[ind]
                        bh = hs[ind]
                        cls_max_conf = cls_max_confs[ind]
                        cls_max_id = cls_max_ids[ind]
                        box = [bcx/w, bcy/h, bw/w, bh/h, det_conf]
                        if (not only_objectness) and validation:
                            for c in range(num_classes):
                                tmp_conf = cls_confs[ind][c]
                                box.append(tmp_conf)
                                box.append(c)

                        # box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id]
                        # if (not only_objectness) and validation:
                        #     for c in range(num_classes):
                        #         tmp_conf = cls_confs[ind][c]
                        #         if c != cls_max_id and det_confs[ind]*tmp_conf > conf_thresh:
                        #             box.append(tmp_conf)
                        #             box.append(c)
                        boxes.append(box)
        all_boxes.append(boxes)
    t3 = time.time()
    if False:
        print('---------------------------------')
        print('matrix computation : %f' % (t1-t0))
        print('        gpu to cpu : %f' % (t2-t1))
        print('      boxes filter : %f' % (t3-t2))
        print('---------------------------------')
    return all_boxes




def plot_boxes_cv2(img, boxes, savename=None, class_names=None, color=None):
    import cv2
    colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]]);
    def get_color(c, x, max_val):
        ratio = float(x)/max_val * 5
        i = int(math.floor(ratio))
        j = int(math.ceil(ratio))
        ratio = ratio - i
        r = (1-ratio) * colors[i][c] + ratio*colors[j][c]
        return int(r*255)

    width = img.shape[1]
    height = img.shape[0]
    for i in range(len(boxes)):
        box = boxes[i]
        x1 = int(round((box[0] - box[2]/2.0) * width))
        y1 = int(round((box[1] - box[3]/2.0) * height))
        x2 = int(round((box[0] + box[2]/2.0) * width))
        y2 = int(round((box[1] + box[3]/2.0) * height))

        if color:
            rgb = color
        else:
            rgb = (255, 0, 0)
        if len(box) >= 7 and class_names:
            cls_conf = box[5]
            cls_id = box[6]
            print('%s: %f' % (class_names[cls_id], cls_conf))
            classes = len(class_names)
            offset = cls_id * 123457 % classes
            red   = get_color(2, offset, classes)
            green = get_color(1, offset, classes)
            blue  = get_color(0, offset, classes)
            if color is None:
                rgb = (red, green, blue)
            img = cv2.putText(img, class_names[cls_id], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 1.2, rgb, 1)
        img = cv2.rectangle(img, (x1,y1), (x2,y2), rgb, 1)
    if savename:
        print("save plot results to %s" % savename)
        cv2.imwrite(savename, img)
    return img


# For showing results
def plot_boxes(img, boxes, savename=None, class_names=None):
    colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]]);
    def get_color(c, x, max_val):
        ratio = float(x)/max_val * 5
        i = int(math.floor(ratio))
        j = int(math.ceil(ratio))
        ratio = ratio - i
        r = (1-ratio) * colors[i][c] + ratio*colors[j][c]
        return int(r*255)

    width = img.width
    height = img.height
    draw = ImageDraw.Draw(img)
    for i in range(len(boxes)):
        box = boxes[i]
        x1 = max(0.0, (box[0] - box[2]/2.0) * width)
        y1 = max(0.0, (box[1] - box[3]/2.0) * height)
        x2 = min(width, (box[0] + box[2]/2.0) * width)
        y2 = min(height, (box[1] + box[3]/2.0) * height)

        rgb = (255, 0, 0)
        if len(box) >= 7 and class_names:
            cls_conf = box[5]
            cls_id = box[6]
            print('%s: %f' % (class_names[cls_id], cls_conf))
            classes = len(class_names)
            # offset = cls_id * 123457 % classes
            # red   = get_color(2, offset, classes)
            # green = get_color(1, offset, classes)
            # blue  = get_color(0, offset, classes)
            rgb = (0, 255, 0)
            font = ImageFont.truetype('/usr/home/sut/yolo_jhmdb/font/OpenSans-Bold.ttf', 24)
            draw.text((x1 + 10, y1 + 5.0), class_names[cls_id], fill=rgb)
            draw.text((x1 + 10, y2 - 20.0), str(np.around(cls_conf.numpy(), decimals=3)),fill = rgb)
        draw.rectangle([x1, y1, x2, y2], outline = rgb, width=5)

    img_out = img.resize((448, 448), Image.ANTIALIAS)
    if savename:
        print("save plot results to %s" % savename)
        img_out.save(savename)
    return img

def plot_boxes_tensor(img, boxes, savename=None, class_names=None):
    img_temp = img.numpy().transpose((3, 4, 1, 2, 0))
    im = Image.fromarray(np.uint8(img_temp[:, :, :, 4, 0] * 255), 'RGB')
    plot_boxes(im, boxes, savename, class_names)

def read_truths(lab_path):
    if not os.path.exists(lab_path):
        return np.array([])
    if os.path.getsize(lab_path):
        truths = np.loadtxt(lab_path)
        truths = truths.reshape(truths.size//5, 5) # to avoid single truth problem
        return truths
    else:
        return np.array([])

def read_truths_args(lab_path, min_box_scale):
    truths = read_truths(lab_path)
    new_truths = []
    for i in range(truths.shape[0]):
        cx = (truths[i][1] + truths[i][3]) / (2 * 320)
        cy = (truths[i][2] + truths[i][4]) / (2 * 240)
        imgw = (truths[i][3] - truths[i][1]) / 320
        imgh = (truths[i][4] - truths[i][2]) / 240
        truths[i][0] = truths[i][0] - 1
        truths[i][1] = cx
        truths[i][2] = cy
        truths[i][3] = imgw
        truths[i][4] = imgh
        
        if truths[i][3] < min_box_scale:
            continue
        new_truths.append([truths[i][0], truths[i][1], truths[i][2], truths[i][3], truths[i][4]])
    return np.array(new_truths)

def load_class_names(namesfile):
    class_names = []
    with open(namesfile, 'r') as fp:
        lines = fp.readlines()
    for line in lines:
        line = line.rstrip()
        class_names.append(line)
    return class_names

def image2torch(img):
    width = img.width
    height = img.height
    img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes()))
    img = img.view(height, width, 3).transpose(0,1).transpose(0,2).contiguous()
    img = img.view(1, 3, height, width)
    img = img.float().div(255.0)
    return img

def do_detect(model, img, conf_thresh, nms_thresh, use_cuda=1):
    model.eval()
    t0 = time.time()

    if isinstance(img, Image.Image):
        width = img.width
        height = img.height
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes()))
        img = img.view(height, width, 3).transpose(0,1).transpose(0,2).contiguous()
        img = img.view(1, 3, height, width)
        img = img.float().div(255.0)
    elif type(img) == np.ndarray: # cv2 image
        img = torch.from_numpy(img.transpose(2,0,1)).float().div(255.0).unsqueeze(0)
    else:
        print("unknow image type")
        exit(-1)

    t1 = time.time()

    if use_cuda:
        img = img.cuda()
    img = torch.autograd.Variable(img)
    t2 = time.time()

    output = model(img)
    output = output.data
    #for j in range(100):
    #    sys.stdout.write('%f ' % (output.storage()[j]))
    #print('')
    t3 = time.time()

    boxes = get_region_boxes(output, conf_thresh, model.num_classes, model.anchors, model.num_anchors)[0]
    #for j in range(len(boxes)):
    #    print(boxes[j])
    t4 = time.time()

    boxes = nms(boxes, nms_thresh)
    t5 = time.time()

    if False:
        print('-----------------------------------')
        print(' image to tensor : %f' % (t1 - t0))
        print('  tensor to cuda : %f' % (t2 - t1))
        print('         predict : %f' % (t3 - t2))
        print('get_region_boxes : %f' % (t4 - t3))
        print('             nms : %f' % (t5 - t4))
        print('           total : %f' % (t5 - t0))
        print('-----------------------------------')
    return boxes

def read_data_cfg(datacfg):
    options = dict()
    options['gpus'] = '0,1,2,3'
    options['num_workers'] = '10'
    with open(datacfg, 'r') as fp:
        lines = fp.readlines()

    for line in lines:
        line = line.strip()
        if line == '':
            continue
        key,value = line.split('=')
        key = key.strip()
        value = value.strip()
        options[key] = value
    return options

def scale_bboxes(bboxes, width, height):
    import copy
    dets = copy.deepcopy(bboxes)
    for i in range(len(dets)):
        dets[i][0] = dets[i][0] * width
        dets[i][1] = dets[i][1] * height
        dets[i][2] = dets[i][2] * width
        dets[i][3] = dets[i][3] * height
    return dets
      
def file_lines(thefilepath):
    count = 0
    thefile = open(thefilepath, 'rb')
    while True:
        buffer = thefile.read(8192*1024).decode()
        if not buffer:
            break
        count += buffer.count('\n')
    thefile.close( )
    return count

def get_image_size(fname):
    '''Determine the image type of fhandle and return its size.
    from draco'''
    with open(fname, 'rb') as fhandle:
        head = fhandle.read(24)
        if len(head) != 24: 
            return
        if imghdr.what(fname) == 'png':
            check = struct.unpack('>i', head[4:8])[0]
            if check != 0x0d0a1a0a:
                return
            width, height = struct.unpack('>ii', head[16:24])
        elif imghdr.what(fname) == 'gif':
            width, height = struct.unpack('<HH', head[6:10])
        elif imghdr.what(fname) == 'jpeg' or imghdr.what(fname) == 'jpg':
            try:
                fhandle.seek(0) # Read 0xff next
                size = 2 
                ftype = 0 
                while not 0xc0 <= ftype <= 0xcf:
                    fhandle.seek(size, 1)
                    byte = fhandle.read(1)
                    while ord(byte) == 0xff:
                        byte = fhandle.read(1)
                    ftype = ord(byte)
                    size = struct.unpack('>H', fhandle.read(2))[0] - 2 
                # We are at a SOFn block
                fhandle.seek(1, 1)  # Skip `precision' byte.
                height, width = struct.unpack('>HH', fhandle.read(4))
            except Exception: #IGNORE:W0703
                return
        else:
            return
        return width, height

def logging(message):
    print('%s %s' % (time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), message))

def load_value_file(file_path):
    with open(file_path, 'r') as input_file:
        value = float(input_file.read().rstrip('\n\r'))

    return value