TensorBox/evaluate_little.py at master · Cuky88/TensorBox · GitHub

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# python evaluate.py --weights data/overfeat_rezoom/save.ckpt-150000 --test_boxes data/brainwash/val_boxes.json
# python evaluate.py --weights data/COD20K/save.ckpt-110000 --test_boxes data/COD20K/val_boxes.json
# python evaluate.py --weights data/BoxCars/save.ckpt-40000 --test_boxes data/BoxCars/val_boxes.json
# python evaluate.py --weights data/ownset/save.ckpt-40000 --test_boxes data/ownset/val_boxes.json


import tensorflow as tf
import os
import json
import subprocess
from scipy.misc import imread, imresize
from scipy import misc

from train import build_forward
from utils.annolist import AnnotationLib as al
from utils.train_utils import add_rectangles, rescale_boxes

import cv2
import argparse

def get_image_dir(args):
    weights_iteration = int(args.weights.split('-')[-1])
    expname = '_' + args.expname if args.expname else ''
    image_dir = '%s/images_%s_%d%s' % (os.path.dirname(args.weights), os.path.basename(args.test_boxes)[:-5], weights_iteration, expname)
    return image_dir

def get_results(args, H):
    tf.reset_default_graph()
    x_in = tf.placeholder(tf.float32, name='x_in', shape=[H['image_height'], H['image_width'], 3])
    if H['use_rezoom']:
        pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
        grid_area = H['grid_height'] * H['grid_width']
        pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * H['rnn_len'], 2])), [grid_area, H['rnn_len'], 2])
        if H['reregress']:
            pred_boxes = pred_boxes + pred_boxes_deltas
    else:
        pred_boxes, pred_logits, pred_confidences = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, args.weights)

        pred_annolist = al.AnnoList()

        sizes = [];
        true_annolist = al.parse(args.test_boxes)
        for i in range(len(true_annolist)):
            true_anno = true_annolist[i]
            for rect in true_anno.rects:
                size = (rect.x2 - rect.x1) * (rect.y2 - rect.y1)
                if (size > 0):
                    sizes.append(size)
                else:
                    print("Size was less than 0")

        sizes.sort()
        filterSize = sizes[int(0.1 * (len(sizes) - 1))]
        data_dir = os.path.dirname(args.test_boxes)
        image_dir = get_image_dir(args)
        subprocess.call('mkdir -p %s' % image_dir, shell=True)
        for i in range(len(true_annolist)):
            true_anno1 = true_annolist[i]
            j = 0
            while j < len(true_anno.rects):
                rect = true_anno.rects[j]
                size = (rect.x2 - rect.x1) * (rect.y2 - rect.y1)
                if size < filterSize:
                    del true_anno.rects[j]
                else:
                    j += 1
            orig_img = imread('%s/%s' % (data_dir, true_anno1.imageName))[:,:,:3]
            img = imresize(orig_img, (H["image_height"], H["image_width"]), interp='cubic')

            true_anno = rescale_boxes((orig_img.shape[0], orig_img.shape[1]), true_anno1, H["image_height"],
                                      H["image_width"])
            #for r in true_anno.rects:
            #    r.x1, r.y1, r.x2, r.y2 = int(r.x1), int(r.y1), int(r.x2), int(r.y2)
            #    new_img = cv2.rectangle(img, (r.x1, r.y1), (r.x2, r.y2), (255, 255, 255), 2)
            #imname = '%s/%s' % (image_dir + "_before", os.path.basename(true_anno.imageName))
            #misc.imsave(imname, new_img)

            feed = {x_in: img}
            (np_pred_boxes, np_pred_confidences) = sess.run([pred_boxes, pred_confidences], feed_dict=feed)
            pred_anno = al.Annotation()
            pred_anno.imageName = true_anno.imageName
            # TODO: try to fix order of x and y in add_rectangles?!
            new_img, rects = add_rectangles(H, [img], np_pred_confidences, np_pred_boxes,
                                            use_stitching=True, rnn_len=H['rnn_len'], min_conf=args.min_conf, tau=args.tau, show_suppressed=args.show_suppressed)

            for r in true_anno.rects:
                new_img=cv2.rectangle(new_img,(r.x1,r.y1),(r.x2, r.y2), (0,0,255), 2)

            #print(rects)
            for r in rects:
                if r.x1 > r.x2:
                    r.x1, r.x2 = r.x2, r.x1
                if r.y1 > r.y2:
                    r.y1, r.y2 = r.y2, r.y1
                #print("x1: %s - x2: %s - y1: %s - y2: %s"%(r.x1, r.x2, r.y1, r.y2))

            true_anno = true_anno1
            pred_anno.rects = rects
            pred_anno.imagePath = os.path.abspath(data_dir)
            pred_anno = rescale_boxes((H["image_height"], H["image_width"]), pred_anno, orig_img.shape[0], orig_img.shape[1])
            pred_annolist.append(pred_anno)

            imname = '%s/%s' % (image_dir, os.path.basename(true_anno.imageName))
            misc.imsave(imname, new_img)
            if i % 25 == 0:
                print(str(i) + " out of " + str(len(true_annolist)))
    return pred_annolist, true_annolist

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', required=True)
    parser.add_argument('--expname', default='')
    parser.add_argument('--test_boxes', required=True)
    parser.add_argument('--gpu', default=0)
    parser.add_argument('--logdir', default='output')
    parser.add_argument('--iou_threshold', default=0.5, type=float)
    parser.add_argument('--tau', default=0.25, type=float)
    parser.add_argument('--min_conf', default=0.2, type=float)
    parser.add_argument('--show_suppressed', default=False, type=bool)
    args = parser.parse_args()
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    hypes_file = 'hypes/%s' % args.weights.split("/")[1]+".json"
    with open(hypes_file, 'r') as f:
        H = json.load(f)
    expname = args.expname + '_' if args.expname else ''
    pred_boxes = '%s.%s%s' % (args.weights, expname, os.path.basename(args.test_boxes))
    true_boxes = '%s.gt_%s%s' % (args.weights, expname, os.path.basename(args.test_boxes))


    pred_annolist, true_annolist = get_results(args, H)
    pred_annolist.save(pred_boxes)
    true_annolist.save(true_boxes)

    try:
        rpc_cmd = 'cd utils/annolist/; ./doRPC.py --minOverlap %f %s %s; cd ../../' % (args.iou_threshold, '../../'+true_boxes, '../../'+pred_boxes)
        print('$ %s' % rpc_cmd)
        rpc_output = subprocess.check_output(rpc_cmd, shell=True)
        print(rpc_output)
        txt_file = [line for line in rpc_output.split('\n') if line.strip()][-1]
        output_png = '%s/results.png' % get_image_dir(args)
        plot_cmd = 'cd utils/annolist/;./plotSimple.py %s --output %s; cd ../../' % (txt_file, '../../'+output_png)
        print('$ %s' % plot_cmd)
        plot_output = subprocess.check_output(plot_cmd, shell=True)
        print('output results at: %s' % plot_output)
    except Exception as e:
        print(e)

if __name__ == '__main__':
    main()