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test.py
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test.py
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#! /usr/bin/env python
# coding=utf-8
# ================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : test.py
# Author : YunYang1994
# Created date: 2019-07-19 10:29:34
# Description :
#
# ================================================================
import cv2
import os
import shutil
import numpy as np
import tensorflow as tf
import core.utils as utils
from core.config import cfg
from core.yolov3 import YOLOv3, decode
INPUT_SIZE = 416
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
CLASSES = utils.read_class_names(cfg.YOLO.CLASSES)
predicted_dir_path = '../mAP/predicted'
ground_truth_dir_path = '../mAP/ground-truth'
if os.path.exists(predicted_dir_path):
shutil.rmtree(predicted_dir_path)
if os.path.exists(ground_truth_dir_path):
shutil.rmtree(ground_truth_dir_path)
if os.path.exists(cfg.TEST.DECTECTED_IMAGE_PATH):
shutil.rmtree(cfg.TEST.DECTECTED_IMAGE_PATH)
os.mkdir(predicted_dir_path)
os.mkdir(ground_truth_dir_path)
os.mkdir(cfg.TEST.DECTECTED_IMAGE_PATH)
# Build Model
input_layer = tf.keras.layers.Input([INPUT_SIZE, INPUT_SIZE, 3])
feature_maps = YOLOv3(input_layer)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, i)
bbox_tensors.append(tf.reshape(bbox_tensor, (-1, 5+NUM_CLASS)))
bbox_tensors = tf.concat(bbox_tensors, axis=0)
model = tf.keras.Model(input_layer, bbox_tensors)
model.load_weights("./yolov3")
with open(cfg.TEST.ANNOT_PATH, 'r') as annotation_file:
for num, line in enumerate(annotation_file):
annotation = line.strip().split()
image_path = annotation[0]
image_name = image_path.split('/')[-1]
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
bbox_data_gt = np.array([list(map(int, box.split(',')))
for box in annotation[1:]])
if len(bbox_data_gt) == 0:
bboxes_gt = []
classes_gt = []
else:
bboxes_gt, classes_gt = bbox_data_gt[:, :4], bbox_data_gt[:, 4]
ground_truth_path = os.path.join(
ground_truth_dir_path, str(num) + '.txt')
print('=> ground truth of %s:' % image_name)
num_bbox_gt = len(bboxes_gt)
with open(ground_truth_path, 'w') as f:
for i in range(num_bbox_gt):
class_name = CLASSES[classes_gt[i]]
xmin, ymin, xmax, ymax = list(map(str, bboxes_gt[i]))
bbox_mess = ' '.join(
[class_name, xmin, ymin, xmax, ymax]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())
print('=> predict result of %s:' % image_name)
predict_result_path = os.path.join(
predicted_dir_path, str(num) + '.txt')
# Predict Process
image_size = image.shape[:2]
image_data = utils.image_preporcess(
np.copy(image), [INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis, ...].astype(np.float32)
pred_bbox = model.predict(image_data)
bboxes = utils.postprocess_boxes(
pred_bbox, image_size, INPUT_SIZE, cfg.TEST.SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method='nms')
if cfg.TEST.DECTECTED_IMAGE_PATH is not None:
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH+image_name, image)
with open(predict_result_path, 'w') as f:
for bbox in bboxes:
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
class_name = CLASSES[class_ind]
score = '%.4f' % score
xmin, ymin, xmax, ymax = list(map(str, coor))
bbox_mess = ' '.join(
[class_name, score, xmin, ymin, xmax, ymax]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())