evaluate.py

from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import os
import shutil
import numpy as np
import tensorflow as tf
from core.yolov4 import filter_boxes
from tensorflow.python.saved_model import tag_constants
import core.utils as utils
from core.config import cfg

flags.DEFINE_string('weights', './checkpoints/yolov4-416',
                    'path to weights file')
flags.DEFINE_string('framework', 'tf', 'select model type in (tf, tflite, trt)'
                    'path to weights file')
flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')
flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_string('annotation_path', "./data/dataset/val2017.txt", 'annotation path')
flags.DEFINE_string('write_image_path', "./data/detection/", 'write image path')
flags.DEFINE_float('iou', 0.5, 'iou threshold')
flags.DEFINE_float('score', 0.35, 'score threshold')

def main(dic_analysis,dic_train):
    INPUT_SIZE = dic_train['size']
    STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(dic_train['model'],dic_train['classes'])
    CLASSES = dic_train['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
    saved_model_loaded = tf.saved_model.load(dic_analysis['weightpath'], tags=[tag_constants.SERVING])
    infer = saved_model_loaded.signatures['serving_default']

    num_lines = sum(1 for line in open(FLAGS.annotation_path)) # check the format of val
    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_preprocess(np.copy(image), [INPUT_SIZE, INPUT_SIZE])
            image_data = cv2.resize(np.copy(image), (INPUT_SIZE, INPUT_SIZE))
            image_data = image_data / 255.
            image_data = image_data[np.newaxis, ...].astype(np.float32)

            batch_data = tf.constant(image_data)
            pred_bbox = infer(batch_data)
            for key, value in pred_bbox.items():
                boxes = value[:, :, 0:4]
                pred_conf = value[:, :, 4:]

            boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
                boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
                scores=tf.reshape(
                    pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
                max_output_size_per_class=50,
                max_total_size=50,
                iou_threshold=FLAGS.iou,
                score_threshold=FLAGS.score
            )
            boxes, scores, classes, valid_detections = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]

            # if cfg.TEST.DECTECTED_IMAGE_PATH is not None:
            #     image_result = utils.draw_bbox(np.copy(image), [boxes, scores, classes, valid_detections])
            #     cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH + image_name, image_result)

            with open(predict_result_path, 'w') as f:
                image_h, image_w, _ = image.shape
                for i in range(valid_detections[0]):
                    if int(classes[0][i]) < 0 or int(classes[0][i]) > NUM_CLASS: continue
                    coor = boxes[0][i]
                    coor[0] = int(coor[0] * image_h)
                    coor[2] = int(coor[2] * image_h)
                    coor[1] = int(coor[1] * image_w)
                    coor[3] = int(coor[3] * image_w)

                    score = scores[0][i]
                    class_ind = int(classes[0][i])
                    class_name = CLASSES[class_ind]
                    score = '%.4f' % score
                    ymin, xmin, ymax, xmax = 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())
            print(num, num_lines)

if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass