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evaluate.py
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evaluate.py
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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', 0.25, 'score threshold')
def main(_argv):
INPUT_SIZE = FLAGS.size
STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
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
if FLAGS.framework == 'tflite':
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
else:
saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
num_lines = sum(1 for line in open(FLAGS.annotation_path))
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)
if FLAGS.framework == 'tflite':
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
if FLAGS.model == 'yolov4' and FLAGS.tiny == True:
boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25)
else:
boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25)
else:
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