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evaluate.py
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evaluate.py
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#! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2018 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : evaluate.py
# Author : YunYang1994
# Created date: 2018-12-20 11:58:21
# Description : compute mAP
#
#================================================================
import sys
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from PIL import Image
from core import utils, yolov3
from core.dataset import dataset, Parser
sess = tf.Session()
IMAGE_H, IMAGE_W = 416, 416
CLASSES = utils.read_coco_names('./data/raccoon.names')
NUM_CLASSES = len(CLASSES)
ANCHORS = utils.get_anchors('./data/raccoon_anchors.txt', IMAGE_H, IMAGE_W)
CKPT_FILE = "./checkpoint/yolov3.ckpt-2500"
IOU_THRESH = 0.5
SCORE_THRESH = 0.3
all_detections = []
all_annotations = []
all_aver_precs = {CLASSES[i]:0. for i in range(NUM_CLASSES)}
test_tfrecord = "./raccoon_dataset/raccoon_*.tfrecords"
parser = Parser(IMAGE_H, IMAGE_W, ANCHORS, NUM_CLASSES)
testset = dataset(parser, test_tfrecord , batch_size=1, shuffle=None, repeat=False)
images_tensor, *y_true_tensor = testset.get_next()
model = yolov3.yolov3(NUM_CLASSES, ANCHORS)
with tf.variable_scope('yolov3'):
pred_feature_map = model.forward(images_tensor, is_training=False)
y_pred_tensor = model.predict(pred_feature_map)
saver = tf.train.Saver()
saver.restore(sess, CKPT_FILE)
try:
image_idx = 0
while True:
y_pred, y_true, image = sess.run([y_pred_tensor, y_true_tensor, images_tensor])
pred_boxes = y_pred[0][0]
pred_confs = y_pred[1][0]
pred_probs = y_pred[2][0]
image = Image.fromarray(np.uint8(image[0]*255))
true_labels_list, true_boxes_list = [], []
for i in range(3):
true_probs_temp = y_true[i][..., 5: ]
true_boxes_temp = y_true[i][..., 0:4]
object_mask = true_probs_temp.sum(axis=-1) > 0
true_probs_temp = true_probs_temp[object_mask]
true_boxes_temp = true_boxes_temp[object_mask]
true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()
true_boxes_list += true_boxes_temp.tolist()
pred_boxes, pred_scores, pred_labels = utils.cpu_nms(pred_boxes, pred_confs*pred_probs, NUM_CLASSES,
score_thresh=SCORE_THRESH, iou_thresh=IOU_THRESH)
# image = utils.draw_boxes(image, pred_boxes, pred_scores, pred_labels, CLASSES, [IMAGE_H, IMAGE_W], show=True)
true_boxes = np.array(true_boxes_list)
box_centers, box_sizes = true_boxes[:,0:2], true_boxes[:,2:4]
true_boxes[:,0:2] = box_centers - box_sizes / 2.
true_boxes[:,2:4] = true_boxes[:,0:2] + box_sizes
pred_labels_list = [] if pred_labels is None else pred_labels.tolist()
all_detections.append( [pred_boxes, pred_scores, pred_labels_list])
all_annotations.append([true_boxes, true_labels_list])
image_idx += 1
if image_idx % 100 == 0:
sys.stdout.write(".")
sys.stdout.flush()
except tf.errors.OutOfRangeError:
pass
for idx in range(NUM_CLASSES):
true_positives = []
scores = []
num_annotations = 0
for i in tqdm(range(len(all_annotations)), desc="Computing AP for class %12s" %(CLASSES[idx])):
pred_boxes, pred_scores, pred_labels_list = all_detections[i]
true_boxes, true_labels_list = all_annotations[i]
detected = []
num_annotations += true_labels_list.count(idx)
for k in range(len(pred_labels_list)):
if pred_labels_list[k] != idx: continue
scores.append(pred_scores[k])
ious = utils.bbox_iou(pred_boxes[k:k+1], true_boxes)
m = np.argmax(ious)
if ious[m] > IOU_THRESH and pred_labels_list[k] == true_labels_list[m] and m not in detected:
detected.append(m)
true_positives.append(1)
else:
true_positives.append(0)
num_predictions = len(true_positives)
true_positives = np.array(true_positives)
false_positives = np.ones_like(true_positives) - true_positives
# sorted by score
indices = np.argsort(-np.array(scores))
false_positives = false_positives[indices]
true_positives = true_positives[indices]
# compute false positives and true positives
false_positives = np.cumsum(false_positives)
true_positives = np.cumsum(true_positives)
# compute recall and precision
recall = true_positives / np.maximum(num_annotations, np.finfo(np.float64).eps)
precision = true_positives / np.maximum(num_predictions, np.finfo(np.float64).eps)
# compute average precision
average_precision = utils.compute_ap(recall, precision)
all_aver_precs[CLASSES[idx]] = average_precision
for idx in range(NUM_CLASSES):
cls_name = CLASSES[idx]
print("=> Class %10s - AP: %.4f" %(cls_name, all_aver_precs[cls_name]))
print("=> mAP: %.4f" %(sum(all_aver_precs.values()) / NUM_CLASSES))