134 lines
4.9 KiB
Python
134 lines
4.9 KiB
Python
from __future__ import print_function
|
|
# Script to evaluate MobileNet-SSD object detection model trained in TensorFlow
|
|
# using both TensorFlow and OpenCV. Example:
|
|
#
|
|
# python mobilenet_ssd_accuracy.py \
|
|
# --weights=frozen_inference_graph.pb \
|
|
# --prototxt=ssd_mobilenet_v1_coco.pbtxt \
|
|
# --images=val2017 \
|
|
# --annotations=annotations/instances_val2017.json
|
|
#
|
|
# Tested on COCO 2017 object detection dataset, http://cocodataset.org/#download
|
|
import os
|
|
import cv2 as cv
|
|
import json
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description='Evaluate MobileNet-SSD model using both TensorFlow and OpenCV. '
|
|
'COCO evaluation framework is required: http://cocodataset.org')
|
|
parser.add_argument('--weights', required=True,
|
|
help='Path to frozen_inference_graph.pb of MobileNet-SSD model. '
|
|
'Download it from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz')
|
|
parser.add_argument('--prototxt', help='Path to ssd_mobilenet_v1_coco.pbtxt from opencv_extra.', required=True)
|
|
parser.add_argument('--images', help='Path to COCO validation images directory.', required=True)
|
|
parser.add_argument('--annotations', help='Path to COCO annotations file.', required=True)
|
|
args = parser.parse_args()
|
|
|
|
### Get OpenCV predictions #####################################################
|
|
net = cv.dnn.readNetFromTensorflow(cv.samples.findFile(args.weights), cv.samples.findFile(args.prototxt))
|
|
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
|
|
|
|
detections = []
|
|
for imgName in os.listdir(args.images):
|
|
inp = cv.imread(cv.samples.findFile(os.path.join(args.images, imgName)))
|
|
rows = inp.shape[0]
|
|
cols = inp.shape[1]
|
|
inp = cv.resize(inp, (300, 300))
|
|
|
|
net.setInput(cv.dnn.blobFromImage(inp, 1.0/127.5, (300, 300), (127.5, 127.5, 127.5), True))
|
|
out = net.forward()
|
|
|
|
for i in range(out.shape[2]):
|
|
score = float(out[0, 0, i, 2])
|
|
# Confidence threshold is in prototxt.
|
|
classId = int(out[0, 0, i, 1])
|
|
|
|
x = out[0, 0, i, 3] * cols
|
|
y = out[0, 0, i, 4] * rows
|
|
w = out[0, 0, i, 5] * cols - x
|
|
h = out[0, 0, i, 6] * rows - y
|
|
detections.append({
|
|
"image_id": int(imgName.rstrip('0')[:imgName.rfind('.')]),
|
|
"category_id": classId,
|
|
"bbox": [x, y, w, h],
|
|
"score": score
|
|
})
|
|
|
|
with open('cv_result.json', 'wt') as f:
|
|
json.dump(detections, f)
|
|
|
|
### Get TensorFlow predictions #################################################
|
|
import tensorflow as tf
|
|
|
|
with tf.gfile.FastGFile(args.weights) as f:
|
|
# Load the model
|
|
graph_def = tf.GraphDef()
|
|
graph_def.ParseFromString(f.read())
|
|
|
|
with tf.Session() as sess:
|
|
# Restore session
|
|
sess.graph.as_default()
|
|
tf.import_graph_def(graph_def, name='')
|
|
|
|
detections = []
|
|
for imgName in os.listdir(args.images):
|
|
inp = cv.imread(os.path.join(args.images, imgName))
|
|
rows = inp.shape[0]
|
|
cols = inp.shape[1]
|
|
inp = cv.resize(inp, (300, 300))
|
|
inp = inp[:, :, [2, 1, 0]] # BGR2RGB
|
|
out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
|
|
sess.graph.get_tensor_by_name('detection_scores:0'),
|
|
sess.graph.get_tensor_by_name('detection_boxes:0'),
|
|
sess.graph.get_tensor_by_name('detection_classes:0')],
|
|
feed_dict={'image_tensor:0': inp.reshape(1, inp.shape[0], inp.shape[1], 3)})
|
|
num_detections = int(out[0][0])
|
|
for i in range(num_detections):
|
|
classId = int(out[3][0][i])
|
|
score = float(out[1][0][i])
|
|
bbox = [float(v) for v in out[2][0][i]]
|
|
if score > 0.01:
|
|
x = bbox[1] * cols
|
|
y = bbox[0] * rows
|
|
w = bbox[3] * cols - x
|
|
h = bbox[2] * rows - y
|
|
detections.append({
|
|
"image_id": int(imgName.rstrip('0')[:imgName.rfind('.')]),
|
|
"category_id": classId,
|
|
"bbox": [x, y, w, h],
|
|
"score": score
|
|
})
|
|
|
|
with open('tf_result.json', 'wt') as f:
|
|
json.dump(detections, f)
|
|
|
|
### Evaluation part ############################################################
|
|
|
|
# %matplotlib inline
|
|
import matplotlib.pyplot as plt
|
|
from pycocotools.coco import COCO
|
|
from pycocotools.cocoeval import COCOeval
|
|
import numpy as np
|
|
import skimage.io as io
|
|
import pylab
|
|
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
|
|
|
|
annType = ['segm','bbox','keypoints']
|
|
annType = annType[1] #specify type here
|
|
prefix = 'person_keypoints' if annType=='keypoints' else 'instances'
|
|
print('Running demo for *%s* results.'%(annType))
|
|
|
|
#initialize COCO ground truth api
|
|
cocoGt=COCO(args.annotations)
|
|
|
|
#initialize COCO detections api
|
|
for resFile in ['tf_result.json', 'cv_result.json']:
|
|
print(resFile)
|
|
cocoDt=cocoGt.loadRes(resFile)
|
|
|
|
cocoEval = COCOeval(cocoGt,cocoDt,annType)
|
|
cocoEval.evaluate()
|
|
cocoEval.accumulate()
|
|
cocoEval.summarize()
|