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