197 lines
6.8 KiB
Python
197 lines
6.8 KiB
Python
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# This script is used to estimate an accuracy of different face detection models.
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# COCO evaluation tool is used to compute an accuracy metrics (Average Precision).
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# Script works with different face detection datasets.
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import os
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import json
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from fnmatch import fnmatch
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from math import pi
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import cv2 as cv
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import argparse
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import os
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import sys
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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parser = argparse.ArgumentParser(
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description='Evaluate OpenCV face detection algorithms '
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'using COCO evaluation tool, http://cocodataset.org/#detections-eval')
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parser.add_argument('--proto', help='Path to .prototxt of Caffe model or .pbtxt of TensorFlow graph')
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parser.add_argument('--model', help='Path to .caffemodel trained in Caffe or .pb from TensorFlow')
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parser.add_argument('--cascade', help='Optional path to trained Haar cascade as '
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'an additional model for evaluation')
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parser.add_argument('--ann', help='Path to text file with ground truth annotations')
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parser.add_argument('--pics', help='Path to images root directory')
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parser.add_argument('--fddb', help='Evaluate FDDB dataset, http://vis-www.cs.umass.edu/fddb/', action='store_true')
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parser.add_argument('--wider', help='Evaluate WIDER FACE dataset, http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/', action='store_true')
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args = parser.parse_args()
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dataset = {}
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dataset['images'] = []
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dataset['categories'] = [{ 'id': 0, 'name': 'face' }]
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dataset['annotations'] = []
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def ellipse2Rect(params):
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rad_x = params[0]
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rad_y = params[1]
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angle = params[2] * 180.0 / pi
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center_x = params[3]
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center_y = params[4]
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pts = cv.ellipse2Poly((int(center_x), int(center_y)), (int(rad_x), int(rad_y)),
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int(angle), 0, 360, 10)
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rect = cv.boundingRect(pts)
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left = rect[0]
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top = rect[1]
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right = rect[0] + rect[2]
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bottom = rect[1] + rect[3]
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return left, top, right, bottom
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def addImage(imagePath):
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assert('images' in dataset)
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imageId = len(dataset['images'])
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dataset['images'].append({
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'id': int(imageId),
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'file_name': imagePath
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})
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return imageId
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def addBBox(imageId, left, top, width, height):
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assert('annotations' in dataset)
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dataset['annotations'].append({
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'id': len(dataset['annotations']),
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'image_id': int(imageId),
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'category_id': 0, # Face
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'bbox': [int(left), int(top), int(width), int(height)],
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'iscrowd': 0,
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'area': float(width * height)
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})
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def addDetection(detections, imageId, left, top, width, height, score):
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detections.append({
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'image_id': int(imageId),
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'category_id': 0, # Face
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'bbox': [int(left), int(top), int(width), int(height)],
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'score': float(score)
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})
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def fddb_dataset(annotations, images):
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for d in os.listdir(annotations):
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if fnmatch(d, 'FDDB-fold-*-ellipseList.txt'):
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with open(os.path.join(annotations, d), 'rt') as f:
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lines = [line.rstrip('\n') for line in f]
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lineId = 0
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while lineId < len(lines):
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# Image
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imgPath = lines[lineId]
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lineId += 1
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imageId = addImage(os.path.join(images, imgPath) + '.jpg')
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img = cv.imread(os.path.join(images, imgPath) + '.jpg')
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# Faces
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numFaces = int(lines[lineId])
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lineId += 1
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for i in range(numFaces):
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params = [float(v) for v in lines[lineId].split()]
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lineId += 1
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left, top, right, bottom = ellipse2Rect(params)
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addBBox(imageId, left, top, width=right - left + 1,
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height=bottom - top + 1)
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def wider_dataset(annotations, images):
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with open(annotations, 'rt') as f:
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lines = [line.rstrip('\n') for line in f]
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lineId = 0
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while lineId < len(lines):
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# Image
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imgPath = lines[lineId]
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lineId += 1
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imageId = addImage(os.path.join(images, imgPath))
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# Faces
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numFaces = int(lines[lineId])
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lineId += 1
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for i in range(numFaces):
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params = [int(v) for v in lines[lineId].split()]
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lineId += 1
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left, top, width, height = params[0], params[1], params[2], params[3]
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addBBox(imageId, left, top, width, height)
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def evaluate():
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cocoGt = COCO('annotations.json')
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cocoDt = cocoGt.loadRes('detections.json')
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
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cocoEval.evaluate()
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cocoEval.accumulate()
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cocoEval.summarize()
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### Convert to COCO annotations format #########################################
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assert(args.fddb or args.wider)
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if args.fddb:
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fddb_dataset(args.ann, args.pics)
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elif args.wider:
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wider_dataset(args.ann, args.pics)
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with open('annotations.json', 'wt') as f:
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json.dump(dataset, f)
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### Obtain detections ##########################################################
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detections = []
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if args.proto and args.model:
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net = cv.dnn.readNet(args.proto, args.model)
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def detect(img, imageId):
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imgWidth = img.shape[1]
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imgHeight = img.shape[0]
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net.setInput(cv.dnn.blobFromImage(img, 1.0, (300, 300), (104., 177., 123.), False, False))
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out = net.forward()
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for i in range(out.shape[2]):
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confidence = out[0, 0, i, 2]
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left = int(out[0, 0, i, 3] * img.shape[1])
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top = int(out[0, 0, i, 4] * img.shape[0])
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right = int(out[0, 0, i, 5] * img.shape[1])
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bottom = int(out[0, 0, i, 6] * img.shape[0])
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x = max(0, min(left, img.shape[1] - 1))
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y = max(0, min(top, img.shape[0] - 1))
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w = max(0, min(right - x + 1, img.shape[1] - x))
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h = max(0, min(bottom - y + 1, img.shape[0] - y))
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addDetection(detections, imageId, x, y, w, h, score=confidence)
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elif args.cascade:
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cascade = cv.CascadeClassifier(args.cascade)
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def detect(img, imageId):
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srcImgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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faces = cascade.detectMultiScale(srcImgGray)
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for rect in faces:
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left, top, width, height = rect[0], rect[1], rect[2], rect[3]
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addDetection(detections, imageId, left, top, width, height, score=1.0)
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for i in range(len(dataset['images'])):
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sys.stdout.write('\r%d / %d' % (i + 1, len(dataset['images'])))
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sys.stdout.flush()
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img = cv.imread(dataset['images'][i]['file_name'])
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imageId = int(dataset['images'][i]['id'])
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detect(img, imageId)
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with open('detections.json', 'wt') as f:
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json.dump(detections, f)
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evaluate()
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def rm(f):
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if os.path.exists(f):
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os.remove(f)
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rm('annotations.json')
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rm('detections.json')
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