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