From 041c90f0c13a4501567cf46703bd6660bff5f26d Mon Sep 17 00:00:00 2001 From: NMA Date: Thu, 22 Sep 2016 16:21:04 +0530 Subject: [PATCH] Added --multi option to infer operation to show a list of faces detected (#189) * Added --multi option to infer operation to show a list of faces detected in image * Added testing for infer --multi demo --- demos/classifier.py | 86 +++++++++++++++++----------- images/examples/longoria-cooper.jpg | Bin 0 -> 111840 bytes tests/openface_demo_tests.py | 14 +++++ 3 files changed, 67 insertions(+), 33 deletions(-) create mode 100644 images/examples/longoria-cooper.jpg diff --git a/demos/classifier.py b/demos/classifier.py index b415100..d80956c 100755 --- a/demos/classifier.py +++ b/demos/classifier.py @@ -50,7 +50,7 @@ dlibModelDir = os.path.join(modelDir, 'dlib') openfaceModelDir = os.path.join(modelDir, 'openface') -def getRep(imgPath): +def getRep(imgPath, multiple=False): start = time.time() bgrImg = cv2.imread(imgPath) if bgrImg is None: @@ -65,29 +65,38 @@ def getRep(imgPath): start = time.time() - bb = align.getLargestFaceBoundingBox(rgbImg) - if bb is None: + if multiple: + bbs = align.getAllFaceBoundingBoxes(rgbImg) + else: + bb1 = align.getLargestFaceBoundingBox(rgbImg) + bbs = [bb1] + if len(bbs) == 0 or (not multiple and bb1 is None): raise Exception("Unable to find a face: {}".format(imgPath)) if args.verbose: print("Face detection took {} seconds.".format(time.time() - start)) - start = time.time() - alignedFace = align.align( - args.imgDim, - rgbImg, - bb, - landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE) - if alignedFace is None: - raise Exception("Unable to align image: {}".format(imgPath)) - if args.verbose: - print("Alignment took {} seconds.".format(time.time() - start)) + reps = [] + for bb in bbs: + start = time.time() + alignedFace = align.align( + args.imgDim, + rgbImg, + bb, + landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE) + if alignedFace is None: + raise Exception("Unable to align image: {}".format(imgPath)) + if args.verbose: + print("Alignment took {} seconds.".format(time.time() - start)) + print("This bbox is centered at {}, {}".format(bb.center().x, bb.center().y)) - start = time.time() - rep = net.forward(alignedFace) - if args.verbose: - print("Neural network forward pass took {} seconds.".format( - time.time() - start)) - return rep + start = time.time() + rep = net.forward(alignedFace) + if args.verbose: + print("Neural network forward pass took {} seconds.".format( + time.time() - start)) + reps.append((bb.center().x, rep)) + sreps = sorted(reps, key=lambda x: x[0]) + return sreps def train(args): @@ -161,24 +170,33 @@ def train(args): pickle.dump((le, clf), f) -def infer(args): +def infer(args, multiple=False): with open(args.classifierModel, 'r') as f: (le, clf) = pickle.load(f) for img in args.imgs: print("\n=== {} ===".format(img)) - rep = getRep(img).reshape(1, -1) - start = time.time() - predictions = clf.predict_proba(rep).ravel() - maxI = np.argmax(predictions) - person = le.inverse_transform(maxI) - confidence = predictions[maxI] - if args.verbose: - print("Prediction took {} seconds.".format(time.time() - start)) - print("Predict {} with {:.2f} confidence.".format(person, confidence)) - if isinstance(clf, GMM): - dist = np.linalg.norm(rep - clf.means_[maxI]) - print(" + Distance from the mean: {}".format(dist)) + reps = getRep(img, multiple) + if len(reps) > 1: + print("List of faces in image from left to right") + for r in reps: + rep = r[1].reshape(1, -1) + bbx = r[0] + start = time.time() + predictions = clf.predict_proba(rep).ravel() + maxI = np.argmax(predictions) + person = le.inverse_transform(maxI) + confidence = predictions[maxI] + if args.verbose: + print("Prediction took {} seconds.".format(time.time() - start)) + if multiple: + print("Predict {} @ x={} with {:.2f} confidence.".format(person, bbx, + confidence)) + else: + print("Predict {} with {:.2f} confidence.".format(person, confidence)) + if isinstance(clf, GMM): + dist = np.linalg.norm(rep - clf.means_[maxI]) + print(" + Distance from the mean: {}".format(dist)) if __name__ == '__main__': @@ -234,6 +252,8 @@ if __name__ == '__main__': help='The Python pickle representing the classifier. This is NOT the Torch network model, which can be set with --networkModel.') inferParser.add_argument('imgs', type=str, nargs='+', help="Input image.") + inferParser.add_argument('--multi', help="Infer multiple faces in image", + action="store_true") args = parser.parse_args() if args.verbose: @@ -266,4 +286,4 @@ Use `--networkModel` to set a non-standard Torch network model.""") if args.mode == 'train': train(args) elif args.mode == 'infer': - infer(args) + infer(args, args.multi) diff --git a/images/examples/longoria-cooper.jpg b/images/examples/longoria-cooper.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a8dbba0812dd0441e3cb9d3ffb9c62aef889a6dd GIT binary patch literal 111840 zcmeGEby$^6w*ZXan@x9vbfd&!!8hxpEM{SjQ{|~03G=Cfe2o}q#%4N4i2FHtXl}C z{Q&m&2moLw{FN#=c-p}j^qoBsp1#hWUJOb`A`CjV_HYkm9uVXg;Fkpd_{A9ngd{}- zB!z_m0GbE+BU>KIFa8kLJm_zD0&EKz&>_&DwCI1;148#l4+z^|^+19A=D~~dn>Rt` z4|ae$n1Vg>BRwBK-vn?kG`Oau13=Ed0QeU78VqxQega7wJ2!I0p 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z?u#!t_3`;%o$qMv9hD^;9}V*7JgBl?IhVhNBg8o@yjKnzBbTtByD$8D4<#>J0ujz1>0) zxyKDZAlN7XHd+s~*;twuQ}OB>>4T6-B9`vP+*w9c6YOdUTPgE!vcpn3-$gr)9C*A zf7t$)_pko|z3IRAivIxnD7{qw0MW1i0Cjy>{{Y>i{{Y&0@c#h*vgv>Qri0gx{{ZeV zf9z`drT+k;{{Z_Ky+Qu~t$+Qqdb|7A{^zTo`?R0_jc3=8fAu@ZzxfA|dZ+&YxhMU` z_2obQtnn}YX}tCRBR~4HNB;oZzcBUV-}>!W{a(KUfAs^#{{Z>D?_M+i0P2b#?Xl|D w{*(Uzw%@AX`fvXLc7IZT=)e1C_2&No{{X6aXa4{oz3RvQvY-C|px*WW+5e?1p8x;= literal 0 HcmV?d00001 diff --git a/tests/openface_demo_tests.py b/tests/openface_demo_tests.py index 7dd0580..a086766 100644 --- a/tests/openface_demo_tests.py +++ b/tests/openface_demo_tests.py @@ -51,6 +51,20 @@ def test_classification_demo_pretrained(): assert "Predict SteveCarell with 0.97 confidence." in out +def test_classification_demo_pretrained_multi(): + cmd = ['python2', os.path.join(openfaceDir, 'demos', 'classifier.py'), + 'infer', '--multi', + os.path.join(openfaceDir, 'models', 'openface', + 'celeb-classifier.nn4.small2.v1.pkl'), + os.path.join(exampleImages, 'longoria-cooper.jpg')] + p = Popen(cmd, stdout=PIPE, stderr=PIPE) + (out, err) = p.communicate() + print(out) + print(err) + assert "Predict EvaLongoria @ x=91 with 0.99 confidence." in out + assert "Predict BradleyCooper @ x=191 with 0.99 confidence." in out + + def test_classification_demo_training(): assert os.path.isdir(lfwSubset), "Get lfw-subset by running ./data/download-lfw-subset.sh"