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Added --multi option to infer operation to show a list of faces detec…

…ted (#189)

* Added --multi option to infer operation to show a list of faces detected in image

* Added testing for infer --multi demo
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1 parent 35c9950 commit 041c90f0c13a4501567cf46703bd6660bff5f26d @nma83 nma83 committed with bamos Sep 22, 2016
Showing with 67 additions and 33 deletions.
  1. +53 −33 demos/classifier.py
  2. BIN images/examples/longoria-cooper.jpg
  3. +14 −0 tests/openface_demo_tests.py
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@@ -50,7 +50,7 @@
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 @@ def infer(args):
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 @@ def infer(args):
if args.mode == 'train':
train(args)
elif args.mode == 'infer':
- infer(args)
+ infer(args, args.multi)
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@@ -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"

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