def detect_face(im, face_cascade):
img = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
fd = FaceDetector(face_cascade)
img = imutils.resize(img, width=500)
faceRects = fd.detect(img, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
raise ValueError('No face deteted.')
return face
def detect_face(im, face_cascade):
img = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
fd = FaceDetector(face_cascade)
img = imutils.resize(img, width=500)
faceRects = fd.detect(img,
scaleFactor=1.1,
minNeighbors=5,
minSize=(100, 100))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
raise ValueError('No face deteted.')
return face
def run(im,
dataset_path,
base64_path,
models_path,
face_cascade,
repre_path,
confidence_t=100):
repres = read_repre(repre_path)
img = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
fd = FaceDetector(face_cascade)
img = imutils.resize(img, width=500)
faceRects = fd.detect(img,
scaleFactor=1.1,
minNeighbors=5,
minSize=(100, 100))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
raise ValueError('No face deteted.')
train_recognizer.run(dataset_path, base64_path, models_path, face_cascade)
results = []
for (i, model_path) in enumerate(glob.glob(models_path + "/*.model")):
fr = FaceRecognizer.load(model_path)
fr.setConfidenceThreshold(confidence_t)
name = model_path.split('/')[-1].replace('.model', '')
confidence = fr.predict(face)
results.append((name, confidence))
results = sorted(results, key=lambda res: res[1])
print results
# visualize the results
k = 5
plt.figure()
plt.subplot(231)
plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.title('query')
for (i, (name, score)) in enumerate(results[:k]):
plt.subplot(2, 3, i + 2)
plt.imshow(cv2.cvtColor(repres[name], cv2.COLOR_BGR2RGB))
plt.title("#%d %s: %.4f" % (i + 1, name, score))
plt.axis('off')
plt.show()
def create_base64_files(dataset_path, base64_path, face_cascade):
if not os.path.exists(base64_path):
os.mkdir(base64_path)
fd = FaceDetector(face_cascade)
print("[INFO] Extracting faces from dataset...")
files = paths.list_images(dataset_path)
data_dict = {}
for f in files:
# name = f.split('/')[-2][:f.split('/')[-1].rfind('.')]
# label = name[:name.rfind('_')]
label = f.split('/')[-2]
img = cv2.imread(f, 0)
img = imutils.resize(img, width=500)
# faceRects = fd.detect(img, scaleFactor=1.1, minNeighbors=9, minSize=(100, 100))
faceRects = fd.detect(img,
scaleFactor=1.1,
minNeighbors=9,
minSize=(40, 40))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
# cv2.imshow('No face detected', img)
# cv2.waitKey(0)
# raise ValueError('No face deteted.')
continue
if data_dict.has_key(label):
data_dict[label].append(face)
else:
data_dict[label] = list((face, ))
# for fname in files:
for k, v in data_dict.items():
filename = k + '.txt'
filepath = os.path.join(base64_path, filename)
f = open(filepath, 'a+')
total = 0
for face in v:
f.write("{}\n".format(encodings.base64_encode_image(face)))
total += 1
print("[INFO] wrote {} frames to file {}".format(total, filename))
f.close()
def create_base64_files(dataset_path, base64_path, face_cascade):
if not os.path.exists(base64_path):
os.mkdir(base64_path)
fd = FaceDetector(face_cascade)
print("[INFO] Extracting faces from dataset...")
files = paths.list_images(dataset_path)
data_dict = {}
for f in files:
# name = f.split('/')[-2][:f.split('/')[-1].rfind('.')]
# label = name[:name.rfind('_')]
label = f.split('/')[-2]
img = cv2.imread(f, 0)
img = imutils.resize(img, width=500)
# faceRects = fd.detect(img, scaleFactor=1.1, minNeighbors=9, minSize=(100, 100))
faceRects = fd.detect(img, scaleFactor=1.1, minNeighbors=9, minSize=(40, 40))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
# cv2.imshow('No face detected', img)
# cv2.waitKey(0)
# raise ValueError('No face deteted.')
continue
if data_dict.has_key(label):
data_dict[label].append(face)
else:
data_dict[label] = list((face,))
# for fname in files:
for k, v in data_dict.items():
filename = k + '.txt'
filepath = os.path.join(base64_path, filename)
f = open(filepath, 'a+')
total = 0
for face in v:
f.write("{}\n".format(encodings.base64_encode_image(face)))
total += 1
print("[INFO] wrote {} frames to file {}".format(total, filename))
f.close()
def run(im, dataset_path, base64_path, models_path, face_cascade, repre_path, confidence_t=100):
repres = read_repre(repre_path)
img = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
fd = FaceDetector(face_cascade)
img = imutils.resize(img, width=500)
faceRects = fd.detect(img, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100))
if len(faceRects) > 0:
(x, y, w, h) = max(faceRects, key=lambda b: (b[2] * b[3]))
face = img[y:y + h, x:x + w].copy(order="C")
else:
raise ValueError('No face deteted.')
train_recognizer.run(dataset_path, base64_path, models_path, face_cascade)
results = []
for (i, model_path) in enumerate(glob.glob(models_path + "/*.model")):
fr = FaceRecognizer.load(model_path)
fr.setConfidenceThreshold(confidence_t)
name = model_path.split('/')[-1].replace('.model', '')
confidence = fr.predict(face)
results.append((name, confidence))
results = sorted(results, key=lambda res: res[1])
print results
# visualize the results
k = 5
plt.figure()
plt.subplot(231)
plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.title('query')
for (i, (name, score)) in enumerate(results[:k]):
plt.subplot(2, 3, i + 2)
plt.imshow(cv2.cvtColor(repres[name], cv2.COLOR_BGR2RGB))
plt.title("#%d %s: %.4f" % (i + 1, name, score))
plt.axis('off')
plt.show()
def data2files(data_dir, face_cascade, res_file_path, label_dict_path):
# initializing face detector
fd = FaceDetector(face_cascade)
dataset = []
files = paths.list_images(data_dir)
file_dict = process_files(files)
for key in file_dict.keys():
print 'Processing %s ...' % key,
celeb_files = file_dict[key]
n_files = len(celeb_files)
ok = 0
for f in celeb_files:
item = create_data_item(f, fd)
if item is not None:
dataset.append(item)
ok += 1
print 'done, face found: %i/%i (%i%%)' % (ok, n_files,
int(100 * ok / n_files))
print 'Saving data in pickle format...',
# data_fname = os.path.join(res_dir, 'data.npz')
data_file = gzip.open(res_file_path, 'wb', compresslevel=1)
pickle.dump(dataset, data_file)
# np.save(data_file, np.array(data_cubes_resh))
data_file.close()
print 'done'
print 'Saving label dictionary ...'
label_dict = {}
names = file_dict.keys()
for i, n in enumerate(names):
label_dict[n] = i
labels_file = gzip.open(label_dict_path, 'wb', compresslevel=1)
pickle.dump(label_dict, labels_file)
labels_file.close()
print 'done'
from pyimagesearch.face_recognition import FaceDetector
from imutils import encodings
import argparse
import imutils
import cv2
# construct the argument parse and parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face-cascade", required=True, help="path to face detection cascade")
ap.add_argument("-o", "--output", required=True, help="path to output file")
ap.add_argument("-w", "--write-mode", type=str, default="a+", help="write method for the output file")
args = vars(ap.parse_args())
# initialize the face detector, boolean indicating if we are in capturing mode or not, and
# the bounding box color
fd = FaceDetector(args["face_cascade"])
captureMode = False
color = (0, 255, 0)
# grab a reference to the webcam and open the output file for writing
camera = cv2.VideoCapture(0)
f = open(args["output"], args["write_mode"])
total = 0
# loop over the frames of the video
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# if the frame could not be grabbed, then we have reached the end of the video
if not grabbed:
help="path to face detection cascade")
ap.add_argument("-c",
"--classifier",
required=True,
help="path to the classifier")
ap.add_argument(
"-t",
"--confidence",
type=float,
default=100.0,
help="maximum confidence threshold for positive face identification")
args = vars(ap.parse_args())
# initialize the face detector, load the face recognizer, and set the confidence
# threshold
fd = FaceDetector(args["face_cascade"])
fr = FaceRecognizer.load(args["classifier"])
fr.setConfidenceThreshold(args["confidence"])
# grab a reference to the webcam
camera = cv2.VideoCapture(0)
# loop over the frames of the video
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# if the frame could not be grabbed, then we have reached the end of the video
if not grabbed:
break
import argparse
import imutils
import cv2
# construct the argument parse and parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face-cascade", required=True, help="path to face detection cascade")
ap.add_argument("-c", "--classifier", required=True, help="path to the classifier")
ap.add_argument("-t", "--confidence", type=float, default=100.0,
help="maximum confidence threshold for positive face identification")
ap.add_argument("-v", "--video", help="path to the (optional) video file")
args = vars(ap.parse_args())
# initialize the face detector, load the face recognizer, and set the confidence
# threshold
fd = FaceDetector(args["face_cascade"])
fr = FaceRecognizer.load(args["classifier"])
fr.setConfidenceThreshold(args["confidence"])
# if a video path was not supplied, grab the reference to the webcam
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, grab a reference to the video file
else:
camera = cv2.VideoCapture(args["video"])
# keep looping
while True:
# grab the current frame
(grabbed, frame) = camera.read()