ap = argparse.ArgumentParser()
ap.add_argument("-i", "--dataset", required=True,
help="path to input directory of faces + images")
ap.add_argument("-e", "--embeddings", required=True,
help="path to output serialized db of facial embeddings")
ap.add_argument("-d", "--detector", required=True,
help="path to OpenCV's deep learning face detector")
ap.add_argument("-m", "--embedding-model", required=True,
help="path to OpenCV's deep learning face embedding model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# load our serialized face detector from disk
print("[INFO] loading face detector...")
protoPath = os.path.sep.join([args["detector"], "deploy.prototxt"])
modelPath = os.path.sep.join([args["detector"],
"res10_300x300_ssd_iter_140000.caffemodel"])
detector=cv2.dnn.readNetFromCaffe("D:/opencv-face-recognition/face_detection_model/deploy.prototxt", "D:/opencv-face-recognition/face_detection_model/res10_300x300_ssd_iter_140000.caffemodel")
# load our serialized face embedding model from disk
print("[INFO] loading face recognizer...")
embedder = cv2.dnn.readNetFromTorch("D:/opencv-face-recognition/openface_nn4.small2.v1.t7")
# grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize our lists of extracted facial embeddings and
# corresponding people names
knownEmbeddings = []
knownNames = []
# initialize the total number of faces processed
total = 0
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(i + 1,
len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
# load the image, resize it to have a width of 600 pixels (while
# maintaining the aspect ratio), and then grab the image
# dimensions
image = cv2.imread(imagePath)
image = imutils.resize(image, width=600)
(h, w) = image.shape[:2]
# construct a blob from the image
imageBlob = cv2.dnn.blobFromImage(
cv2.resize(image, (300, 300)), 1.0, (300, 300),
(104.0, 177.0, 123.0), swapRB=False, crop=False)
# apply OpenCV's deep learning-based face detector to localize
# faces in the input image
detector.setInput(imageBlob)
detections = detector.forward()
# ensure at least one face was found
if len(detections) > 0:
# we're making the assumption that each image has only ONE
# face, so find the bounding box with the largest probability
i = np.argmax(detections[0, 0, :, 2])
confidence = detections[0, 0, i, 2]
# ensure that the detection with the largest probability also
# means our minimum probability test (thus helping filter out
# weak detections)
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for
# the face
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI and grab the ROI dimensions
face = image[startY:endY, startX:endX]
(fH, fW) = face.shape[:2]
# ensure the face width and height are sufficiently large
if fW < 20 or fH < 20:
continue
# construct a blob for the face ROI, then pass the blob
# through our face embedding model to obtain the 128-d
# quantification of the face
faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255,
(96, 96), (0, 0, 0), swapRB=True, crop=False)
embedder.setInput(faceBlob)
vec = embedder.forward()
# add the name of the person + corresponding face
# embedding to their respective lists
knownNames.append(name)
knownEmbeddings.append(vec.flatten())
total += 1
# dump the facial embeddings + names to disk
print("[INFO] serializing {} encodings...".format(total))
data = {"embeddings": knownEmbeddings, "names": knownNames}
f = open("D:\opencv-face-recognition\output.png", "wb")
f.write(pickle.dumps(data))
f.close()
结果
[INFO] loading face detector...
[INFO] loading face recognizer...
[INFO] quantifying faces...
[INFO] serializing 0 encodings...
