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import os
from os.path import join
from pathlib import Path
import cv2 as cv
import numpy as np
import imutils
import operator
from app.utils import im_utils, logger_utils
from app.models.bbox import BBox
from app.settings import app_cfg as cfg
from app.settings import types
class DetectorMTCNN:
# https://github.com/ipazc/mtcnn
# pip install mtcnn
dnn_size = (300, 300)
def __init__(self, size=(400,400)):
from mtcnn.mtcnn import MTCNN
self.detector = MTCNN()
def detect(self, im, opt_size=(400,400), opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
'''Detects face using MTCNN and returns (list) of BBox
:param im: (numpy.ndarray) image
:returns list of BBox
'''
bboxes = []
#conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
#pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
dnn_size = self.dnn_size if opt_size is None else opt_size
im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
dim = im.shape[:2][::-1]
dets = self.detector.detect_faces(im)
for det in dets:
rect = det['box']
#keypoints = det['keypoints'] # not using here. see 'face_landmarks.py'
bbox = BBox.from_xywh_dim(*rect, dim)
bboxes.append(bbox)
if opt_largest and len(bboxes) > 1:
# only keep largest
bboxes.sort(key=operator.attrgetter('area'), reverse=True)
bboxes = [bboxes[0]]
return bboxes
class DetectorHaar:
im_size = (400, 400)
cascade_name = types.HaarCascade.FRONTAL
def __init__(self, cascade=types.HaarCascade.FRONTAL):
self.log = logger_utils.Logger.getLogger()
def detect(self, im, scale_factor=1.05, overlaps=5):
pass
class DetectorDLIBCNN:
dnn_size = (300, 300)
pyramids = 0
conf_thresh = 0.85
def __init__(self, opt_gpu=0):
import dlib
self.log = logger_utils.Logger.getLogger()
cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
self.log.info('load model: {}'.format(cfg.DIR_MODELS_DLIB_CNN))
self.detector = dlib.cnn_face_detection_model_v1(cfg.DIR_MODELS_DLIB_CNN)
os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices # reset
def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
bboxes = []
conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
dnn_size = self.dnn_size if opt_size is None else opt_size
# resize image
im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
dim = im.shape[:2][::-1]
im = im_utils.bgr2rgb(im) # convert to RGB for dlib
# run detector
mmod_rects = self.detector(im, opt_pyramids)
# sort results
for mmod_rect in mmod_rects:
self.log.debug('conf: {}, this: {}'.format(conf_thresh, mmod_rect.confidence))
if mmod_rect.confidence > conf_thresh:
bbox = BBox.from_dlib_dim(mmod_rect.rect, dim)
bboxes.append(bbox)
if opt_largest and len(bboxes) > 1:
# only keep largest
bboxes.sort(key=operator.attrgetter('area'), reverse=True)
bboxes = [bboxes[0]]
return bboxes
class DetectorDLIBHOG:
size = (320, 240)
pyramids = 0
conf_thresh = 0.85
def __init__(self):
import dlib
self.log = logger_utils.Logger.getLogger()
self.detector = dlib.get_frontal_face_detector()
def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=0, opt_largest=False):
conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
dnn_size = self.size if opt_size is None else opt_size
pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
im = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
dim = im.shape[:2][::-1]
im = im_utils.bgr2rgb(im) # ?
hog_results = self.detector.run(im, pyramids)
bboxes = []
if len(hog_results[0]) > 0:
self.log.debug(hog_results)
for rect, score, direction in zip(*hog_results):
if score > conf_thresh:
bbox = BBox.from_dlib_dim(rect, dim)
bboxes.append(bbox)
if opt_largest and len(bboxes) > 1:
# only keep largest
bboxes.sort(key=operator.attrgetter('area'), reverse=True)
bboxes = [bboxes[0]]
return bboxes
class DetectorCVDNN:
dnn_scale = 1.0 # fixed
dnn_mean = (104.0, 177.0, 123.0) # fixed
dnn_crop = False # crop or force resize
size = (300, 300)
conf_thresh = 0.85
def __init__(self):
import dlib
fp_prototxt = join(cfg.DIR_MODELS_CAFFE, 'face_detect', 'opencv_face_detector.prototxt')
fp_model = join(cfg.DIR_MODELS_CAFFE, 'face_detect', 'opencv_face_detector.caffemodel')
self.net = cv.dnn.readNet(fp_prototxt, fp_model)
self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_largest=False, opt_pyramids=None):
"""Detects faces and returns (list) of (BBox)"""
conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
dnn_size = self.size if opt_size is None else opt_size
im = cv.resize(im, dnn_size)
blob = cv.dnn.blobFromImage(im, self.dnn_scale, dnn_size, self.dnn_mean)
self.net.setInput(blob)
net_outputs = self.net.forward()
bboxes = []
for i in range(0, net_outputs.shape[2]):
conf = net_outputs[0, 0, i, 2]
if conf > conf_thresh:
rect_norm = net_outputs[0, 0, i, 3:7]
bboxes.append(BBox(*rect_norm))
if opt_largest and len(bboxes) > 1:
# only keep largest
bboxes.sort(key=operator.attrgetter('area'), reverse=True)
bboxes = [bboxes[0]]
return bboxes
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