def detect(image_path, trackerid, ts, cameraId, face_filter): result = m.detect(image_path) #FIXME: result = result.replace('[,', '[') result = json.loads(result) #print('detect result-----',result) people_cnt = 0 cropped = [] detected = False nrof_faces, img_data, imgs_style, blury_arr, face_width, face_height = load_align_image_v2(result, image_path, trackerid, ts, cameraId, face_filter) if img_data is not None and len(img_data) > 0: people_cnt = len(img_data) detected = True for align_image_path, prewhitened in img_data.items(): style=imgs_style[align_image_path] blury=blury_arr[align_image_path] width=face_width[align_image_path] height=face_height[align_image_path] cropped.append({"path": align_image_path, "style": style, "blury": blury, "ts": ts, "trackerid": trackerid, "totalPeople": people_cnt, "cameraId": cameraId, "width":width,"height":height}) return json.dumps({'detected': detected, "ts": ts, "totalPeople": people_cnt, "cropped": cropped, 'totalmtcnn': nrof_faces})
def onPhoto(): #test1 = imagen en formato base64 de alejandro #test2 = imagen en formateo base54 de obama img = b64ToImg(open("test_2", "rb").read()) id = detect(img) if id is None: print "Nada" else: print id
def demo(in_fn, out_fn): print ">>> Loading image..." img_color = cv2.imread(in_fn) img_gray = cv2.cvtColor(img_color, cv.CV_RGB2GRAY) img_gray = cv2.equalizeHist(img_gray) print in_fn, img_gray.shape print ">>> Detecting faces..." start = time.time() rects = detect(img_gray) end = time.time() print 'time:', end - start img_out = img_color.copy() draw_rects(img_out, rects, (0, 255, 0)) cv2.imwrite(out_fn, img_out)
def __init__(self, img, image_src='/tmp/temp.jpg', segment_dump="/tmp/", a_score=1, prediction_stage=False, min_saliency=0.02, max_iter_slic=100): self.timer = time.time() self.image_src = image_src self.a_score = a_score self.image = img # self.image = io.imread(image_src) print "Image source : ", image_src self.__set_timer("segmentation...") segment_object = seg.SuperPixelSegmentation(self.image, max_iter=max_iter_slic) self.segment_map = segment_object.getSegmentationMap() self.slic_map = segment_object.getSlicSegmentationMap() self.__print_timer("segmentation") self.__set_timer("saliency...") saliency_object = saliency.Saliency(self.image, 3) self.saliency_map = saliency_object.getSaliencyMap() self.__print_timer("saliency") # perform face detection self.__set_timer("face detection...") self.faces = face_detection.detect(np.array(self.image)) self.__print_timer("face detection") self.__set_timer("saliency detection of objects...") self.saliency_list, self.salient_objects, self.pixel_count, self.segment_map2 = cutils.detect_saliency_of_segments( self.segment_map.astype(np.intp), self.saliency_map, min_saliency) self.__print_timer("saliency detection of objects")
def clicked2(self): self.label_3.setText("Status: Id/Roll No-" + self.lineEdit.text() + ", Name- " + self.lineEdit_2.text()) self.update() face_detection.detect(self.lineEdit.text(), self.lineEdit_2.text())
'cpus': 2, 'result': 0 }, { 'res': 1080, 'file': './1_1920x1080.jpg', 'minsize': 200, 'cpus': 2, 'result': 0 }] m.init('./model/') print('warming up') m.set_minsize(40) m.set_num_threads(1) m.set_threshold(0.6, 0.7, 0.8) result = m.detect('./1_854x480.jpg') print(result) m.detect('./1_854x480.jpg') m.detect('./1_854x480.jpg') m.detect('./1_854x480.jpg') m.detect('./1_854x480.jpg') print('starting up') rounds = 20 for item in benchmark: print(item) m.set_minsize(item['minsize']) m.set_num_threads(item['cpus']) start = time.time() for i in range(rounds):
import face_detection import face_recognition import numpy as np import pickle import os # image = 'images/thang.jpg' # faces = face_detection.detect(image) # face_recognition.learn(faces, 'thang') folder = 'test' for image in os.listdir(folder): path = '/'.join([folder, image]) faces = face_detection.detect(path) faces_with_name = face_recognition.recognition(faces, path)
import face_detection as m import time m.init('./models/ncnn/') print('warming up') m.set_minsize(40) m.set_threshold(0.6, 0.7, 0.8) m.set_num_threads(1) m.detect('./images_480p/1_854x480.jpg') m.detect('./images_480p/1_854x480.jpg') m.detect('./images_480p/1_854x480.jpg') m.detect('./images_480p/1_854x480.jpg') m.detect('./images_480p/1_854x480.jpg') start = time.time() for i in range(100): step_start = time.time() result = m.detect('./images_480p/1_854x480.jpg') step_end = time.time() print('step {} duration is {}'.format(i, step_end - step_start)) end = time.time() print(result) print('average duration is {}'.format((end - start) / 100))
model_name = 'blind_with_regularization.model' COM = 'COM9' camera = 1 baudrate = 9600 width = 64 height = 64 prob = 0 label = '' print("loading model .....") model = load_model(model_name) print("model loaded") ard = Arduino(baudrate, COM) ##movleft(),movright() vce = Voice() #left(),right() st = Stop() fac = detect() current = datetime.datetime.now() flag = None cap = cv2.VideoCapture(camera) ret = True prev = None while ret: ret, frame = cap.read() frame = cv2.resize(frame, (640, 480)) faces = fac.faceDetect(frame) ##stop on left ## you have a stop on ''' current = datetime.datetime.now()
import face_detection as m import time m.init('./model/') m.set_minsize(100) m.set_threshold(0.6, 0.7, 0.8) m.set_num_threads(2) print('warming up') result = m.detect('./1_1920x1080.jpg') print(result) m.detect('./1_1920x1080.jpg') m.detect('./1_1920x1080.jpg') m.detect('./1_1920x1080.jpg') m.detect('./1_1920x1080.jpg') start = time.time() for i in range(10): step_start = time.time() result = m.detect('./1_1920x1080.jpg') step_end = time.time() print('step {} duration is {}'.format(i, step_end - step_start)) end = time.time() print(result) print('1080p average duration is {}'.format((end - start) / 10))