def evalu_img(args):
imgpath = args.img_path
min_size = args.min_size
model_dir = args.model_dir
cv2.namedWindow("test")
cv2.moveWindow("test",1400,10)
base_name = "test_img"
save_dir = './output'
crop_size = [112,112]
FaceAnti_model_dir = os.path.join(model_dir,cfgs.DATASET_NAME)
model_path = os.path.join(FaceAnti_model_dir,cfgs.MODEL_PREFIX)
FaceAnti_Model = Face_Anti_Spoof(model_path,args.load_epoch,cfgs.IMG_SIZE,args.gpu,layer='fc')
caffe_mode_dir = os.path.join(model_dir,'face_detect_models')
threshold = np.array([0.7,0.8,0.95])
#Detect_Model = MTCNNDet(min_size,threshold,caffe_mode_dir)
Detect_Model = MtcnnDetector(min_size,threshold,caffe_mode_dir)
img = cv2.imread(imgpath)
h,w = img.shape[:2]
if cfgs.img_downsample and h > 1000:
img = img_ratio(img,240)
rectangles = Detect_Model.detectFace(img)
#draw = img.copy()
face_attributes = []
if len(rectangles)>0:
rectangles = sort_box(rectangles)
img_verify = img_crop(img,rectangles[0],img.shape[1],img.shape[0])
face_attributes.append(img_verify)
tmp,pred_id = FaceAnti_Model.inference(face_attributes)
label_show(img,rectangles,tmp,pred_id)
else:
print("No face detected")
cv2.imshow("test",img)
cv2.waitKey(0)
def __init__(self, args):
self.Detect_Model = MtcnnDetector(args)
self.FaceAttributeModel = FaceAttribute(args)
self.threshold = args.threshold
self.img_dir = args.img_dir
self.real_num = 0
self.save_dir = args.save_dir
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
def video_demo(args):
'''
file_in: input video file path
base_name: saved images prefix name
'''
file_in = args.file_in
min_size = args.min_size
model_dir = args.model_dir
FaceAnti_model_dir = os.path.join(model_dir,cfgs.DATASET_NAME)
model_path = os.path.join(FaceAnti_model_dir,cfgs.MODEL_PREFIX)
if args.gpu is None:
ctx = mx.cpu()
else:
ctx = mx.gpu(0)
FaceAnti_Model = Face_Anti_Spoof(model_path,args.load_epoch,cfgs.IMG_SIZE,ctx=ctx)
caffe_mode_dir = os.path.join(model_dir,'face_detect_models')
threshold = np.array([0.7,0.8,0.95])
#Detect_Model = MTCNNDet(min_size,threshold,caffe_mode_dir)
Detect_Model = MtcnnDetector(min_size,threshold,caffe_mode_dir)
if file_in is None:
v_cap = cv2.VideoCapture(0)
else:
v_cap = cv2.VideoCapture(file_in)
if not v_cap.isOpened():
print("field to open video")
else:
print("video frame num: ",v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
#total_num = v_cap.get(cv2.CAP_PROP_FRAME_COUNT)
frame_w = v_cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_h = v_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
#frame_cnt = 0
#class_dict = dict()
#pred_id_show = int(0)
cv2.namedWindow("src")
cv2.namedWindow("crop")
cv2.moveWindow("crop",1400,10)
cv2.moveWindow("src",10,10)
while v_cap.isOpened():
ret,frame = v_cap.read()
if ret:
rectangles = Detect_Model.detectFace(frame)
else:
continue
face_attributes = []
if len(rectangles)> 0:
'''
rectangles = sort_box(rectangles)
frame_cnt+=1
if frame_cnt == 10:
frame_cnt =0
for key_name in class_dict.keys():
if key_name in cfgs.DATA_NAME[1:]:
pred_id_show = int(1)
break
else:
pred_id_show = int(0)
class_dict.clear()
else:
cur_cnt = class_dict.setdefault(cfgs.DATA_NAME[pred_id],0)
class_dict[cfgs.DATA_NAME[pred_id]] = cur_cnt+1
'''
for box in rectangles:
img_verify = img_crop(frame,box,frame_w,frame_h)
img_verify = Img_Pad(img_verify,cfgs.IMG_SIZE)
cv2.imshow('crop',img_verify)
face_attributes.append(img_verify)
t1 = time.time()
tmp,pred_id = FaceAnti_Model.inference(face_attributes)
t2 = time.time()
print("inference time: ",t2-t1)
label_show(frame,rectangles,tmp,pred_id)
#cv2.imshow('crop',img_verify)
else:
#print("failed ")
pass
cv2.imshow("src",frame)
key_ = cv2.waitKey(10) & 0xFF
if key_ == 27 or key_ == ord('q'):
break
#if fram_cnt == total_num:
# break
v_cap.release()
cv2.destroyAllWindows()
def evalue_fromtxt(args):
'''
file_in: images path recorded
base_dir: images locate in
save_dir: detect faces saved in
fun: saved id images, save name is the same input image
'''
file_in = args.file_in
base_dir = args.base_dir
#base_name = args.base_name
#save_dir = args.save_dir
#crop_size = args.crop_size
#size_spl = crop_size.strip().split(',')
#crop_size = [int(size_spl[0]),int(size_spl[1])]
model_dir = args.model_dir
min_size = args.min_size
f_ = open(file_in,'r')
failed_w = open('./output/failed_face3.txt','w')
lines_ = f_.readlines()
FaceAnti_model_dir = os.path.join(model_dir,cfgs.DATASET_NAME)
model_path = os.path.join(FaceAnti_model_dir,cfgs.MODEL_PREFIX)
FaceAnti_Model = Face_Anti_Spoof(model_path,args.load_epoch,cfgs.IMG_SIZE,args.gpu,layer='fc')
caffe_mode_dir = os.path.join(model_dir,'face_detect_models')
threshold = np.array([0.7,0.8,0.95])
#Detect_Model = MTCNNDet(min_size,threshold,caffe_mode_dir)
Detect_Model = MtcnnDetector(min_size,threshold,caffe_mode_dir)
#mk_dirs(save_dir)
idx_cnt = 0
if cfgs.show:
cv2.namedWindow("src")
cv2.namedWindow("crop")
cv2.moveWindow("crop",650,10)
cv2.moveWindow("src",100,10)
total_item = len(lines_)
for i in tqdm(range(total_item)):
line_1 = lines_[i]
line_1 = line_1.strip()
img_path = os.path.join(base_dir,line_1)
img = cv2.imread(img_path)
if img is None:
continue
h,w = img.shape[:2]
if cfgs.img_downsample and min(w,h) > 1000:
img = img_ratio(img,240)
line_s = line_1.split("/")
img_name = line_s[-1]
new_dir = '/'.join(line_s[:-1])
rectangles = Detect_Model.detectFace(img)
face_attributes = []
if len(rectangles)> 0:
idx_cnt+=1
rectangles = sort_box(rectangles)
img_verify = img_crop(img,rectangles[0],img.shape[1],img.shape[0])
face_attributes.append(img_verify)
tmp,pred_id = FaceAnti_Model.inference(face_attributes)
#cv2.imwrite(savepath,img)
if cfgs.show:
label_show(img,rectangles,tmp,pred_id)
cv2.imshow("crop",img_verify)
cv2.waitKey(1000)
else:
failed_w.write(img_path)
failed_w.write('\n')
print("failed ",img_path)
if cfgs.show:
cv2.imshow("src",img)
cv2.waitKey(10)
failed_w.close()
f_.close()
def __init__(self,args):
self.Detect_Model = MtcnnDetector(args)
self.BreathMaskModel = BreathMask(args)
self.threshold = args.threshold
self.img_dir = args.img_dir
self.real_num = 0
class BreathMastTest(object):
def __init__(self,args):
self.Detect_Model = MtcnnDetector(args)
self.BreathMaskModel = BreathMask(args)
self.threshold = args.threshold
self.img_dir = args.img_dir
self.real_num = 0
def img_crop(self,img,bbox,imgw,imgh):
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
boxw = x2-x1
boxh = y2-y1
x1 = int(max(0,int(x1-0.3*boxw)))
y1 = int(max(0,int(y1-0.3*boxh)))
x2 = int(min(imgw,int(x2+0.3*boxw)))
y2 = int(min(imgh,int(y2+0.3*boxh)))
cropimg = img[y1:y2,x1:x2,:]
return cropimg
def label_show(self,img,rectangles,scores,pred_id):
'''
scores: shape-[batch,cls_nums]
pred_id: shape-[batch,cls_nums]
rectangles: shape-[batch,15]
0-5: x1,y1,x2,y2,score
'''
show_labels = ['no_weare','weare_mask']
colors = [(0,0,255),(255,0,0)]
for idx,box in enumerate(rectangles):
tmp_pred = pred_id[idx]
tmp_score = '%.2f' % scores[idx]
show_name = show_labels[int(tmp_pred)] +'_'+tmp_score
color = colors[int(tmp_pred)]
cv2.rectangle(img,(int(box[0]),int(box[1])),(int(box[2]),int(box[3])),color,1)
font=cv2.FONT_HERSHEY_COMPLEX_SMALL
font_scale = int((box[3]-box[1])*0.01)
points = (int(box[0]),int(box[1]))
cv2.putText(img, show_name, points, font, font_scale, color, 2)
return img
def inference_img(self,img):
t1 = time.time()
imgorg = img.copy()
orgreg = 0
img_out = img
pred_ids = []
if not orgreg:
rectangles = self.Detect_Model.detectFace(imgorg)
face_breathmasks = []
frame_h,frame_w = imgorg.shape[:2]
img_out = img.copy()
pred_ids = []
if len(rectangles)> 0:
for box in rectangles:
img_verify = self.img_crop(imgorg,box,frame_w,frame_h)
img_verify = cv2.resize(img_verify,(112,112))
# img_verify = Img_Pad(img_verify,[cfg.InputSize_h,cfg.InputSize_w])
cv2.imshow('crop',img_verify)
face_breathmasks.append(img_verify)
scores,pred_ids = self.BreathMaskModel.inference(face_breathmasks)
img_out = self.label_show(img,rectangles,scores,pred_ids)
else:
scores,pred_ids = self.BreathMaskModel.inference([img])
img_out = self.label_show(img,[[10,10,119,119]],scores,pred_ids)
t2 = time.time()
#print('consuming:',t2-t1)
return img_out,pred_ids
def __call__(self,imgpath):
if os.path.isdir(imgpath):
cnts = os.listdir(imgpath)
for tmp in cnts:
tmppath = os.path.join(imgpath,tmp.strip())
img = cv2.imread(tmppath)
if img is None:
continue
frame,cnt_head = self.inference_img(img)
# print('heads >> ',cnt_head)
cv2.imshow('result',frame)
#savepath = os.path.join(self.save_dir,save_name)
# cv2.imwrite('test.jpg',frame)
cv2.waitKey(0)
elif os.path.isfile(imgpath) and imgpath.endswith('txt'):
# if not os.path.exists(self.save_dir):
# os.makedirs(self.save_dir)
f_r = open(imgpath,'r')
file_cnts = f_r.readlines()
for j in tqdm(range(len(file_cnts))):
tmp_file = file_cnts[j].strip()
tmp_file_s = tmp_file.split('\t')
if len(tmp_file_s)>0:
tmp_file = tmp_file_s[0]
self.real_num = int(tmp_file_s[1])
if not tmp_file.endswith('jpg'):
tmp_file = tmp_file +'.jpg'
# tmp_path = os.path.join(self.img_dir,tmp_file)
tmp_path = tmp_file
if not os.path.exists(tmp_path):
print(tmp_path)
continue
img = cv2.imread(tmp_path)
if img is None:
print('None',tmp)
continue
frame,cnt_head = self.inference_img(img)
cv2.imshow('result',frame)
#savepath = os.path.join(self.save_dir,save_name)
#cv2.imwrite('test.jpg',frame)
cv2.waitKey(0)
elif os.path.isfile(imgpath) and imgpath.endswith(('.mp4','.avi')) :
cap = cv2.VideoCapture(imgpath)
if not cap.isOpened():
print("failed open camera")
return 0
else:
while cap.isOpened():
_,img = cap.read()
frame,cnt_head = self.inference_img(img)
cv2.imshow('result',frame)
q=cv2.waitKey(10) & 0xFF
if q == 27 or q ==ord('q'):
break
cap.release()
cv2.destroyAllWindows()
elif os.path.isfile(imgpath):
img = cv2.imread(imgpath)
if img is not None:
# grab next frame
# update FPS counter
frame,cnt_head = self.inference_img(img)
print(cnt_head)
cv2.imshow('result',frame)
# cv2.imwrite('test_a1.jpg',frame)
key = cv2.waitKey(0)
elif imgpath=='video':
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("failed open camera")
return 0
else:
while cap.isOpened():
_,img = cap.read()
frame,cnt_head = self.inference_img(img)
cv2.imshow('result',frame)
q=cv2.waitKey(10) & 0xFF
if q == 27 or q ==ord('q'):
break
cap.release()
cv2.destroyAllWindows()
else:
print('please input the right img-path')
class FaceAttrTest(object):
def __init__(self, args):
self.Detect_Model = MtcnnDetector(args)
self.FaceAttributeModel = FaceAttribute(args)
self.threshold = args.threshold
self.img_dir = args.img_dir
self.real_num = 0
self.save_dir = args.save_dir
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
def img_crop(self, img, bbox, imgw, imgh):
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
boxw = x2 - x1
boxh = y2 - y1
x1 = int(max(0, int(x1 - 0.3 * boxw)))
y1 = int(max(0, int(y1 - 0.3 * boxh)))
x2 = int(min(imgw, int(x2 + 0.3 * boxw)))
y2 = int(min(imgh, int(y2 + 0.3 * boxh)))
cropimg = img[y1:y2, x1:x2, :]
return cropimg
def label_show(self, img, rectangles, scores, pred_id):
'''
scores: shape-[batch,cls_nums]
pred_id: shape-[batch,cls_nums]
rectangles: shape-[batch,15]
0-5: x1,y1,x2,y2,score
'''
for idx, rectangle in enumerate(rectangles):
tmp_pred = pred_id[idx]
tmp_scores = scores[idx]
labels = np.zeros(6, dtype=np.int32)
show_scores = np.zeros(6)
labels = [
tmp_pred[0], tmp_pred[8], tmp_pred[10], tmp_pred[11],
tmp_pred[15], tmp_pred[17]
]
show_scores = [
tmp_scores[0], tmp_scores[8], tmp_scores[10], tmp_scores[11],
tmp_scores[15], tmp_scores[17]
]
p_name = ['no_bear', 'bangs', 'male', 'hat', 'glass', 'smile']
n_name = [
'bear', 'no_bangs', 'female', 'no_hat', 'no_glass', 'no_smile'
]
#p_color = [[255,0,0],[255,0,0],[255,0,0],[255,0,0],[255,0,0],[255,0,0],[255,0,0],[255,0,0]]
#n_color = [[0,0,255],[0,0,255],[0,0,255],[0,0,255],[0,0,255],[0,0,255],[0,0,255],[0,0,255]]
show_name = np.where(labels, p_name, n_name)
# show_scores = np.where(labels,show_scores,1-show_scores)
#show_color = np.where(labels,p_color,n_color)
for t_id, score in enumerate(show_name):
if labels[t_id]:
colors = (255, 0, 0)
tmp_s = show_scores[t_id]
else:
colors = (0, 0, 255)
tmp_s = 1.0 - show_scores[t_id]
score = score + '_' + '%.2f' % tmp_s
if score == 'no_bear':
colors = (0, 0, 255)
self.draw_label(img, rectangle, score, t_id, colors)
if labels[3]:
color_box = (255, 0, 0)
else:
color_box = (0, 0, 255)
self.draw_box(img, rectangle, color_box)
return img
def draw_label(self,
image,
point,
label,
mode=0,
color=(255, 255, 255),
font=cv2.FONT_HERSHEY_COMPLEX_SMALL,
font_scale=2,
thickness=2):
'''
mode: 0~7
'''
size = cv2.getTextSize(label, font, font_scale, thickness)[0]
x, y = int(point[0]), int(point[1])
w, h = int(point[2]), int(point[3])
lb_w = int(size[0])
lb_h = int(size[1])
unit = int(mode)
unit2 = int(mode + 1)
if y - int(unit2 * lb_h) <= 0:
cv2.rectangle(image, (x, y + h + unit * lb_h),
(x + lb_w, y + h + unit2 * lb_h), color)
cv2.putText(image, label, (x, y + h + unit2 * lb_h), font,
font_scale, color, thickness)
else:
cv2.rectangle(image, (x, y - unit2 * lb_h),
(x + lb_w, y - unit * lb_h), color)
cv2.putText(image, label, (x, y - unit * lb_h), font, font_scale,
color, thickness)
def draw_box(self, img, box, color=(255, 0, 0)):
cv2.rectangle(img, (int(box[0]), int(box[1])),
(int(box[2]), int(box[3])), color, 1)
'''
if len(box)>5:
if cfgs.x_y:
for i in range(5,15,2):
cv2.circle(img,(int(box[i+0]),int(box[i+1])),2,(0,255,0))
else:
box = box[5:]
for i in range(5):
cv2.circle(img,(int(box[i]),int(box[i+5])),2,(0,255,0))
'''
def inference_img(self, img):
t1 = time.time()
imgorg = img.copy()
orgreg = 0
img_out = img
pred_ids = []
if not orgreg:
rectangles = self.Detect_Model.detectFace(imgorg)
face_breathmasks = []
frame_h, frame_w = imgorg.shape[:2]
img_out = img.copy()
pred_ids = []
if len(rectangles) > 0:
for box in rectangles:
img_verify = self.img_crop(imgorg, box, frame_w, frame_h)
# img_verify = cv2.resize(img_verify,(112,112))
img_verify = Img_Pad(img_verify,
[cfg.InputSize_h, cfg.InputSize_w])
cv2.imshow('crop', img_verify)
face_breathmasks.append(img_verify)
scores, pred_ids = self.FaceAttributeModel.inference(
face_breathmasks)
img_out = self.label_show(img, rectangles, scores, pred_ids)
else:
scores, pred_ids = self.FaceAttributeModel.inference([img])
img_out = self.label_show(img, [[10, 10, 119, 119]], scores,
pred_ids)
t2 = time.time()
#print('consuming:',t2-t1)
return img_out, pred_ids
def __call__(self, imgpath):
if os.path.isdir(imgpath):
cnts = os.listdir(imgpath)
for tmp in cnts:
tmppath = os.path.join(imgpath, tmp.strip())
img = cv2.imread(tmppath)
if img is None:
continue
frame, cnt_head = self.inference_img(img)
# print('heads >> ',cnt_head)
cv2.imshow('result', frame)
savename = tmp.strip()
savepath = os.path.join(self.save_dir, savename)
cv2.imwrite(savepath, frame)
cv2.waitKey(0)
elif os.path.isfile(imgpath) and imgpath.endswith('txt'):
# if not os.path.exists(self.save_dir):
# os.makedirs(self.save_dir)
f_r = open(imgpath, 'r')
file_cnts = f_r.readlines()
for j in tqdm(range(len(file_cnts))):
tmp_file = file_cnts[j].strip()
tmp_file_s = tmp_file.split('\t')
if len(tmp_file_s) > 0:
tmp_file = tmp_file_s[0]
self.real_num = int(tmp_file_s[1])
if not tmp_file.endswith('jpg'):
tmp_file = tmp_file + '.jpg'
# tmp_path = os.path.join(self.img_dir,tmp_file)
tmp_path = tmp_file
if not os.path.exists(tmp_path):
print(tmp_path)
continue
img = cv2.imread(tmp_path)
if img is None:
print('None', tmp)
continue
frame, cnt_head = self.inference_img(img)
cv2.imshow('result', frame)
#savepath = os.path.join(self.save_dir,save_name)
#cv2.imwrite('test.jpg',frame)
cv2.waitKey(0)
elif os.path.isfile(imgpath) and imgpath.endswith(('.mp4', '.avi')):
cap = cv2.VideoCapture(imgpath)
if not cap.isOpened():
print("failed open camera")
return 0
else:
while cap.isOpened():
_, img = cap.read()
frame, cnt_head = self.inference_img(img)
cv2.imshow('result', frame)
q = cv2.waitKey(10) & 0xFF
if q == 27 or q == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
elif os.path.isfile(imgpath):
img = cv2.imread(imgpath)
if img is not None:
# grab next frame
# update FPS counter
frame, cnt_head = self.inference_img(img)
print(cnt_head)
cv2.imshow('result', frame)
# cv2.imwrite('test_a1.jpg',frame)
key = cv2.waitKey(0)
elif imgpath == 'video':
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("failed open camera")
return 0
else:
while cap.isOpened():
_, img = cap.read()
frame, cnt_head = self.inference_img(img)
cv2.imshow('result', frame)
q = cv2.waitKey(10) & 0xFF
if q == 27 or q == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
else:
print('please input the right img-path')
def evalu_img(args):
cv2.namedWindow("test")
cv2.moveWindow("test", 1400, 10)
threshold = np.array([0.5, 0.7, 0.9])
base_name = "test_img"
save_dir = './output'
crop_size = [112, 112]
#detect_model = MTCNNDet(min_size,threshold)
detect_model = MtcnnDetector(args)
#alignface = Align_img(crop_size)
imgorg = cv2.imread(args.file_in)
#img = cv2.resize(img,(640,480))
#img = cv2.cvtColor(imgorg,cv2.COLOR_BGR2RGB)
img = imgorg
h, w = img.shape[:2]
rectangles = detect_model.detectFace(img)
#draw = img.copy()
print("num:", len(rectangles))
if len(rectangles) > 0:
points = np.array(rectangles)
#print('rec shape',points.shape)
points = points[:, 5:]
#print("landmarks: ",points)
points_list = points.tolist()
# crop_imgs = alignImg(img,crop_size,points_list)
crop_imgs = alignImg_opencv(img, crop_size, points_list)
# crop_imgs = alignImg_solve(img,crop_size,points_list)
#crop_imgs = alignface.extract_image_chips(img,points_list)
# crop_imgs = alignImg_angle(img,crop_size,points_list)
for idx_cnt, img_out in enumerate(crop_imgs):
savepath = os.path.join(save_dir,
base_name + '_' + str(idx_cnt) + ".jpg")
#img_out = cv2.resize(img_out,(112,112))
cv2.imshow("crop", img_out)
cv2.waitKey(0)
cv2.imwrite(savepath, img_out)
for rectangle in rectangles:
print('w,h', rectangle[2] - rectangle[0],
rectangle[3] - rectangle[1])
score_label = str("{:.2f}".format(rectangle[4]))
cv2.putText(imgorg, score_label,
(int(rectangle[0]), int(rectangle[1])),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
cv2.rectangle(imgorg, (int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])),
(239, 185, 0), 2)
if len(rectangle) > 5:
if 1:
for i in range(5, 15, 2):
cv2.circle(
imgorg,
(int(rectangle[i + 0]), int(rectangle[i + 1])), 2,
(0, 255, 0))
else:
rectangle = rectangle[5:]
for i in range(5):
cv2.circle(imgorg,
(int(rectangle[i]), int(rectangle[i + 5])),
2, (0, 255, 0))
else:
print("No face detected")
cv2.imshow("test", imgorg)
cv2.waitKey(0)