def test(stage, testFolder):
print("Start testing in %s"%(testFolder))
detectors = [None, None, None]
if stage in ['pnet', 'rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/pnet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('pnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a)) # auto match a max epoch model
modelPath = os.path.join(modelPath, "pnet-%d"%(maxEpoch))
print("Use PNet model: %s"%(modelPath))
detectors[0] = FcnDetector(P_Net,modelPath)
if stage in ['rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/rnet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('rnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d"%(maxEpoch))
print("Use RNet model: %s"%(modelPath))
detectors[1] = Detector(R_Net, 24, 1, modelPath)
if stage in ['onet']:
modelPath = os.path.join(rootPath, 'tmp/model/onet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('onet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "onet-%d"%(maxEpoch))
print("Use ONet model: %s"%(modelPath))
detectors[2] = Detector(O_Net, 48, 1, modelPath)
mtcnnDetector = MtcnnDetector(detectors=detectors, min_face_size = 24, threshold=[0.9, 0.6, 0.7])
testImages = []
for name in os.listdir(testFolder):
testImages.append(os.path.join(testFolder, name))
testDatas = TestLoader(testImages)
# Now to detect
allBoxes, allLandmarks = mtcnnDetector.detect_face(testDatas)
print("\n")
# Save it
for idx, imagePath in enumerate(testImages):
savePath = os.path.join(rootPath, 'testing', 'results_%s'%(stage))
if not os.path.isdir(savePath):
os.makedirs(savePath)
image = cv2.imread(imagePath)
save_bboxes(savePath,idx,image,allBoxes[idx])
for bbox in allBoxes[idx]:
cv2.putText(image,str(np.round(bbox[4],2)),(int(bbox[0]),int(bbox[1])),cv2.FONT_HERSHEY_TRIPLEX,1,color=(255,0,255))
cv2.rectangle(image, (int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])),(0,0,255))
allLandmark = allLandmarks[idx]
if allLandmark is not None: # pnet and rnet will be ignore landmark
for landmark in allLandmark:
for i in range(len(landmark)/2):
cv2.circle(image, (int(landmark[2*i]),int(int(landmark[2*i+1]))), 3, (0,0,255))
cv2.imwrite(os.path.join(savePath, "result_%d.jpg" %(idx)), image)
print("Save image to %s"%(savePath))
def main(args):
'''通过PNet或RNet生成下一个网络的输入'''
size = args.input_size
batch_size = config.batches
min_face_size = config.min_face
stride = config.stride
thresh = config.thresh
#模型地址
model_path = ['../model/PNet/', '../model/RNet/', '../model/ONet']
if size == 12:
net = 'PNet'
save_size = 24
elif size == 24:
net = 'RNet'
save_size = 48
# 图片数据地址
base_dir = 'g:/mtcnn-dataset/data/WIDER_train/'
# 处理后的图片存放地址
data_dir = 'g:/mtcnn-dataset/data/%d' % (save_size)
neg_dir = os.path.join(data_dir, 'negative')
pos_dir = os.path.join(data_dir, 'positive')
part_dir = os.path.join(data_dir, 'part')
for dir_path in [neg_dir, pos_dir, part_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
detectors = [None, None, None]
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
if net == 'RNet':
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
filename = '../data/wider_face_train_bbx_gt.txt'
#读取文件的image和box对应函数在utils中
data = read_annotation(base_dir, filename)
mtcnn_detector = MtcnnDetector(detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh)
save_path = data_dir
save_file = os.path.join(save_path, 'detections.pkl')
if not os.path.exists(save_file):
#将data制作成迭代器
print('载入数据')
test_data = TestLoader(data['images'])
detectors, _ = mtcnn_detector.detect_face(test_data)
print('完成识别')
with open(save_file, 'wb') as f:
pickle.dump(detectors, f, 1)
print('开始生成图像')
save_hard_example(save_size, data, neg_dir, pos_dir, part_dir, save_path)
def test(stage, testFolder):
print("Start testing in %s"%(testFolder))
detectors = [None, None, None]
if stage in ['pnet', 'rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/pnet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('pnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a)) # auto match a max epoch model
modelPath = os.path.join(modelPath, "pnet-%d"%(maxEpoch))
print("Use PNet model: %s"%(modelPath))
detectors[0] = FcnDetector(P_Net,modelPath)
if stage in ['rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/rnet/model/middle/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('rnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d"%(maxEpoch))
print("Use RNet model: %s"%(modelPath))
detectors[1] = Detector(R_Net, 24, 1, modelPath)
if stage in ['onet']:
modelPath = os.path.join(rootPath, 'tmp/onet/model/small/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('onet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "onet-%d"%(maxEpoch))
print("Use ONet model: %s"%(modelPath))
detectors[2] = Detector(O_Net, 48, 1, modelPath)
mtcnnDetector = MtcnnDetector(detectors=detectors, min_face_size =12, threshold=[0.6, 0.6, 0.7],scale_factor=0.7)
testImages = []
for name in os.listdir(testFolder):
testImages.append(os.path.join(testFolder, name))
print("\n")
right_num=0
miss_num=0
FN=0
# Save it
for idx, imagePath in enumerate(testImages):
if(idx<=6000):
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
print(right_num,FN,miss_num)
try:
allBoxes, allLandmarks = mtcnnDetector.detect_face([image])
if(allBoxes.__len__()==1):
right_num+=1
else:
FN+=(allBoxes.__len__()-1)
except:
miss_num+=1
pass
else:
break
def test(stage):
detectors = [None, None, None]
if stage in ['pnet', 'rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/pnet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('pnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a)) # auto match a max epoch model
modelPath = os.path.join(modelPath, "pnet-%d"%(maxEpoch))
print("Use PNet model: %s"%(modelPath))
detectors[0] = FcnDetector(P_Net,modelPath)
if stage in ['rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/rnet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('rnet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d"%(maxEpoch))
print("Use RNet model: %s"%(modelPath))
detectors[1] = Detector(R_Net, 24, 1, modelPath)
if stage in ['onet']:
modelPath = os.path.join(rootPath, 'tmp/model/onet/')
a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('onet-') and b.endswith('.index')]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "onet-%d"%(maxEpoch))
print("Use ONet model: %s"%(modelPath))
detectors[2] = Detector(O_Net, 48, 1, modelPath)
mtcnnDetector = MtcnnDetector(detectors=detectors, min_face_size =50, threshold=[0.8, 0.8, 0.9],scale_factor=0.4)
cap = cv2.VideoCapture(0)
while(True):
testImages = []
ret, image = cap.read()
image=cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
testImages.append(image)
# Now to detect
starttime=time.time()
allBoxes, allLandmarks = mtcnnDetector.detect_face(testImages)
# print("\n")
# Save it
# print(time.time()-starttime)
for bbox in allBoxes[0]:
cv2.putText(image,str(np.round(bbox[4],2)),(int(bbox[0]),int(bbox[1])),cv2.FONT_HERSHEY_TRIPLEX,1,color=(255,0,255))
cv2.rectangle(image, (int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])),(0,0,255))
allLandmark = allLandmarks[0]
if allLandmark is not None: # pnet and rnet will be ignore landmark
for landmark in allLandmark:
for i in range(int(len(landmark)/2)):
cv2.circle(image, (int(landmark[2*i]),int(int(landmark[2*i+1]))), 3, (0,0,255))
cv2.imshow("test", image)
c = cv2.waitKey(1) & 0xFF
if c == 27 or c == ord('q'):
break
def test_net(batch_size, stage, thresh, min_face_size, stride):
print(">>>>>> Detect bbox for %s..." % (stage))
detectors = [None, None, None]
if stage in ["rnet", "onet"]:
modelPath = os.path.join(rootPath, 'tmp/model/pnet/')
# 第几个checkpoint
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('pnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "pnet-%d" % (maxEpoch))
print("Use PNet model: %s" % (modelPath))
PNet = FcnDetector(P_Net, modelPath)
detectors[0] = PNet
if stage in ["onet"]:
modelPath = os.path.join(rootPath, 'tmp/model/rnet/')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('rnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d" % (maxEpoch))
print("Use RNet model: %s" % (modelPath))
RNet = Detector(R_Net, 24, batch_size, modelPath)
detectors[1] = RNet
# read annatation(type:dict)
widerImagesPath = os.path.join(rootPath, "dataset", "WIDER_train",
"images")
annoTxtPath = os.path.join(rootPath, "dataset",
"wider_face_train_bbx_gt.txt")
# data['images'], data['bboxes']
data = read_wider_annotation(widerImagesPath, annoTxtPath)
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh)
test_data = TestLoader(data['images'])
# do detect
detections, _ = mtcnn_detector.detect_face(test_data)
# save detect result
save_path = os.path.join(rootPath, "tmp/data", stage)
if not os.path.exists(save_path):
os.makedirs(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print("\nDone! Start to do OHEM...")
__save_data(stage, data, save_path)
def test(stage, profiling):
print("Start Detecting")
detectors = [None, None, None]
if stage in ['pnet', 'rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/pnet/')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('pnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a)) # auto match a max epoch model
modelPath = os.path.join(modelPath, "pnet-%d" % (maxEpoch))
print("Use PNet model: %s" % (modelPath))
detectors[0] = FcnDetector(P_Net, modelPath, profiling)
if stage in ['rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/model/rnet/')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('rnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d" % (maxEpoch))
print("Use RNet model: %s" % (modelPath))
detectors[1] = Detector(R_Net, 24, 1, modelPath, profiling)
if stage in ['onet']:
modelPath = os.path.join(rootPath, 'tmp/model/onet/')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('onet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "onet-%d" % (maxEpoch))
print("Use ONet model: %s" % (modelPath))
detectors[2] = Detector(O_Net, 48, 1, modelPath, profiling)
mtcnnDetector = MtcnnDetector(detectors=detectors,
min_face_size=24,
threshold=[0.9, 0.6, 0.7])
# Now to detect
camID = 0
cap = cv2.VideoCapture(camID)
while True:
ret, image = cap.read()
if ret == 0:
break
[h, w] = image.shape[:2]
print(h, w)
#image_data = cv2.flip(image, 1)
#image_data = cv2.flip(image, 1)
image_data = image
start_time = time.time()
testImages = []
testImages.append(image_data)
allBoxes, allLandmarks = mtcnnDetector.detect_face(testImages)
inf_time = time.time() - start_time
print("inference time(s): {}".format(inf_time))
del testImages[0]
#print("allBoxes: {}".format(allBoxes))
#print("allLandmarks: {}".format(allLandmarks))
#print("\n")
# Save it
if (len(allBoxes) >= 1):
for idx, bbox in enumerate(allBoxes):
cv2.putText(image_data,
str(np.round(bbox[idx][4], 2)),
(int(bbox[idx][0]), int(bbox[idx][1])),
cv2.FONT_HERSHEY_TRIPLEX,
1,
color=(255, 0, 255))
cv2.rectangle(image_data,
(int(bbox[idx][0]), int(bbox[idx][1])),
(int(bbox[idx][2]), int(bbox[idx][3])),
(0, 0, 255))
allLandmark = allLandmarks[idx][0].tolist()
total_landmark_pts = len(allLandmark)
if allLandmark is not None and len(
allLandmark
) == 10: # pnet and rnet will be ignore landmark
for index, landmark in enumerate(allLandmark):
for i in range(int(total_landmark_pts / 2)):
cv2.circle(image_data,
(int(allLandmark[2 * i]),
int(int(allLandmark[2 * i + 1]))), 3,
(255, 255, 255))
cv2.imshow('Face/Landmark Detection', image_data)
k = cv2.waitKey(1) & 0xff
if k == ord('q') or k == 27:
break
cap.release()
def test(stage, testFolder):
print("Start testing in %s" % (testFolder))
detectors = [None, None, None]
if stage in ['pnet', 'rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/origin/model/pnet')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('pnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a)) # auto match a max epoch model
modelPath = os.path.join(modelPath, "pnet-%d" % (maxEpoch))
print("Use PNet model: %s" % (modelPath))
detectors[0] = FcnDetector(P_Net, modelPath)
if stage in ['rnet', 'onet']:
modelPath = os.path.join(rootPath, 'tmp/origin/model/rnet')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('rnet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "rnet-%d" % (maxEpoch))
print("Use RNet model: %s" % (modelPath))
detectors[1] = Detector(R_Net, 24, 1, modelPath)
if stage in ['onet']:
modelPath = os.path.join(rootPath, 'tmp/origin/model/onet')
a = [
b[5:-6] for b in os.listdir(modelPath)
if b.startswith('onet-') and b.endswith('.index')
]
maxEpoch = max(map(int, a))
modelPath = os.path.join(modelPath, "onet-%d" % (maxEpoch))
print("Use ONet model: %s" % (modelPath))
detectors[2] = Detector(O_Net, 48, 1, modelPath)
mtcnnDetector = MtcnnDetector(detectors=detectors,
min_face_size=50,
threshold=[0.8, 0.9, 0.9])
fileFoldName = "faceListInt.txt"
outFilename = 'F:/software/yansan/MTCNN-on-FDDB-Dataset-master/FDDB-folds/' + 'predict.txt' # fileOutName
foldFilename = 'F:/software/yansan/MTCNN-on-FDDB-Dataset-master/FDDB-folds/' + fileFoldName
prefixFilename = 'E:/database/FDDB_Face Detection Data Set and Benchmark/'
fout = open(outFilename, 'a+')
f = open(foldFilename, 'r') # FDDB-fold-00.txt, read
for imgpath in tqdm(f.readlines()):
testImages = []
imgpath = imgpath.split('\n')[0]
# foutOnce.write(imgpath+'\n')
# foutFold.write(imgpath+'\r')
img = cv2.imread(prefixFilename + imgpath + '.jpg')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if img is None:
continue
testImages.append(img)
boundingboxes, points = mtcnnDetector.detect_face(testImages)
# boundingboxes, points = demo.detect_face(img_matlab, minsize, PNet, RNet, ONet, threshold, False, factor)
text1 = str(imgpath) + '\n' + str(len(boundingboxes[0])) + '\n'
fout.write(text1) # FDDB-fold-%02d-out.txt or predict.txt
for bbox in boundingboxes[0]:
# print(bbox,"???")
text2 = str(int(bbox[0])) + ' ' + str(int(bbox[1])) + ' ' \
+ str(abs(int(bbox[2] - bbox[0]))) + ' ' \
+ str(abs(int(bbox[3] - bbox[1]))) + ' ' \
+ str(bbox[4]) + '\n'
fout.write(text2) # FDDB-fold-%02d-out.txt or predict.txt
# text2 = str(int(boundingboxes[coordinate][0][0]))
# fout.write(text2) # FDDB-fold-%02d-out.txt or predict.txt
# print error
f.close() # input the fold list, FDDB-fold-00.txt
fout.close() # output the result, predict.txt
# load onet model
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
gt_imdb = []
path = "xixi"
for item in os.listdir(path):
gt_imdb.append(os.path.join(path, item))
test_data = TestLoader(gt_imdb)
all_boxes, landmarks = mtcnn_detector.detect_face(test_data)
count = 0
for imagepath in gt_imdb:
image = cv2.imread(imagepath)
for bbox in all_boxes[count]:
cv2.rectangle(image, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 0, 255))
print(imagepath)
count = count + 1
cv2.imwrite("{}.jpg".format(count), image)
#cv2.imwrite("result_landmark/%d.png" %(count),image)
cv2.imshow("lala", image)
cv2.waitKey(0)