def process():
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
adaptive_resize(os.path.join(app.config['UPLOAD_FOLDER'], filename))
sys.path.append(os.path.join(os.path.dirname(__file__), 'darknet'))
import darknet.darknet as dn
#net = dn.load_net(bytes("darknet/cfg/tiny-yolo.cfg", 'ascii'), bytes("darknet/tiny-yolo.weights", 'ascii'), 0)
net = dn.load_net(bytes("darknet/cfg/yolo.cfg", 'ascii'),
bytes("darknet/yolo.weights", 'ascii'), 0)
meta = dn.load_meta(bytes("darknet/cfg/coco.data", 'ascii'))
r = dn.detect(net,
meta,
bytes(app.config['UPLOAD_FOLDER'] + filename, 'ascii'),
thresh=.5)
res = ''
for object in r:
name = object[0].decode('utf-8')
prob = float(object[1])
rct = object[2]
res += 'Detected <b>' + name + '</b> with probability of ' + str(
round(prob, 2))
#res += ' ' + str(rct)
res += '<br />'
draw_object(app.config['UPLOAD_FOLDER'] + filename, rct)
return render_template('result.html',
filename='uploads/' + filename + '?' +
str(randint(0, 999)),
result_text=res)
else:
return 'invalid data'
def init_models(basic_path):
print("\n [*]Loading object detection model!\n")
det_net = darknet.load_net(
str.encode(basic_path) + b"darknet/cfg/yolov3-food.cfg",
str.encode(basic_path) +
b"darknet/backup/food/yolov3-food_final.weights", 0)
det_meta = darknet.load_meta(
str.encode(basic_path) + b"darknet/cfg/food.data")
print("\n [*]Loading object classficiation model!\n")
classes = 231
base_model = Xception(include_top=True, input_shape=(299, 299, 3))
base_model.layers.pop()
predictions = Dense(classes,
activation='softmax')(base_model.layers[-1].output)
clf_model = Model(input=base_model.input, output=[predictions])
clf_model.load_weights(basic_path +
"classification/models/xception-0-15-0.82.h5")
class_dict = {
v: k
for k, v in np.load(basic_path +
"classification/class_index/food231.npy")[(
)].items()
}
return det_net, det_meta, clf_model, class_dict
def model_init():
net = darknet.load_net(
"/root/htsc_detect/darknet/htsc_test.cfg".encode('utf-8'),
"/root/htsc_detect/darknet/htsc.weights".encode('utf-8'), 0) #模型载入
meta = darknet.load_meta(
"/root/htsc_detect/darknet/htsc.data".encode('utf-8')) #Processor载入
return net, meta
def load_system():
try:
loaded_models = []
vehicle_threshold = .5
vehicle_weights = 'data/vehicle-detector/vehicle-detection.weights'
vehicle_netcfg = 'data/vehicle-detector/vehicle-detection.cfg'
vehicle_dataset = 'data/vehicle-detector/vehicle-detection.data'
lp_threshold = .25
lp_weights = 'data/lp-detector/lpd.weights'
lp_netcfg = 'data/lp-detector/lpd.cfg'
lp_dataset = 'data/lp-detector/lpd.data'
ocr_threshold = .4
ocr_weights = 'data/cr/cr.weights'
ocr_netcfg = 'data/cr/cr.cfg'
ocr_dataset = 'data/cr/cr.data'
vehicle_net = dn.load_net(vehicle_netcfg.encode('utf-8'),
vehicle_weights.encode('utf-8'), 0)
vehicle_meta = dn.load_meta(vehicle_dataset.encode('utf-8'))
loaded_models.append([vehicle_net, vehicle_meta, vehicle_threshold])
lp_net = dn.load_net(lp_netcfg.encode('utf-8'),
lp_weights.encode('utf-8'), 0)
lp_meta = dn.load_meta(lp_dataset.encode('utf-8'))
loaded_models.append([lp_net, lp_meta, lp_threshold])
ocr_net = dn.load_net(ocr_netcfg.encode('utf-8'),
ocr_weights.encode('utf-8'), 0)
ocr_meta = dn.load_meta(ocr_dataset.encode('utf-8'))
loaded_models.append([ocr_net, ocr_meta, lp_threshold])
return loaded_models
except:
traceback.print_exc()
sys.exit(1)
sys.exit(0)
def patch_predictor(gpu, i_queue, o_queue):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# initialize yolov3 model
thresh = 0.1
hier_thresh = 0.5
nms = 0.45
# config_file = "/home/ssd_array0/Develop/liyu/darknet/cfg/gnet2.net".encode('utf-8')
# weights_file = "/home/ssd_array0/Develop/liyu/darknet/backup/gnet2/gnet2_200000.weights".encode('utf-8')
# datacfg_file = "/home/ssd_array0/Develop/liyu/darknet/cfg/gnet2.data".encode('utf-8')
try:
net = load_net(config_file, weights_file, 0)
meta = load_meta(datacfg_file)
except:
print("[ERROR] failed to load yolov3 model")
return
while True:
item = i_queue.get()
patch = item[1]
predictions = detect_numpy(net, meta, patch, thresh, hier_thresh, nms)
predictions_ = []
for pred in predictions:
label = pred[0]
probability = pred[1]
cx, cy, w, h = pred[2]
x, y = int(cx - w / 2), int(cy - h / 2)
w, h = int(w), int(h)
w, h = min(w, w + x), min(h, h + y)
x, y = max(0, x), max(0, y)
predictions_.append([label, probability, (x, y, w, h)])
o_queue.put(
(item[0], predictions_, item[2])) # (txt_name, predictions, N)
del item
from darknet import darknet
import cv2, numpy as np
# 파이썬에서 dll 로딩하여 제공하는 함수 호출 가능하게 하는 모듈
from ctypes import *
#1. 사용할 변수 선언
config_path = "C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/train/my_yolov3.cfg"
weigh_path = "C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/train/my_yolov3_final.weights"
meta_path = "C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/train/my_data.data"
video_path = "C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/data/video/16-3_cam01_assault01_place02_night_spring.mp4"
threshold = 0.25
#2. 관련 파일 load
# cfg(모델관련)와 weight 파일 아스키코드로 load
net = darknet.load_net(bytes(config_path, "ascii"), bytes(weigh_path, "ascii"),
0)
# data(classes, train, valid, names, backup의 경로가 명시된 텍스트파일) 아스키코드로 load
meta = darknet.load_meta(bytes(meta_path, "ascii"))
# 비디오의 프레임을 추출하는 VideoCapture 함수
cap = cv2.VideoCapture(video_path)
print(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
print(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#3. opencv로 darknet 실행
# 비디오 캡쳐 객체가 정상으로 open 되는 동안 반복
while (cap.isOpened()):
# 비디오의 한 프레임씩 읽기
# 제대로 읽으면 ret=True, 읽은 프레임은 image 변수
ret, image = cap.read()
# resize하기 위해서는 픽셀 사이의 값을 결정. INTER_AREA=사이즈를 줄이기 위한 보간법
from src.utils import nms
if __name__ == '__main__':
try:
input_dir = sys.argv[1]
output_dir = input_dir
ocr_threshold = .4
ocr_weights = 'data/cr/cr.weights'
ocr_netcfg = 'data/cr/cr.cfg'
ocr_dataset = 'data/cr/cr.data'
ocr_net = dn.load_net(ocr_netcfg.encode('utf-8'),
ocr_weights.encode('utf-8'), 0)
ocr_meta = dn.load_meta(ocr_dataset.encode('utf-8'))
imgs_paths = sorted(glob('%s/*lp.png' % output_dir))
print('Performing Character Recognition...')
for i, img_path in enumerate(imgs_paths):
print('\tScanning %s' % img_path)
bname = basename(splitext(img_path)[0])
R, (width, height) = detect(ocr_net,
ocr_meta,
img_path.encode('utf-8'),
i.time = 0
i.index = -1
elif i.state == 1:
if findList(indices, i.index) == -1:
i.state = 2
i.time = time.time()
if __name__ == '__main__':
tracker = Sort()
colors = [(random.randrange(0, 255), random.randrange(0, 255),
random.randrange(0, 255)) for i in range(10)]
dn.set_gpu(0)
net = dn.load_net(
"/home/bardoe/sources/sceneLight/model/yolov3.cfg".encode("utf-8"),
"/home/bardoe/sources/sceneLight/model/yolov3.weights".encode("utf-8"),
0)
meta = dn.load_meta(
"/home/bardoe/sources/sceneLight/model/coco.data".encode("utf-8"))
cap = cv2.VideoCapture(0)
frames = 0
start = time.time()
sender.start()
sender.activate_output(1)
sender[1].multicast = True
while cap.isOpened():
if frames > 100:
frames = 0
start = time.time()
from darknet import darknet
import cv2, numpy as np, sys
from ctypes import *
net = darknet.load_net(
b"C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/cfg/myyolov3.cfg",
b"C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/weight/myyolov3_final.weights",
0)
meta = darknet.load_meta(
b"C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/weight/my.data")
cap = cv2.VideoCapture(
0
) #"C:/Users/bitcamp/anaconda3/Lib/site-packages/darknet/26-4_cam01_assault01_place01_night_spring.mp4")
print(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
print(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
i = 0
while (cap.isOpened()):
i += 1
ret, image = cap.read()
image = cv2.resize(image, dsize=(640, 480), interpolation=cv2.INTER_AREA)
print(i)
if not ret:
break
frame = darknet.nparray_to_image(image)
r = darknet.detect_image(net,
meta,
frame,
thresh=.5,
hier_thresh=.5,
nms=.45,
debug=False)
from darknet import darknet
import cv2, numpy as np, sys
from ctypes import *
net = darknet.load_net(
b"D:/python_module/darknet-master/build/darknet/x64/project/myyolov3.cfg",
b"D:/python_module/darknet-master/build/darknet/x64/project/backup/myyolov3_final.weights",
0)
meta = darknet.load_meta(
b"D:/python_module/darknet-master/build/darknet/x64/project/my.data")
cap = cv2.VideoCapture(
"D:/python_module/darknet-master/build/darknet/x64/project/22-2.mp4")
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
fps = cap.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('otter_out26-4_test.avi', fourcc, fps, (640, 480))
print(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
print(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
i = 0
while (cap.isOpened()):
i += 1
ret, image = cap.read()
image = cv2.resize(image, dsize=(640, 480), interpolation=cv2.INTER_AREA)
print(i)
if not ret:
break
frame = darknet.nparray_to_image(image)
r = darknet.detect_image(net,
meta,
frame,
thresh=.5,
from glob import glob
if __name__ == '__main__':
try:
input_dir = sys.argv[1]
output_dir = input_dir
lp_threshold = .25
lp_weights = 'data/lp-detector/lpd.weights'
lp_netcfg = 'data/lp-detector/lpd.cfg'
lp_dataset = 'data/lp-detector/lpd.data'
lp_net = dn.load_net(lp_netcfg.encode('utf-8'),
lp_weights.encode('utf-8'), 0)
lp_meta = dn.load_meta(lp_dataset.encode('utf-8'))
imgs_paths = glob('%s/*car.png' % input_dir)
print('Searching for license plates...')
for i, img_path in enumerate(imgs_paths):
print('\t Processing %s' % img_path)
bname = splitext(basename(img_path))[0]
R, _ = detect(lp_net,
lp_meta,
img_path.encode('utf-8'),
from darknet.darknet import detect
if __name__ == '__main__':
try:
input_dir = sys.argv[1]
output_dir = sys.argv[2]
vehicle_threshold = .5
vehicle_weights = 'data/vehicle-detector/vehicle-detection.weights'
vehicle_netcfg = 'data/vehicle-detector/vehicle-detection.cfg'
vehicle_dataset = 'data/vehicle-detector/vehicle-detection.data'
vehicle_net = dn.load_net(vehicle_netcfg.encode('utf-8'),
vehicle_weights.encode('utf-8'), 0)
vehicle_meta = dn.load_meta(vehicle_dataset.encode('utf-8'))
imgs_paths = image_files_from_folder(input_dir)
imgs_paths.sort()
if not isdir(output_dir):
makedirs(output_dir)
print('Searching for vehicles...')
for i, img_path in enumerate(imgs_paths):
print('\tScanning %s' % img_path)
bname = basename(splitext(img_path)[0])
def __init__(self, gpu, cfg, weights, data):
darknet.set_gpu(gpu)
self.net = darknet.load_net(str.encode(cfg), str.encode(weights), 0)
self.meta = darknet.load_meta(str.encode(data))
def to_box(r):
boxes = []
scores = []
for rc in r:
if rc[0] == b'text':
cx, cy, w, h = rc[-1]
scores.append(rc[1])
prob = rc[1]
xmin, ymin, xmax, ymax = cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
boxes.append([int(xmin), int(ymin), int(xmax), int(ymax)])
return boxes, scores
import pdb
if GPU:
try:
dn.set_gpu(GPUID)
except:
pass
net = dn.load_net(yoloCfg.encode('utf-8'), yoloWeights.encode('utf-8'), 0)
meta = dn.load_meta(yoloData.encode('utf-8'))
os.chdir(pwd)
def text_detect(img):
r = detect_np(net, meta, img, thresh=0, hier_thresh=0.5,
nms=None) ##输出所有box,与opencv dnn统一
bboxes = to_box(r)
return bboxes
