def __call__(self, opt):
exps = sorted(os.listdir('./runs'))
assert len(exps), f'Experiments not exists, please train model first'
q = "\n".join([f'[{i:>2}] {x}' for i, x in enumerate(exps)])
ipt = input(
f'Existing experiments:\n{q}\nPlease select which model to evaluate\n'
f'(0 for model0, 0,1,2 for model0,1,2): ')
ipt = '0' if ipt == '' else ipt
indexes = [int(x) for x in ipt.split(',')] if ',' in ipt else [int(ipt)]
for idx in indexes:
print('\n')
exp = exps[idx]
opt.weights = f'./runs/{exp}/weights/best.pt'
roi = int(opt.use_roi)
ssvm = int(opt.use_ssvm)
tp = f'{Path(opt.source).parent}/{exp}_sz{opt.img_size}_ROI{roi}_SSVM{ssvm}{opt.fog}' # target path
flag = 0 # 0: detect and evaluate, 1: direct evaluate
if os.path.exists(tp):
ipt = input(f'Output_path: {tp} exists, \nevaluate without detect [YES/no]: ')
if ipt in ['Yes', 'y', 'Y', 'yes', '']:
flag = 1
elif ipt in ['No', 'N', 'n']:
flag = 0
else:
raise NotImplementedError(f'input error: {ipt}')
if flag == 0:
opt.output = tp
print(f'opt: {opt}')
dp = post.DmPost(opt)
detect.run(opt, dp)
ret = self.gcm.analyse(thresh=0.5, v5_out=tp, pn_dir=Path(opt.source).parent)
time, ret = self.read_time_and_fps(tp=tp)
def main():
if len(sys.argv) >= 2 and sys.argv[1] == '-h':
exit_usage(0)
if len(sys.argv) != 4:
exit_usage(1)
net = sys.argv[1]
weight_file = sys.argv[2]
img_dir = sys.argv[3]
detect.init(net, weight_file)
files = sorted(os.listdir(img_dir))
run_time = 0
img_num = 0
for f in files:
if f.split('.')[-1] != 'jpg':
continue
file_path = img_dir + '/' + f
img = cv2.imread(file_path, 1)
time_start = time.time()
bbox = detect.run(img, img.shape[0], img.shape[1])
time_end = time.time()
run_time = run_time + time_end - time_start
img_num = img_num + 1
print("[%.6f, %.6f, %.6f, %.6f]" %
(bbox[0], bbox[1], bbox[2], bbox[3]))
draw_bbox(img, bbox)
detect.cleanup()
print("total image number: %d" % img_num)
print("frames per second of detection: %.6f" % (img_num / run_time))
def index():
print('Request-form',list(request.form.keys()),file=sys.stderr)
print('Request-form-name',request.form['name'],file=sys.stderr)
# print('Request-form-image',request.form['image'],file=sys.stderr)
image_name = request.form['name']
image_string = request.form['image']
#image_bytes = bytes(image_string,'utf-8')
#image_decoded = base64.decodestring(image_string)
image = Image.open(BytesIO(base64.b64decode(image_string)))
rotated_image = image.rotate(270,expand=True)
input_array = np.array(rotated_image)
input_array = np.expand_dims(input_array,axis=0)
result_array = detect.run(input_array)
result_image = Image.fromarray(result_array)
#print('rotated_image.shape = ',input_array.shape)
#rotated_image.save('default.jpg',format='JPEG')
#convert image back to string..
buffered = BytesIO()
result_image.save(buffered, format="JPEG")
final_img_str = base64.b64encode(buffered.getvalue())
# print('Request-files:',request.files,file=sys.stderr)
# print('Requestfiletype:',type(request.files),file=sys.stderr)
# data = request.files.to_dict()
# print('data',data,file=sys.stderr)
# #to-do Input file validation... (ensure input file is valid jpg or png)
# file = data['upload']
# print('File name:',file.filename,file=sys.stderr)
# file_path = os.path.join("Images",file.filename)
# file.save(file_path)
# print('File saved with name:',file.filename,file=sys.stderr)
#Deserialize the image..
# with open(image_name,'wb') as image_file:
# image_file.write(image)
response = final_img_str
return response
def yolov5_server_callback(self, request, response):
self.get_logger().info('Incoming request successful')
bridge = CvBridge()
cv_image = bridge.imgmsg_to_cv2(request.img, desired_encoding='bgr8')
# Check if there is a faster way to send an image to yolov5
cv2.imwrite(SRC, cv_image)
obj_roi_arr = detect.run(weights=WEIGHTS,
source=SRC,
conf_thres=CONF,
nosave=True)
print("result: {}".format(obj_roi_arr))
response.obj_roi_arr = obj_roi_arr
print("sending response ... ")
return response
roll = math.degrees(roll)
if get_num(point_dict, 60, 1) > get_num(point_dict, 72, 1):
roll = -roll
print(
f"Image: {img_name.split('/')[-1]} has the following angles:\n Pitch: {pitch:.2f}; Yaw: {yaw:.2f}; Roll: {roll:.2f}\n"
)
plot_euler_angles(pitch, yaw, roll, img_name)
if __name__ == '__main__':
model = load_angle_model('./model.pth')
parser = argparse.ArgumentParser()
parser.add_argument('--source',
type=str,
default='./data/samples',
help='source') # file or directory
parser.add_argument('--cropped',
type=str,
default='./data/cropped',
help='cropped images') # file or directory
opt = parser.parse_args()
detect.run(source=opt.source)
run_inference(model=model, opt=opt)
if os.path.exists('./data/cropped'):
shutil.rmtree('./data/cropped') # delete output folder
os.makedirs('./data/cropped') # make new output folders
if os.path.exists('./data/web'
): # delete images downloaded from web if there are any
shutil.rmtree('./data/web') # delete output folder
if name == "person":
name = "Planetarium"
resp = {}
# [x, y]
top_left, top_right = item['topLeft']
bottom_left, bottom_right = item['bottomRight']
return_string = 'There is {} in your {}.'
if top_left < half_width and bottom_left < half_width:
response += return_string.format(name, "left side")
elif top_right > half_width and bottom_right > half_width:
response += return_string.format(name, "right side")
else:
response + return_string.format(name, "center")
if name == "Planetarium":
description = get_planeteriums_events()
response += " " + description
return response
if __name__ == '__main__':
try:
server_port = int(sys.argv[1])
except Exception:
server_port = 8080
run()
if not os.path.isdir('saved_images'):
os.mkdir('saved_images')
app.run(debug=True, host="0.0.0.0")
from PIL import Image
from flask import Flask, request, send_file, jsonify, render_template, url_for
from flask_cors import CORS
from werkzeug.utils import secure_filename
import detect
UPLOAD_FOLDER = '/upload'
ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}
app = Flask(__name__)
CORS(app)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
run = detect.run()
net = run.load_model(model_name='u2net')
logging.basicConfig(level=logging.INFO)
# @app.route("/", methods=["GET"])
# def hello():
# return 'Hello guys'
@app.route("/", methods=['GET', 'POST'])
def upload_file():
return render_template('upload.html')
@app.route("/remove", methods=["GET", "POST"])
def run():
url = "http://60.214.100.134:10001/artemis-web/debug/"
path_unhanded = 'unhanded/100121/'
path_handed = 'ai-dispose/100121/'
dict = {
"appKey": "23189490",
"appSecret": "k0CzWXJUOBH0yMlq9D31",
"contentType": "application/x-www-form-urlencoded;charset=UTF-8",
"headers": "{}",
"httpMethod": "GET",
"mock": "false",
"parameter": "{}",
"path": "/api/mss/v1/hls/37046803001310516439",
"query": "{}"
}
i = 0
req1 = requests.post(url, dict)
jsession1 = json.loads(req1.text)
nurl = jsession1['data'].replace("10.141.71.28:83", "60.214.100.134:10001")
#print(nurl)
mysql = pymysql.connect(host='127.0.0.1',
user='******',
password='******',
port='3306',
db='Xztest')
cursor = mysql.cursor()
while True:
# start_time = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '9:00', '%Y-%m-%d%H:%M')
# end_time = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '9:30', '%Y-%m-%d%H:%M')
# now_time = datetime.datetime.now()
# if start_time<now_time<end_time:
try:
consiquent_fail = 0 # 连续失败帧数
max_consiquent_fail = 50 # 最大连续失败帧数
while True:
cap = cv2.VideoCapture(nurl)
ret, frame = cap.read()
# 取流失败
if not ret:
consiquent_fail += 1
if consiquent_fail >= max_consiquent_fail:
break # 失败过量则重连 cap
# 取流成功
else:
for j in range(3):
cur_time = int(round(time.time() * 1000))
standart_time = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
save_path = path_unhanded + cur_time + ".jpg"
cv2.imwrite(save_path, frame[:, :, ::-1])
res = detect.run(weights='weights/best.pt',
source=save_path,
project='ai-dispose',
name='100121')
# if len(res):
# sql = 'insert into t_ai_img values (%s,%s,%s,%s)'
# param = ('ai-dipose/100121/'+cur_time+'.jpg','煤堆未覆盖',standart_time,cur_time)
# cursor.execute(sql,param)
# mysql.commit()
for img in res:
cur_time = int(round(time.time() * 1000))
standart_time = time.strftime(
"%Y-%m-%d %H:%M:%S", time.localtime())
size = os.path.getsize('ai-dispose/100121/' +
img) / 1024
sql = 'insert into t_ai_img(video_no, label, img_url, create_time, video_url, img_no, size, update_time) values (%s,%s,%s,%s,%s,%s,%s,%s)'
param = ('37046803001310516439', '裸漏煤堆/物料堆场扬尘识别',
'1111', standart_time, '2222', cur_time,
size, standart_time)
cursor.execute(sql, param)
mysql.commit()
i = i + 1
time.sleep(60)
time.sleep(1800)
break
except cv2.error as err:
print(err)
continue
mysql.close()
cursor.close()
except cv2.error as err:
print(err)
continue
mysql.close()
cursor.close()
mysql = pymysql.connect(host='127.0.0.1',
user='******',
password='******',
port=3306,
db='Xztest')
cursor = mysql.cursor()
res = detect.run(weights='weights/best.pt',
source='unhanded',
project='ai-dispose',
name='100121')
print(res)
# if len(res):
# sql = 'insert into t_ai_img values (%s,%s,%s,%s)'
# param = ('ai-dipose/100121/'+cur_time+'.jpg','煤堆未覆盖',standart_time,cur_time)
# cursor.execute(sql,param)
# mysql.commit()
for img in res:
cur_time = int(round(time.time() * 1000))
standart_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
size = os.path.getsize('ai-dispose/100121/' + img) / 1024
sql = 'insert into t_ai_img(video_no, label, img_url, create_time, video_url, img_no, size, update_time) values (%s,%s,%s,%s,%s,%s,%s,%s)'
param = ('37046803001310516439', '裸漏煤堆/物料堆场扬尘识别', '1111', standart_time,
# load_video(opt.video_path)
load_video(video_path)
for file in os.scandir('output/overwatch/'):
os.remove(file.path)
print("-- Complete get frames")
print(
"-----------------------------------------------------------------------------------"
)
# run YOLO
print("\n-- Write name of weights file: weights/", end='')
file = input()
file_path = f"weights/{file}"
detect.run(file_path)
os.system(f"python detect.py --weights_path {file_path}")
print(
"-----------------------------------------------------------------------------------"
)
# get features
print("\n-- Getting features ...")
get_features.cal_dist("output", 960.0, 540.0)
print("-- Complete get features")
print(
"-----------------------------------------------------------------------------------"
)
# classify
print("\n-- Classifying ...")
