def receiver(client, addr):
global counter
counter += 1 # 스레드 별로 imshow에서 사용할 타이틀을 다르게 하기 위해서
frame_name = f'frame{counter}'
reader = client.makefile('rb')
writer = client.makefile('wb')
while True:
data, data_len = net2.receive(reader)
if not data:
break
# print('received ', data_len) # 이미지 처리
# AI 알고리즘 처리 - 불량 여부 판단... 여기에 넣으면됭
show_image(data, frame_name)
result = json.dumps({'result': 'ok'})
net2.send(writer, result.encode())
print('exit receiver')
def receiver(client, addr):
reader = client.makefile('rb')
# writer = client.makefile('wb')
while True:
data, data_len = net2.receive(reader)
print('-------수신 ')
if not data_len:
return
# data : jpeg 이미지
# image : bgr 이미지
ret, image = show_image(data)
# AI 알고리즘 처리
cv2.imshow('image', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
print('exit receiver')
def vido_streaming():
with socket.socket() as s:
s.connect((HOST,PORT))
writer = s.makefile('wb')
reader = s.makefile('rb')
stream=io.BytesIO()
for _ in camera.capture_continuous(stream,'jpeg',use_video_port=True):
image=stream.getvalue()
net2.send(writer,image)
result=net2.receive(reader)[0]
stream.seek(0)
stream.truncate()
if not button.is_pressed:
writer.write(struct.pack('<L',0))
writer.flush()
break
def receiver(client, addr):
global count
# count += 1
filename = count
reader = client.makefile('rb')
# writer = client.makefile('wb')
data, data_len = net2.receive(reader)
# print('-------수신 ')
if not data_len:
return
# data : jpeg 이미지
# image : bgr 이미지
image, key = show_image(data)
# cv2.imwrite("test" + str(count) + ".jpg" , image)
# print(image)
# AI 알고리즘 처리
cv2.imshow(str(filename), image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def receiver(client, addr , model):
print('\n\n\n#####################################################\n')
asyncio.set_event_loop(asyncio.new_event_loop())
# �ð� �������� ��������
current_time = datetime.now() + timedelta(hours=9)
current_time = current_time.strftime("%m%d%H%M%S.%f")[:-3]
current_time = current_time.replace("." , "")
print("#####################")
print(current_time)
print("#####################")
###############################
# 1. �̹��� ���� �� ���� #
###############################
print("="*40)
print('Receiver start')
print("="*40)
reader = client.makefile('rb')
data, data_len = net2.receive(reader)
if not data_len:
return
image, key = show_image(data)
# ī�� ��Ʈ �̹���
file_name = "test"
cv2.imwrite("./" + file_name + ".jpg" , image)
###############################
# 2. AI - �з� #
###############################
## YOLO ����
NAME_CFG = "yolov4_CASHER_v6.cfg"
NAME_WEIGHT = "yolov4_CASHER_v6_best.weights"
os.system('./darknet detector test data/obj.data ' + NAME_CFG + " " + NAME_WEIGHT + " test.jpg" + " -thresh 0.5 -out result.json")
os.system('mv predictions.jpg ./predictions/' + "result_" + current_time + ".jpg")
os.system('mv result.json ./predictions/' + "result_" + current_time + ".json")
## OBJECT DETECTION ���
json_file_name = "result_" + current_time + ".json"
with open('./predictions/' + json_file_name , 'r') as f:
json_data = json.load(f)
print(json_data)
THRESHOLD = 0.7
ITEM_LST = []
RESULT = 1
for item_data in json_data[0]["objects"]:
item_dict = {}
item_dict["id"] = item_data["name"]
item_dict["prob"] = item_data["confidence"]
item_dict["outcome"] = 1
if item_dict["prob"] > THRESHOLD :
item_dict["outcome"] = 1
else:
item_dict["outcome"] = 0
RESULT = 0
ITEM_LST.append(item_dict)
# ###############################
# # 3. Ŭ���� ���� �۽� #
# ###############################
print("="*40)
print('Send Server start')
print("="*40)
## 1. REST_API >> S3 ���
REST_API_DICT = {}
REST_API_DICT["result"] = RESULT
REST_API_DICT["time"] = current_time
REST_API_DICT["item"] = ITEM_LST
response_1 = send_REST_API(REST_API_DICT)
print("REST-API SERVER")
print(response_1)
## 3. BOTO3 >> S3 ���
if RESULT == False:
response_2 = send_S3( "./predictions/" + "result_" + current_time + ".jpg" , "result_" + current_time + ".jpg" )
print("BOTO3 SERVER")
print(response_2)
## 4. WEBSOCKET >> WEB ���
WEB_DICT = {}
WEB_LST = []
for item in ITEM_LST:
item_dict = {}
item_dict["id"] = YOLO_CONVERT_INDICATOR[item["id"]]
item_dict["name"] = DF[DF["Item_id"]==int(item_dict["id"])]["Product_name"].values[0]
item_dict["price"] = int(DF[DF["Item_id"]==int(item_dict["id"])]["Price"].values[0])
item_dict["Qty"] = 1
WEB_LST.append(item_dict)
WEB_DICT["item"] = WEB_LST
asyncio.get_event_loop().run_until_complete(connect(WEB_DICT))
def receiver(client, addr , model):
#print('\n\n\n#####################################################\n')
asyncio.set_event_loop(asyncio.new_event_loop())
###############################
# 1. 이미지 수신 및 저장 #
###############################
print('\n\n\n')
print("="*100)
print('Receiver start')
print("="*100)
reader = client.makefile('rb')
data, data_len = net2.receive(reader)
if not data_len:
return
image, key = show_image(data)
# 현재 시간 기준으로 파일명 저장
current_time = time.time()
current_ms = str(int(round(current_time * 1000)))[-3:]
current_time = time.strftime(f'%m%d%H%M%S{current_ms}', time.localtime(current_time))
file_name = "result_" + current_time + '.jpg' # result_0601135334123.jpg (6월 1일 13시 53분 34.123초)
print(f'\n#\n# File Name : {file_name}\n#\n')
cv2.imwrite("./History/" + file_name, image)
###############################
# 2. AI - 분류 #
###############################
THRESHHOLD = 0.9
# 이미지 Resize
input_arr = resize(image, (200,200)) / 255
input_arr = np.array([input_arr]) # Convert single image to a batch.
input_arr = input_arr[:,:,:,::-1] # GBR to RGB
# Model predict
prob = MODEL.predict(input_arr)[0]
pred = prob.argmax()
prob = prob[pred]
# 더미값
primary_key = int(indicate_key[pred])
result = 1 if prob > THRESHHOLD else 0
print('\n#\n# 예측 결과\n#\n')
print(f'Predict Index : {pred} name : {indicate[pred]}')
print(f'Probability : {prob}')
print(f'Primary Key : {primary_key}\n')
###############################
# 3. 클라우드 서버 송신 #
###############################
## 1. SOCKET >> IOT 통신
if FAIL_IOT:
print('Start IoT socket...')
sendData = str(result)
connectionSock.send(sendData.encode('utf-8'))
connectionSock.send(sendData.encode('utf-8'))
# 클라우드 서버에 송신할 데이터 생성
send_data = {'result' : result , 'time' : current_time , 'item' : [] }
result_item = {
"outcome": result,
"id": str(primary_key), # Type : string
"prob": int(prob*100)
}
## REST-API 보내는 데이터
send_data['item'].append(result_item)
ITEM_DICT = {'item' : []}
item_info = DF[DF['Item_id'] == primary_key] # SELECT * FROM table WHERE Item_id = primary_key
temp_dict = {
'id' : primary_key,
'name' : item_info['Product_name'].values[0],
'price' : int(item_info['Price'].values[0]),
'Qty' : 1
}
ITEM_DICT['item'].append(temp_dict)
if result :
asyncio.get_event_loop().run_until_complete(connect(ITEM_DICT))
# 서버 송신 (분류 성공 >> REST-API , 분류 실패 >> REST-API / S3)
response = send_REST_API(send_data)
if not result : send_S3(file_name) # 분류 실패 시 S3에 이미지파일도 전송
print(f'서버 송신 결과 : {response}')