Merge branch 'dev' into main

Author: eugenos-programos

.dockerignore

@@ -1,2 +1,3 @@
 .cache
-__pycache__
+__pycache__
+idle_models

.gitignore

@@ -4,4 +4,5 @@ images/
 models/
 env/
 __pycache__
-.env
+.env
+.vscode/

confs/configs.json

@@ -2,9 +2,6 @@
     "classes": [
         67
     ],
-    "iou_thres": 0.5,
-    "conf_thres": 0.3,
-    "model_path": "idle_models/idle_v0.3.11.pt",
     "wait_time": 10,
     "threshold": 100
 }

confs/settings.env

@@ -1,5 +1,5 @@
 username = 'admin'
 password = 'just4Taqtile'
-camera_url = 'http://192.168.1.166/onvif-http/snapshot?Profile_1'
-server_url = 'http://192.168.1.999'
-folder = 'images/192.168.1.168'
+camera_url = 'http://192.168.1.162/onvif-http/snapshot?Profile_1'
+server_url = 'http://127.0.0.1'
+folder = 'images/192.168.1.163'

functional.py

@@ -9,9 +9,7 @@
 import requests
 import httplib2
 import numpy as np
-from idle_models.ObjectDetectionModel import ObjDetectionModel
 import time
-from dotenv import load_dotenv
 
 
 def create_logger():
@@ -34,9 +32,6 @@ def create_logger():
 
 def init_connection():
     password = os.environ.get("password")
-    if password is None:
-        load_dotenv("confs/settings.env")
-        password = os.environ.get("password")
     username = os.environ.get("username")
     try:
         h = httplib2.Http(".cache")
@@ -47,20 +42,16 @@ def init_connection():
     return None
 
 
-def init_model():
-    model = ObjDetectionModel(MODEL_PATH, CONF_THRES, IOU_THRES, CLASSES)
-    return model
-
-
 def get_frame(h):
     try:
+        time_ = datetime.datetime.now()
         _, content = h.request(os.environ.get(
             "camera_url"), "GET", body="foobar")
         nparr = np.frombuffer(content, np.uint8)
-        img0 = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-        return img0
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        return img, time_
     except Exception as exc:
-        return None
+        return None, None
 
 
 def put_rectangle(img, boxes, scores):
@@ -78,25 +69,23 @@ def check_coordinates_diffs(coords_1: np.array, coords_2: np.array, threshold=TH
     return diff > threshold
 
 
-def send_report_and_save_photo(img0):
+def send_report_and_save_photo(img0, start_track_time):
     server_url = os.environ.get("server_url")
     folder = os.environ.get("folder")
 
     pathlib.Path(folder).mkdir(exist_ok=True, parents=True)
     save_photo_url = f'{folder}/' + str(uuid.uuid4()) + '.jpg'
     cv2.imwrite(save_photo_url, img0)
 
-    time_delta_seconds = WAIT_TIME
-    start_tracking = str(datetime.datetime.now())
-    time.sleep(time_delta_seconds)
+    time.sleep(WAIT_TIME)
     stop_tracking = str(datetime.datetime.now())
 
     report_for_send = {
         'camera': folder.split('/')[1],
         'algorithm': 'idle_control',
-        'start_tracking': start_tracking,
+        'start_tracking': start_track_time,
         'stop_tracking': stop_tracking,
-        'photos': [{'image': save_photo_url, 'date': start_tracking}],
+        'photos': [{'image': save_photo_url, 'date': start_track_time}],
         'violation_found': True,
     }
     try:

idle_models/Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.10
+RUN apt-get update
+RUN pip install torch==1.13.1+cpu torchvision==0.14.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+WORKDIR /var/www/5scontrol
+COPY . .
+RUN mkdir -p /usr/src/app
+EXPOSE 5000
+CMD [ "flask", "run","--host","0.0.0.0","--port","5000"]

idle_models/ObjectDetectionModel.py renamed to idle_models/IdleObjectDetectionModel.py

@@ -1,8 +1,8 @@
-from ultralytics import YOLO
 import torch
+from ultralytics import YOLO
 
 
-class ObjDetectionModel:
+class IdleObjectDetectionModel:
     def __init__(self, path: str, conf_thresh, iou_thresh, classes) -> None:
         self.model = YOLO(path)
         self.conf_thresh = conf_thresh

idle_models/app.py

@@ -0,0 +1,27 @@
+from PIL import Image
+from flask import Flask, jsonify, request
+from configs.load_configs import *
+from IdleObjectDetectionModel import IdleObjectDetectionModel
+import numpy as np
+
+
+app = Flask(__name__)
+model = IdleObjectDetectionModel(
+    MODEL_PATH,
+    CONF_THRES,
+    IOU_THRES,
+    CLASSES
+)
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if request.method == 'POST':
+        image = np.array(request.json['image']).astype(np.float32)
+        coords, confs = model(image)
+        return jsonify(
+            {
+                "coordinates": coords.tolist(),
+                "confidences": confs.tolist()
+            }
+        )

idle_models/configs/confs.json

@@ -0,0 +1,9 @@
+{
+    "classes": [
+        67
+    ],
+    "iou_thres": 0.5,
+    "conf_thres": 0.3,
+    "model_path": "idle_v0.3.11.pt",
+    "port": 5000
+}

idle_models/configs/load_configs.py

@@ -0,0 +1,10 @@
+import json
+
+
+with open("configs/confs.json", "r") as conf:
+    configs = json.load(conf)
+    CONF_THRES = configs.get("conf_thres")
+    IOU_THRES = configs.get("iou_thres")
+    MODEL_PATH = configs.get("model_path")
+    CLASSES = configs.get("classes")
+    PORT = configs.get("port")

idle_models/requirements.txt

@@ -0,0 +1,11 @@
+httplib2==0.22.0
+numpy==1.22.3
+opencv_python==4.7.0.72
+pafy==0.5.5
+Pillow==9.5.0
+pydantic==1.10.2
+python-dotenv==1.0.0
+PyYAML==6.0
+requests==2.27.1
+ultralytics==8.0.112
+Flask==2.2.2

main.py

@@ -1,33 +1,39 @@
 import time
 from functional import *
+from send_request import predict
+from dotenv import load_dotenv
 
 
+password = os.environ.get("password")
+if password is None:
+    load_dotenv("confs/settings.env")
 logger = create_logger()
 
 
 def run():
+    server_url = os.environ.get("server_url")
     while (h := init_connection()) is None:
         logger.warning("Cannot create connection")
         time.sleep(1)
-    model = init_model()
-    logger.info("Model initialized")
     prev_preds = None
     iter_idx = 0
     while True:
         iter_idx += 1
         if iter_idx % 60 == 0:
-            logger.info("60 iterations was passed")
-        if (img := get_frame(h)) is None:
+            logger.info("60 iterations passed")
+        img, start_tracking = get_frame(h)
+        start_tracking = str(start_tracking)
+        if img is None:
             logger.warning("Empty photo")
-            time.sleep(4)
+            time.sleep(1)
             continue
-        time.sleep(3)
-        preds, scores = model(img)
-        img = put_rectangle(img, preds.numpy(), scores.numpy())
+        time.sleep(2)
+        preds, scores = predict(img, server_url)
+        img = put_rectangle(img, preds, scores)
         if len(scores) > 0:
-            logger.info("Telephone was detected")
+            logger.info("Telephone is detected")
             if check_coordinates_diffs(prev_preds, preds):
-                send_report_and_save_photo(img)
+                send_report_and_save_photo(img, start_tracking)
             prev_preds = preds
 
 

requirements.txt

@@ -1,17 +1,10 @@
 colorlog==4.8.0
 httplib2==0.22.0
-matplotlib==3.6.3
 numpy==1.22.3
-onnx==1.13.1
 opencv_python==4.7.0.72
 pafy==0.5.5
-pandas==1.5.3
 Pillow==9.5.0
 pydantic==1.10.2
 python-dotenv==1.0.0
 PyYAML==6.0
 requests==2.27.1
-scipy==1.10.1
-seaborn==0.12.2
-tqdm==4.64.1
-ultralytics==8.0.112

send_request.py

@@ -0,0 +1,17 @@
+import requests
+import numpy as np
+
+
+PORT = 5000
+
+
+def predict(img: np.array, server_url: str):
+    response = requests.post(
+        f"{server_url}:{PORT}/predict",
+        json={
+            "image": img.tolist()
+        }
+    )
+    coordinates = np.array(response.json().get("coordinates"))
+    confidences = np.array(response.json().get("confidences"))
+    return [coordinates, confidences]