Merge pull request #8 from 5sControl/dev

Dev

.gitignore

@@ -7,4 +7,4 @@ __pycache__
 .vscode/
 build/
 cmake-build-debug/
-idle_models/weights/
+idle_model/weights/

README.md

@@ -1 +1,9 @@
-# algorithms-python
+# algorithms-python
+
+```bash
+docker build ./idle_models -t 5scontrol/idle_python_server:v-.-.-
+docker build . -t 5scontrol/idle_python:v-.-.-
+
+docker run --network host --rm 5scontrol/idle_python_server:v-.-.-
+docker run --network host --rm 5scontrol/idle_python:v-.-.-
+```

confs/__init__.py

@@ -0,0 +1,2 @@
+from .load_configs import load_configs
+configs = load_configs()

confs/configs.json

@@ -1,7 +1,4 @@
 {
-    "classes": [
-        77
-    ],
     "wait_time": 10,
     "threshold": 100
 }

confs/load_configs.py

@@ -1,11 +1,8 @@
 import json
+import os
 
 
-with open("confs/configs.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")
-    WAIT_TIME = configs.get("wait_time")
-    THRESHOLD = configs.get("threshold")
+def load_configs() -> dict:
+    with open("confs/configs.json", "r") as conf:
+        configs = json.load(conf)
+    return configs

connection/IdleReporter.py

@@ -0,0 +1,47 @@
+import os
+import time
+import logging
+import requests
+import datetime
+import uuid
+import cv2
+import numpy as np
+
+
+class IdleReporter:
+    def __init__(self, images_folder: str, server_url: str, wait_time: int, logger: logging.Logger) -> None:
+        self.images_folder = images_folder
+        self.server_url = server_url
+        self.wait_time = wait_time
+        self.logger = logger
+        os.makedirs(self.images_folder, exist_ok=True)
+
+    def _save_image(self, image: np.array) -> str:
+        save_photo_url = f'{self.images_folder}/' + str(uuid.uuid4()) + '.jpg'
+        cv2.imwrite(save_photo_url, image)
+        return save_photo_url
+
+    def create_report(self, image: np.array, start_tracking_time: datetime.time) -> dict:
+        time.sleep(self.wait_time)
+        stop_tracking_time = str(datetime.datetime.now())
+        saved_image_name = self._save_image(image)
+        report_for_send = {
+            'camera': self.images_folder.split('/')[1],
+            'algorithm': 'idle_control',
+            'start_tracking': start_tracking_time,
+            'stop_tracking': stop_tracking_time,
+            'photos': [{'image': saved_image_name, 'date': start_tracking_time}],
+            'violation_found': True,
+        }
+        return report_for_send
+
+    def send_report(self, report: dict) -> None:
+        try:
+            self.logger.info(str(report))
+            requests.post(
+                url=f'{self.server_url}:80/api/reports/report-with-photos/', json=report
+            )
+        except Exception as exc:
+            self.logger.error(f"Cannot send report. Following error raised: {str(exc)}")
+
+

connection/ImageHTTPExtractor.py

@@ -0,0 +1,29 @@
+import httplib2
+import logging
+import datetime
+import numpy as np
+import cv2
+from typing import Tuple, Union
+
+
+class ImageHTTPExtractor:
+    def __init__(self, server_url: str, logger: logging.Logger, **credentials) -> None:
+        self.http_connection = httplib2.Http(".cache")
+        self.http_connection.add_credentials(credentials.get("username"), credentials.get("password"))
+        self.logger = logger
+        self.server_url = server_url
+
+    def get_snapshot(self) -> Tuple[Union[cv2.Mat, None], Union[datetime.time, None]]:
+        try:
+                curr_time = datetime.datetime.now()
+                response, content = self.http_connection.request(
+                    self.server_url,
+                    "GET", 
+                    body="foobar"
+                )
+                nparr = np.frombuffer(content, np.uint8)
+                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+                return img, curr_time
+        except Exception as exc:
+                self.logger.error(f"Cannot retrieve image. Following error raised - {exc}")
+                return None, None

connection/ModelPredictionsReceiver.py

@@ -0,0 +1,50 @@
+import requests
+import numpy as np
+from logging import Logger
+from PIL import Image
+import io
+
+
+PORT = 5001
+
+
+class ModelPredictionsReceiver:
+    def __init__(self, server_url: str, logger: Logger) -> None:
+        self.server_url = server_url
+        self.logger = logger
+
+    @staticmethod
+    def _convert_image2bytes(image: np.array, format='PNG') -> io.BytesIO:
+        pil_image = Image.fromarray(image)
+        img_byte_arr = io.BytesIO()
+        pil_image.save(img_byte_arr, format=format)
+        img_byte_arr.seek(0)
+        return img_byte_arr
+
+    def _predict(self, img: np.array) -> np.array:
+        try:
+            response = requests.post(
+                f"{self.server_url}:{PORT}/predict",
+                files={
+                    "image": ("image", self._convert_image2bytes(img), "image/png")
+                }
+            )
+            response.raise_for_status()
+            return np.array(response.json().get("coordinates"))
+        except Exception as exc:
+            self.logger.critical("Cannot send request to model server. Error - {}".format(exc))
+            return np.array([])
+
+    def predict(self, img: np.array) -> np.array:
+        imgs = [
+            img[:img.shape[0] // 2, :, :],
+            img[img.shape[0] // 2:, :, :]
+        ]
+        preds = [self._predict(imgs[0]), self._predict(imgs[1])]
+        if len(preds[1]):
+            preds[1][:, 1] += img.shape[0] // 2
+            preds[1][:, 3] += img.shape[0] // 2
+        result = np.append(*preds)
+        if len(result.shape) == 1:
+            result = np.expand_dims(result, 0)
+        return result

connection/__init__.py

@@ -0,0 +1,3 @@
+from .IdleReporter import IdleReporter
+from .ImageHTTPExtractor import ImageHTTPExtractor
+from .ModelPredictionsReceiver import ModelPredictionsReceiver

idle_model/IdleObjectDetectionModel.py

@@ -0,0 +1,24 @@
+import torch
+import numpy as np
+import torch
+from ultralytics import YOLO
+
+
+class IdleObjectDetectionModel:
+    def __init__(self, path: str, conf_thresh, iou_thresh, classes) -> None:
+        self.model = YOLO(path)
+        self.conf_thresh = conf_thresh
+        self.iou_thresh = iou_thresh
+        self.classes = classes
+
+    @torch.no_grad()
+    def __call__(self, img: np.array) -> np.array:
+        output = self.model(
+            source=img,
+            conf=self.conf_thresh,
+            iou=self.iou_thresh,
+            max_det=600,
+            classes=self.classes,
+            verbose=False
+        )[0].boxes
+        return output.xyxy

idle_models/configs/confs.json → idle_model/configs/confs.json

@@ -3,7 +3,7 @@
         67
     ],
     "iou_thres": 0.5,
-    "conf_thres": 0.65,
-    "model_path": "./weights",
+    "conf_thres": 0.8,
+    "model_path": "weights/yolov8l.pt",
     "port": 5001
 }

idle_model/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 → idle_model/requirements.txt

@@ -9,5 +9,4 @@ PyYAML==6.0
 requests==2.27.1
 Flask==2.2.2
 colorlog==4.8.0
-transformers==4.28.1
-timm==0.6.13
+ultralytics==8.0.136