Merge pull request #10 from 5sControl/dev

Dev

confs/settings.env

@@ -1,5 +1,5 @@
 username = 'admin'
 password = 'just4Taqtile'
-camera_url = 'http://192.168.1.110:3456/onvif-http/snapshot?camera_ip=192.168.1.168'
+camera_url = 'http://192.168.1.110/images/192.168.1.169/snapshot.jpg'
 server_url = 'http://127.0.0.1'
 folder = 'images/192.168.1.163'

connection/ImageHTTPExtractor.py

@@ -15,15 +15,15 @@ def __init__(self, server_url: str, logger: logging.Logger, **credentials) -> No
 
     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
+            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
+            self.logger.error(f"Cannot retrieve image. Following error raised - {exc}")
+            return None, None

connection/ModelPredictionsReceiver.py

@@ -30,7 +30,7 @@ def predict(self, img: np.array) -> np.array:
                 }
             )
             response.raise_for_status()
-            return np.array(response.json().get("coordinates"))
+            return np.array(response.json().get("coordinates")).astype(np.float32)
         except Exception as exc:
             self.logger.critical("Cannot send request to model server. Error - {}".format(exc))
-            return np.array([])
+            return np.array([[]])

idle_model/configs/confs.json

@@ -3,7 +3,7 @@
         67
     ],
     "iou_thres": 0.5,
-    "conf_thres": 0.5,
+    "conf_thres": 0.6,
     "model_path": "weights/idle_v0.4.4.pt",
     "conf_path": "weights/yolor_csp_x.cfg",
     "port": 5001

main.py

@@ -18,7 +18,7 @@
 
 logger = utils.create_logger()
 
-prev_preds = None
+prev_preds = np.array([[]]).astype(np.float32)
 reporter = connection.IdleReporter(folder, server_url, configs["wait_time"], logger)
 image_extractor = connection.ImageHTTPExtractor(camera_url, logger, username=username, password=password)
 model_predictor = connection.ModelPredictionsReceiver(server_url, logger)
@@ -37,7 +37,7 @@
     if preds is None:
         time.sleep(1)
         continue
-    if preds.size != 0 and np.any(preds[:, -1] == 1.):
+    if preds.size != 0 and not np.any(preds == 1.):
         logger.info("Telephone is detected")
         if utils.are_bboxes_equal(prev_preds, preds, configs["threshold"]):
             img = utils.put_rectangle(img, preds[:, :4], preds[:, 4])

requirements.txt

@@ -8,3 +8,4 @@ pydantic==1.10.2
 python-dotenv==1.0.0
 PyYAML==6.0
 requests==2.27.1
+numba==0.57.1

utils/functional.py

@@ -1,5 +1,6 @@
 import cv2
 import numpy as np
+import numba
 
 
 def put_rectangle(img: cv2.Mat, boxes: list, scores: list) -> np.array:
@@ -10,8 +11,8 @@ def put_rectangle(img: cv2.Mat, boxes: list, scores: list) -> np.array:
     return img
 
 
-def are_bboxes_equal(coords_1: np.array, coords_2: np.array, threshold) -> bool:
+@numba.njit(numba.boolean(numba.float32[:, :], numba.float32[:, :], numba.float32), parallel=True)
+def are_bboxes_equal(coords_1: np.array, coords_2: np.array, threshold: float) -> bool:
     if coords_1 is None or coords_1.shape != coords_2.shape:
         return True
-    diff = np.abs(coords_1 - coords_2).sum()
-    return diff > threshold
+    return np.abs(coords_1 - coords_2).sum() > threshold