Addition of multi-processing features

.gitignore

@@ -2,4 +2,5 @@
 .venv/
 .idea/
 __pycache__/
-.DS_Store
+.DS_Store
+config.py

detector/detector.py

@@ -0,0 +1,123 @@
+import numpy as np
+import cv2
+import os
+import functools, operator
+
+import logging
+
+
+class Detector:
+	weights_file = None
+	config_file = None
+	names_file = None
+
+	confidence_threshold = 0
+	nms_threshold = 0
+	LABELS = []
+
+	net = None
+	ln = None
+
+	def __init__(self, model_path="model", use_gpu=False, confidence_thres=0.5, nms_thres=0.3,
+	             weights_file="clearbot.weights", config_file="clearbot.cfg", names_file="clearbot.names"):
+
+		self.confidence_threshold = confidence_thres
+		self.nms_threshold = nms_thres
+
+		self.weights_file = os.path.sep.join([os.path.dirname(os.path.realpath(__file__)), model_path, weights_file])
+		self.config_file = os.path.sep.join([os.path.dirname(os.path.realpath(__file__)), model_path, config_file])
+		self.names_file = os.path.sep.join([os.path.dirname(os.path.realpath(__file__)), model_path, names_file])
+		logging.debug("Finished initialising model file paths")
+
+		try:
+			self.LABELS = open(self.names_file).read().strip().split("\n")
+			logging.debug(f"Loaded labels from the names file: \n{self.LABELS}")
+		except Exception as e:
+			logging.error(e)
+
+		try:
+			logging.debug("Loading Darknet model")
+			self.net = cv2.dnn.readNetFromDarknet(self.config_file, self.weights_file)
+			logging.debug("Finished loading Darknet model")
+		except Exception as e:
+			logging.error(e)
+
+		if use_gpu:
+			logging.info("Will try to use GPU backend")
+			self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
+			self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
+		else:
+			logging.info("Using CPU only")
+
+		self.ln = self.net.getLayerNames()
+
+		unconnected_layers = functools.reduce(operator.iconcat, self.net.getUnconnectedOutLayers(), [])
+		logging.debug(f"Indexes of unconnected layers: {unconnected_layers}")
+		self.ln = [self.ln[i - 1] for i in unconnected_layers]
+		logging.debug(f"Output layers for YOLO are: {self.ln}")
+
+	def detect(self, frame):
+		"""
+		Detect the objects in the frame
+		:param frame: Frame that has been captured from the OpenCV video stream
+		:return: dict object with the objects and the bounding boxes
+		"""
+		(H, W) = frame.shape[:2]
+		blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
+
+		self.net.setInput(blob)
+		layer_outputs = self.net.forward(self.ln)
+
+		boxes = []
+		confidences = []
+		class_ids = []
+
+		for output in layer_outputs:
+			for detection in output:
+				scores = detection[5:]
+				class_id = np.argmax(scores)
+				confidence = scores[class_id]
+
+				if confidence > self.confidence_threshold:
+					box = detection[0:4] * np.array([W, H, W, H])
+					(centerX, centerY, width, height) = box.astype("int")
+
+					x = int(centerX - (width / 2))
+					y = int(centerY - (width / 2))
+
+					# Adding int(width) and int(height) is really important for some reason.
+					# Removing it gives an error in NMSBoxes() call
+					# Shall figure out soon and write a justification here.
+					boxes.append([x, y, int(width), int(height)])
+					confidences.append(float(confidence))
+					class_ids.append(class_id)
+
+		logging.debug((boxes, confidences, self.confidence_threshold, self.nms_threshold))
+		indexes = cv2.dnn.NMSBoxes(boxes, confidences, self.confidence_threshold, self.nms_threshold)
+		logging.debug(f"Indexes: {indexes}")
+		if len(indexes) > 0:
+			indexes = indexes.flatten()
+		return map(lambda idx: self.detected_to_result(boxes[idx], confidences[idx], class_ids[idx]), indexes)
+
+	def detected_to_result(self, box, confidence, class_id):
+		(x, y) = (box[0], box[1])
+		(w, h) = (box[2], box[3])
+
+		label = self.LABELS[class_id]
+
+		return {
+			"label": label,
+			"confidence": confidence,
+			"bbox": {
+				"x": x,
+				"y": y,
+				"width": w,
+				"height": h
+			}
+		}
+
+
+if __name__ == "__main__":
+	logging.getLogger().setLevel(logging.DEBUG)
+
+	detector = Detector()