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()

.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()

.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()

.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()

.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()

.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()

.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()

.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()