Added parking event testing tools

tools/parked_event_motion_detect_test.py

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+# This script allows you to test your motion detection settings with a real-time video stream.
+
+import os # Required to interact with certain operating system functions
+import json # Required to process JSON data
+import cv2 # Required to capture video.
+
+predator_root_directory = str(os.path.dirname(os.path.realpath(__file__))) # This variable determines the folder path of the root Predator directory. This should usually automatically recognize itself, but it if it doesn't, you can change it manually.
+
+try:
+    if (os.path.exists(predator_root_directory + "/../config.json")):
+        config = json.load(open(predator_root_directory + "/../config.json")) # Load the configuration database from config.json
+    else:
+        print("The configuration file doesn't appear to exist at " + predator_root_directory + "/../config.json.")
+        exit()
+except:
+    print("The configuration database couldn't be loaded. It may be corrupted.")
+    exit()
+
+device = "main"
+
+if (device not in config["dashcam"]["capture"]["video"]["devices"]):
+    print("The specified device does not exist in the configuration. Be sure to change the 'device' variable in the motion_detect_test.py file to the device you want to test.")
+    exit()
+
+resolution = [config["dashcam"]["capture"]["video"]["devices"][device]["resolution"]["width"], config["dashcam"]["capture"]["video"]["devices"][device]["resolution"]["height"]] # This determines the resolution that will be used for the video capture device.
+#capture = cv2.VideoCapture(config["dashcam"]["capture"]["video"]["devices"][device]["index"]); # Open the video capture device.
+capture = cv2.VideoCapture("/home/cvieira/Downloads/NearbyLightning.m4v"); # Open the video capture device.
+codec = list(config["dashcam"]["capture"]["video"]["devices"][device]["codec"])
+capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(codec[0], codec[1], codec[2], codec[3])) # Set the codec to be MJPEG.
+capture.set(cv2.CAP_PROP_FRAME_WIDTH,resolution[0]) # Set the video stream width.
+capture.set(cv2.CAP_PROP_FRAME_HEIGHT,resolution[1]) # Set the video stream height.
+
+background_subtractor = cv2.createBackgroundSubtractorMOG2()
+
+total_area = resolution[0] * resolution[1] # Calculate the total area of the frame.
+
+output = cv2.VideoWriter("./predator_motion_detect_test.avi", cv2.VideoWriter_fourcc(*'XVID'), 16, (resolution[0], resolution[1])) # Update the video output.
+
+while True:
+    ret, frame = capture.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    fgmask = background_subtractor.apply(gray)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    fgmask = cv2.erode(fgmask, kernel, iterations=1)
+    fgmask = cv2.dilate(fgmask, kernel, iterations=1)
+    contours, hierarchy = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    moving_area = 0
+    for contour in contours:
+        moving_area += cv2.contourArea(contour)
+        if cv2.contourArea(contour) > 10:
+            x, y, w, h = cv2.boundingRect(contour)
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+
+    moving_percentage = moving_area / total_area # Calculate the percentage of the frame that is in motion.
+    moving_percentage_human = "{:.5f}%".format(moving_percentage*100) # Convert the moving percentage to a human-readable string.
+    if (moving_area > 0): # Check to see if there is any movement at all.
+        if (moving_percentage > float(config["dashcam"]["parked"]["event"]["trigger_motion"]["sensitivity"])): # Check to see if there is movement that exceeds the sensitivity threshold.
+            print(str(moving_area) + "\t(" + str(format(moving_percentage_human)) + ")\tTriggered") # Display the movement as both a number and a percentage.
+        else:
+            print(str(moving_area) + "\t(" + str(format(moving_percentage_human)) + ")") # Display the movement as both a number and a percentage.
+
+    cv2.putText(frame, moving_percentage_human, (10, 30), 2, 0.8, (0, 0, 0)) # Add the main overlay stamp to the video stream.
+    output.write(frame) # Save this frame to the video file.
+    cv2.imshow('Motion Detection', frame)
+
+    if cv2.waitKey(1) == ord('q'):
+        break
+
+capture.release()
+cv2.destroyAllWindows()

tools/parked_event_object_recognition_test.py

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+# This script allows you to test your object recognition settings with a real-time video stream.
+
+import os # Required to interact with certain operating system functions
+import json # Required to process JSON data
+import cv2 # Required to capture video.
+from ultralytics import YOLO
+import numpy
+
+predator_root_directory = str(os.path.dirname(os.path.realpath(__file__))) # This variable determines the folder path of the root Predator directory. This should usually automatically recognize itself, but it if it doesn't, you can change it manually.
+
+try:
+    if (os.path.exists(predator_root_directory + "/../config.json")):
+        config = json.load(open(predator_root_directory + "/../config.json")) # Load the configuration database from config.json
+    else:
+        print("The configuration file doesn't appear to exist at " + predator_root_directory + "/../config.json.")
+        exit()
+except:
+    print("The configuration database couldn't be loaded. It may be corrupted.")
+    exit()
+
+
+model = YOLO("../assets/models/dashcam_model.pt")
+def predict(frame):
+    global model
+    results = model(frame)
+    class_names = results[0].names
+
+    detected_objects = [] # This is a placeholder that will hold all of the detected objects.
+    for result in results:
+        boxes = result.boxes
+        for i in range(0, len(boxes)):
+            obj = {}
+            box = result.boxes[i].xyxy.numpy().tolist()[0]
+            obj["bbox"] = {}
+            obj["bbox"]["x1"] = round(box[0])
+            obj["bbox"]["y1"] = round(box[1])
+            obj["bbox"]["x2"] = round(box[2])
+            obj["bbox"]["y2"] = round(box[3])
+            obj["name"] = class_names[int(result.boxes[i].cls.numpy().tolist()[0])]
+            obj["conf"] = result.boxes[i].conf.numpy().tolist()[0]
+            detected_objects.append(obj)
+    return detected_objects
+
+device = "main"
+
+if (device not in config["dashcam"]["capture"]["video"]["devices"]):
+    print("The specified device does not exist in the configuration. Be sure to change the 'device' variable in the motion_detect_test.py file to the device you want to test.")
+    exit()
+
+resolution = [config["dashcam"]["capture"]["video"]["devices"][device]["resolution"]["width"], config["dashcam"]["capture"]["video"]["devices"][device]["resolution"]["height"]] # This determines the resolution that will be used for the video capture device.
+#capture = cv2.VideoCapture(config["dashcam"]["capture"]["video"]["devices"][device]["index"]); # Open the video capture device.
+capture = cv2.VideoCapture("/home/cvieira/Downloads/ParkingEventDemo.mp4"); # Open the video capture device.
+codec = list(config["dashcam"]["capture"]["video"]["devices"][device]["codec"])
+capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(codec[0], codec[1], codec[2], codec[3])) # Set the codec to be MJPEG.
+capture.set(cv2.CAP_PROP_FRAME_WIDTH,resolution[0]) # Set the video stream width.
+capture.set(cv2.CAP_PROP_FRAME_HEIGHT,resolution[1]) # Set the video stream height.
+
+background_subtractor = cv2.createBackgroundSubtractorMOG2()
+
+total_area = resolution[0] * resolution[1] # Calculate the total area of the frame.
+
+output = cv2.VideoWriter("./predator_object_recognition_test.avi", cv2.VideoWriter_fourcc(*'XVID'), 16, (resolution[0], resolution[1])) # Update the video output.
+
+
+while True:
+    ret, frame = capture.read()
+
+    detected_objects = predict(frame)
+    for element in detected_objects:
+        print(element["name"], element["conf"])
+        if (element["conf"] >= config["dashcam"]["parked"]["event"]["trigger_object_recognition"]["minimum_confidence"] and element["name"] in config["dashcam"]["parked"]["event"]["trigger_object_recognition"]["objects"]): # Check to see if this object is in the list of target objects.
+            print("Detected event.")
+            color = config["dashcam"]["parked"]["event"]["label"]["color"]
+            cv2.rectangle(frame, (element["bbox"]["x1"], element["bbox"]["y1"]), (element["bbox"]["x2"], element["bbox"]["y2"]), (color[2], color[1], color[0]), 2) # Draw a box around the contour in the frame.
+
+    cv2.imshow('Object Detection', frame)
+
+    output.write(frame) # Save this frame to the video file.
+    if cv2.waitKey(1) == ord('q'):
+        break
+
+capture.release()
+cv2.destroyAllWindows()