warhawk/warhawk.py at main · thesarinjaizz09/warhawk · GitHub

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from ultralytics import YOLO as warhawkAi
import cvzone as warhawkAiInterpretor
import cv2 as warhawkAiMedia
import math as warhawkMath
import warhawkObjectClassnames_fire
import numpy as nm
from sort import *

def detectVideo(path_x):
    videoPath = path_x
    warhawkAiClassnames = warhawkObjectClassnames_fire.warhawkAiClassnames
    droneFeedCapture = warhawkAiMedia.VideoCapture(videoPath)
    warhawkModel = warhawkAi("../warhawk-weights/yolov8m.pt")
    warhawkAiClassesToDetect = [0, 1, 2, 3, 4, 5, 6, 7]

    humanTracker = Sort(max_age=40, min_hits=8, iou_threshold=0.3)
    carTracker = Sort(max_age=40, min_hits=8, iou_threshold=0.3)
    busTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)
    truckTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)
    boatTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)
    airplaneTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)
    bicycleTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)
    motorcycleTracker = Sort(max_age=40, min_hits=6, iou_threshold=0.3)

    humanLogs = []
    carLogs = []
    busLogs = []
    truckLogs = []
    boatLogs = []
    airplaneLogs = []
    bicycleLogs = []
    motorcycleLogs = []

    while True:
        success, img = droneFeedCapture.read()
        results = warhawkModel(img, stream=True, classes=warhawkAiClassesToDetect)
        humanDetections = np.empty((0, 5))
        carDetections = np.empty((0, 5))
        busDetections = np.empty((0, 5))
        truckDetections = np.empty((0, 5))
        boatDetections = np.empty((0, 5))
        airplaneDetections = np.empty((0, 5))
        motorcycleDetections = np.empty((0, 5))
        bicycleDetections = np.empty((0, 5))

        for r in results:
            for box in r.boxes:
                x1, y1, x2, y2 = box.xyxy[0]
                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                conf = warhawkMath.ceil((box.conf[0] * 100)) / 100
                objectClass = warhawkAiClassnames[int(box.cls[0])]
                if objectClass == 'person':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2)
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, font=warhawkAiMedia.FONT_HERSHEY_PLAIN)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    humanDetections = np.vstack((humanDetections, currentArray))
                elif objectClass == 'car':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(255, 0, 0))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(0, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(255, 0, 0),
                                                     font=warhawkAiMedia.FONT_HERSHEY_PLAIN)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    carDetections = np.vstack((carDetections, currentArray))
                elif objectClass == 'truck':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2,
                                                    colorR=(0, 75, 150))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(0, 75, 150),
                                                     font=warhawkAiMedia.FONT_HERSHEY_PLAIN)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    truckDetections = np.vstack((truckDetections, currentArray))
                elif objectClass == 'bus':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(11, 11, 155))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(11, 11, 155), font=warhawkAiMedia.FONT_HERSHEY_PLAIN,)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    busDetections = np.vstack((busDetections, currentArray))
                elif objectClass == 'boat':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(255, 255, 0))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(255, 100, 225), font=warhawkAiMedia.FONT_HERSHEY_PLAIN,)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    boatDetections = np.vstack((boatDetections, currentArray))
                elif objectClass == 'motorcycle':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(192, 192, 192))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(192, 192, 192), font=warhawkAiMedia.FONT_HERSHEY_PLAIN,)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    motorcycleDetections = np.vstack((motorcycleDetections, currentArray))
                elif objectClass == 'airplane':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(0, 0, 0))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(0, 0, 0), font=warhawkAiMedia.FONT_HERSHEY_PLAIN,)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    airplaneDetections = np.vstack((airplaneDetections, currentArray))
                elif objectClass == 'bicycle':
                    warhawkAiInterpretor.cornerRect(img, (x1, y1, x2 - x1, y2 - y1), l=9, rt=2, t=2, colorR=(8, 33, 12))
                    warhawkAiInterpretor.putTextRect(img, f'{objectClass} {conf}', (max(0, x1), max(35, y1)), scale=1,
                                                     thickness=1, offset=10, colorR=(8, 33, 12), font=warhawkAiMedia.FONT_HERSHEY_PLAIN,)
                    currentArray = np.array([x1, y1, x2, y2, conf])
                    bicycleDetections = np.vstack((bicycleDetections, currentArray))


        if r.boxes:
            humanTrackerResults = humanTracker.update(humanDetections)
            carTrackerResults = carTracker.update(carDetections)
            busTrackerResults = busTracker.update(busDetections)
            truckTrackerResults = truckTracker.update(truckDetections)
            boatTrackerResults = boatTracker.update(boatDetections)
            airplaneTrackerResults = airplaneTracker.update(airplaneDetections)
            bicycleTrackerResults = bicycleTracker.update(bicycleDetections)
            motorcycleTrackerResults = motorcycleTracker.update(motorcycleDetections)

            for result in humanTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in humanLogs:
                    humanLogs.append(int(id))
                    with open("./data/persons.log", "w") as txt_file:
                        for line in humanLogs:
                            txt_file.write(f'Person-{line} (Co-ordinates)' + "\n")

            for result in truckTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in truckLogs:
                    truckLogs.append(int(id))
                    with open("./data/trucks.log", "w") as txt_file:
                        for line in truckLogs:
                            txt_file.write(f'Truck-{line} (Co-ordinates)' + "\n")

            for result in carTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in carLogs:
                    carLogs.append(int(id))
                    with open("./data/cars.log", "w") as txt_file:
                        for line in carLogs:
                            txt_file.write(f'Cars-{line} (Co-ordinates)' + "\n")

            for result in busTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in busLogs:
                    busLogs.append(int(id))
                    with (open("./data/bus.log", "w") as txt_file):
                        for line in busLogs:
                            txt_file.write(f'Bus-{line} (Co-ordinates)' + "\n")

            for result in boatTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in boatLogs:
                    boatLogs.append(int(id))
                    with (open("./data/boat.log", "w") as txt_file):
                        for line in boatLogs:
                            txt_file.write(f'Boat-{line} (Co-ordinates)' + "\n")

            for result in motorcycleTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in motorcycleLogs:
                    motorcycleLogs.append(int(id))
                    with (open("./data/motorcycle.log", "w") as txt_file):
                        for line in motorcycleLogs:
                            txt_file.write(f'Motorcycle-{line} (Co-ordinates)' + "\n")

            for result in airplaneTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in airplaneLogs:
                    airplaneLogs.append(int(id))
                    with (open("./data/airplane.log", "w") as txt_file):
                        for line in airplaneLogs:
                            txt_file.write(f'Airplane-{line} (Co-ordinates)' + "\n")

            for result in bicycleTrackerResults:
                x1, y1, x2, y2, id = result
                if int(id) not in bicycleLogs:
                    bicycleLogs.append(int(id))
                    with (open("./data/bicycle.log", "w") as txt_file):
                        for line in bicycleLogs:
                            txt_file.write(f'Bicycle-{line} (Co-ordinates)' + "\n")

        yield img


    # warhawkAiMedia.imshow("Drone Live Video", img)
    # if (warhawkAiMedia.waitKey(1) & 0xFF) == ord('q'):
    #     warhawkAiMedia.destroyAllWindows()
    #     break