Traffic_Optimization/webcam.py at main · PIYUSH-JOSHI1/Traffic_Optimization · GitHub

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from ultralytics import YOLO
import cv2
import cvzone
import math
import time

cap = cv2.VideoCapture(0)  # For Webcam
cap.set(3, 1280)
cap.set(4, 720)
# cap = cv2.VideoCapture("./videos/Chikpete.mp4")  # For Video

model = YOLO("yolov8l.pt")

classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
              "teddy bear", "hair drier", "toothbrush", "auto rickshaw"
              ]

prev_frame_time = 0
new_frame_time = 0

while True:
    new_frame_time = time.time()
    success, img = cap.read()
    results = model(img, stream=True)

    people_count = 0  # Reset the count for each frame
    other_objects_count = 0  # Reset the count for each frame

    for r in results:
        boxes = r.boxes
        for box in boxes:
            # Bounding Box
            x1, y1, x2, y2 = box.xyxy[0]
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
            # cv2.rectangle(img,(x1,y1),(x2,y2),(255,0,255),3)
            w, h = x2 - x1, y2 - y1

            # Display bounding box
            cvzone.cornerRect(img, (x1, y1, w, h))

            # Confidence
            conf = math.ceil((box.conf[0] * 100)) / 100

            # Class Name
            cls = int(box.cls[0])
            object_name = classNames[cls]

            # Increment count based on object detected
            if object_name == "person":
                people_count += 1
            else:
                other_objects_count += 1

            # Display object name and confidence rate on top of the box
            cv2.putText(img, f'{object_name} {conf}', (x1, max(y1 - 10, 25)), cv2.FONT_HERSHEY_SIMPLEX, 1,
                        (255, 255, 255), 2, cv2.LINE_AA)

    # Display people count on top left corner
    cv2.putText(img, f'People: {people_count}', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (100, 100, 100), 2, cv2.LINE_AA)

    # Display other objects count on top right corner with a gap
    cv2.putText(img, f'Other Objects: {other_objects_count}', (img.shape[1] - 400, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
                (100, 100, 100), 2, cv2.LINE_AA)

    fps = 10 / (new_frame_time - prev_frame_time)
    prev_frame_time = new_frame_time
    print(fps)

    cv2.imshow("Image", img)
    cv2.waitKey(1)