CodeAlpha_ObjectDetectionAndTracking/object_detection_and_tracking.py at main · sri0305-AI/CodeAlpha_ObjectDetectionAndTracking · GitHub

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# -*- coding: utf-8 -*-
"""Object detection_and_Tracking

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1u89Ak1oBNPtZ-KWsgcaJjIPHFUZT-EBP
"""

!pip install ultralytics
!pip install opencv-python
!pip install filterpy
!pip install lap

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

from google.colab import files

uploaded = files.upload()

from google.colab.patches import cv2_imshow

cap = cv2.VideoCapture("sample_video.mp4")

while True:
    ret, frame = cap.read()

    if not ret:
        break

    results = model(frame)

    annotated_frame = results[0].plot()

    cv2_imshow(annotated_frame)

    break   # Shows only first frame

cap.release()
out.release()

print("photo saved as output_photo")

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

cap = cv2.VideoCapture("sample_video.mp4")

width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))

out = cv2.VideoWriter(
    "output_video.mp4",
    cv2.VideoWriter_fourcc(*'mp4v'),
    fps,
    (width, height)
)

while True:
    ret, frame = cap.read()

    if not ret:
        break

    results = model(frame)

    annotated_frame = results[0].plot()

    out.write(annotated_frame)

cap.release()
out.release()

print("Video saved as output_video.mp4")

from google.colab import files
files.download("output_video.mp4")

!git clone https://github.com/abewley/sort.git

!sed -i "s/matplotlib.use('TkAgg')/matplotlib.use('Agg')/g" /content/sort/sort.py

import sys
sys.path.append('/content/sort')

from sort import Sort

tracker = Sort()

import cv2
import numpy as np
from ultralytics import YOLO
from sort import Sort

model = YOLO("yolov8n.pt")
tracker = Sort()

cap = cv2.VideoCapture("sample_video.mp4")

width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))

out = cv2.VideoWriter(
    "tracked_output.mp4",
    cv2.VideoWriter_fourcc(*'mp4v'),
    fps,
    (width, height)
)

while True:
    ret, frame = cap.read()

    if not ret:
        break

    results = model(frame)

    detections = []

    for r in results:
        for box in r.boxes:

            x1, y1, x2, y2 = box.xyxy[0]
            conf = float(box.conf[0])

            detections.append([
                int(x1),
                int(y1),
                int(x2),
                int(y2),
                conf
            ])

    if len(detections) > 0:
        detections = np.array(detections)
        tracks = tracker.update(detections)

        for track in tracks:

            x1, y1, x2, y2, track_id = track

            cv2.rectangle(
                frame,
                (int(x1), int(y1)),
                (int(x2), int(y2)),
                (0,255,0),
                2
            )

            cv2.putText(
                frame,
                f"ID:{int(track_id)}",
                (int(x1), int(y1)-10),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.7,
                (0,255,0),
                2
            )

    out.write(frame)

cap.release()
out.release()

print("Tracking completed!")
print("Saved as tracked_output.mp4")

from google.colab import files
files.download("tracked_output.mp4")