Create a video of a journey

# Import libraries
# Generic library

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from natsort import natsorted

# Creation of the video
from tqdm import tqdm
import cv2

from pytrack.analytics import video
from pytrack.graph import distance

# Creation of matched path
from pytrack.graph import graph
from pytrack.graph import utils
from pytrack.matching import candidate, mpmatching_utils, mpmatching

df = pd.read_excel("dataset.xlsx")

latitude = df["latitude"].to_list()
longitude = df["longitude"].to_list()

points = [(lat, lon) for lat, lon in zip(latitude[:30], longitude[:30])]

# Create BBOX
north, east = np.max(np.array([*points]), 0)
south, west = np.min(np.array([*points]), 0)

# Extract road graph
G = graph.graph_from_bbox(*distance.enlarge_bbox(north, south, west, east, 500), simplify=True, network_type='drive')

nodes, edges = utils.graph_to_gdfs(G)  # Add to G a geometry attribute describing the geometry of both nodes and edges. TODO: create an autonomous method.

# Extract candidates
G_interp, candidates = candidate.get_candidates(G, points, interp_dist=5, closest=True, radius=30)

# Extract trellis DAG graph
trellis = mpmatching_utils.create_trellis(candidates)

# Perform the map-matching process
path_prob, predecessor = mpmatching.viterbi_search(G_interp, trellis, "start", "target")

_, path = mpmatching_utils.create_matched_path(G_interp, trellis, predecessor)  # Path expressed through a list of nodes (lat, lng)

Downloaded 448.33kB

root_dir = "SV_panoramas"  # Directory where save Google Street View panoramas
api_key = 'Insert your private API key for Google services'

if not os.path.exists(root_dir):
    os.makedirs(root_dir)

for i in tqdm(range(len(path))):
    if not os.path.isdir(os.path.join(root_dir, str(i))):
        if i != 0:
            point = path[i]
            prec_point = path[i - 1]
            head = distance.get_bearing(prec_point[0], prec_point[1], point[0], point[1])
        else:
            point = path[i]
            succ_point = path[i + 1]
            head = distance.get_bearing(point[0], point[1], succ_point[0], succ_point[1])

        pic, meta = video.extract_streetview_pic(point, api_key, size="640x640", heading=head, pitch=-10)

        if pic is not None:
            video.save_streetview(pic, meta, os.path.join(root_dir, str(i)))

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# Create video of the path
root_dir = "SV_panoramas"

images = list()
for root, dirs, files in os.walk(root_dir):
    for file in files:
        if file.endswith("pic.png"):
            images += [os.path.join(root, file)]

images = natsorted(images)

fourcc = cv2.VideoWriter_fourcc(*"avc1")
video_path = os.path.join("video.mp4")

video.make_video(images, video_path, fourcc, fps=16, size=(640, 640), is_color=True)

from IPython.display import Video

Video("video.mp4", embed=True, width=640, height=640)