akleemans/generate_diary.py

## generate_diary.py
from typing import List, Tuple
import requests
import json
import glob
import os
from PIL import Image, ImageOps

"""
This script will automically pick up images from a subfolder 'img' and try to classify them against
the Pl@ntNet API. (You have to provide your API key below.)

It will then create a HTML file with a list of plants (with images downscaled) and their classification,
including a small map if geo-coordinates were found.

Last updated: 2024-10-14
https://kleemans.ch/classifying-plants-creating-an-excursion-diary
"""
api_key = '<api-key>'


template = """
<!doctype html>
<html lang="en">
  <head>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/water.css@2/out/light.css">
    <title>{{title}}</title>
    <style>
      body {
        width: 1000px;
      }
      .plant-img {
        width: 400px;
        height: 400px;
        object-fit: cover;
      }
      #map {
        height: 350px;
        width: 700px;
        margin-left: auto;
        margin-right: auto;
        margin-bottom: 50px;
      }
    </style>
    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.8.0/dist/leaflet.css" crossorigin=""
    integrity="sha512-hoalWLoI8r4UszCkZ5kL8vayOGVae1oxXe/2A4AO6J9+580uKHDO3JdHb7NzwwzK5xr/Fs0W40kiNHxM9vyTtQ=="/>
  </head>
  <body>
    <h1>{{title}}</h1>
    <p>
      (Beschreibung Ausflug)
    </p>
    <div id="map"></div>
    <h2>Pflanzen</h2>
    <table>
{{table_content}}
    </table>
     <script src="https://unpkg.com/leaflet@1.8.0/dist/leaflet.js" crossorigin=""
 integrity="sha512-BB3hKbKWOc9Ez/TAwyWxNXeoV9c1v6FIeYiBieIWkpLjauysF18NzgR1MBNBXf8/KABdlkX68nAhlwcDFLGPCQ=="></script>
 <script>
  const map = L.map('map').setView({{gps_data}}, 10);
  L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
      maxZoom: 19, attribution: '© OpenStreetMap'}).addTo(map);
  L.marker({{gps_data}}).addTo(map);
 </script>
  </body>
</html>
"""

tr_template = """
<tr>
  <td><img class="plant-img" src="{{filename}}"></td>
  <td>
  {{names}}</td>
</tr>
"""

# Latin names of plants of interest can be added here, they will be shown on top
z200 = [
  "Abies alba",
  "Acer campestre",
  # etc.
]

api_service = 'https://my-api.plantnet.org/v2/identify'
api_project = 'weurope'
api_lang = 'de'
base_url = f'{api_service}/{api_project}?api-key={api_key}&lang={api_lang}'

basewidth = 1280
leaf_recognizer = '_l'
img_dir = 'img/'
scaled_img_dir = 'img_scaled/'

def downscale():
  os.mkdir('img_scaled')
  for filename in glob.glob(f'{img_dir}*'):
    print('Downscaling', filename)
    img = Image.open(filename)
    img = ImageOps.exif_transpose(img)

    w = img.size[0]
    h = img.size[1]
    factor = max(w, h) / basewidth

    img = img.resize((int(w/factor), int(h/factor)), Image.LANCZOS)

    new_filename = filename.replace(img_dir, scaled_img_dir)
    img.save(new_filename)

def decimal_coords(ref: str, coords: Tuple[float, float, float]):
  decimal_degrees = coords[0] + coords[1] / 60.0 + coords[2] / 3600.0
  if ref == 'S' or ref == 'W':
      decimal_degrees = -decimal_degrees
  return decimal_degrees

def get_coordinates() -> Tuple[float, float]:
  for filename in glob.glob(f'{img_dir}*'):
    exif = Image.open(filename)._getexif()

    if exif is not None:
        entry = list(filter(lambda x: x[0] == 34853, exif.items()))
        gps_info = entry[0][1]
        try:
          lat = decimal_coords(gps_info[1], gps_info[2])
          long = decimal_coords(gps_info[3], gps_info[4])
          return [round(lat, 8), round(long, 8)]
        except:
          continue
  return (0.0, 0.0)


def get_species(filename: str) -> List[str]:
  organ = 'leaf' if leaf_recognizer in filename else 'flower'
  files = {'images': open(filename, 'rb')}
  response = requests.post(base_url, files=files, data={'organs': [organ]})
  json_result = json.loads(response.text)

  names = []
  for result in json_result['results']:
    # print('result:', result)
    score = result['score']
    if score < 0.05:
      break
    lat_name = result['species']['scientificNameWithoutAuthor']
    common_name = '-'
    if len(result['species']['commonNames']) > 0:
      common_name = result['species']['commonNames'][0]
    names.append([lat_name + ' (' + str(score * 100)[:4] + '%)', common_name])
  return names

def get_table_content() -> str:
  table_content = ''
  for filename in glob.glob(f'{scaled_img_dir}*'):
    print('Identifying', filename)
    names_str = ''
    is_z200 = False
    for name in get_species(filename):
      lat_name = name[0].split(' (')[0]
      names_str += f'<div><b>{name[0]}</b></div>\n'
      names_str += f'<div>{name[1]}</div>\n'
      if ' '.join(lat_name.split(' ')[:2]) in z200:
        is_z200 = True
        names_str += '<div><i>CH-200</i></div>\n'

    tr_entry = tr_template.replace('{{filename}}', filename).replace('{{names}}', names_str)
    if is_z200:
      table_content = tr_entry + table_content
    else:
      table_content += tr_entry
  return table_content


###

# Get GPS data
gps_data = get_coordinates()

# Downscale all images
downscale()

table_content = get_table_content()

print('Writing file...')
title = os.getcwd().split('/')[-1]
html = template.replace('{{table_content}}', table_content) \
  .replace('{{gps_data}}', str(gps_data)).replace('{{title}}', title)

with open('index.html', 'w') as write_file:
  write_file.write(html)
print('Done!')
	from typing import List, Tuple
	import requests
	import json
	import glob
	import os
	from PIL import Image, ImageOps

	"""
	This script will automically pick up images from a subfolder 'img' and try to classify them against
	the Pl@ntNet API. (You have to provide your API key below.)

	It will then create a HTML file with a list of plants (with images downscaled) and their classification,
	including a small map if geo-coordinates were found.

	Last updated: 2024-10-14
	https://kleemans.ch/classifying-plants-creating-an-excursion-diary
	"""
	api_key = '<api-key>'


	template = """
	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/water.css@2/out/light.css">
	<title>{{title}}</title>
	<style>
	body {
	width: 1000px;
	}
	.plant-img {
	width: 400px;
	height: 400px;
	object-fit: cover;
	}
	#map {
	height: 350px;
	width: 700px;
	margin-left: auto;
	margin-right: auto;
	margin-bottom: 50px;
	}
	</style>
	<link rel="stylesheet" href="https://unpkg.com/leaflet@1.8.0/dist/leaflet.css" crossorigin=""
	integrity="sha512-hoalWLoI8r4UszCkZ5kL8vayOGVae1oxXe/2A4AO6J9+580uKHDO3JdHb7NzwwzK5xr/Fs0W40kiNHxM9vyTtQ=="/>
	</head>
	<body>
	<h1>{{title}}</h1>
	<p>
	(Beschreibung Ausflug)
	</p>
	<div id="map"></div>
	<h2>Pflanzen</h2>
	<table>
	{{table_content}}
	</table>
	<script src="https://unpkg.com/leaflet@1.8.0/dist/leaflet.js" crossorigin=""
	integrity="sha512-BB3hKbKWOc9Ez/TAwyWxNXeoV9c1v6FIeYiBieIWkpLjauysF18NzgR1MBNBXf8/KABdlkX68nAhlwcDFLGPCQ=="></script>
	<script>
	const map = L.map('map').setView({{gps_data}}, 10);
	L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
	maxZoom: 19, attribution: '© OpenStreetMap'}).addTo(map);
	L.marker({{gps_data}}).addTo(map);
	</script>
	</body>
	</html>
	"""

	tr_template = """
	<tr>
	<td><img class="plant-img" src="{{filename}}"></td>
	<td>
	{{names}}</td>
	</tr>
	"""

	# Latin names of plants of interest can be added here, they will be shown on top
	z200 = [
	"Abies alba",
	"Acer campestre",
	# etc.
	]

	api_service = 'https://my-api.plantnet.org/v2/identify'
	api_project = 'weurope'
	api_lang = 'de'
	base_url = f'{api_service}/{api_project}?api-key={api_key}&lang={api_lang}'

	basewidth = 1280
	leaf_recognizer = '_l'
	img_dir = 'img/'
	scaled_img_dir = 'img_scaled/'

	def downscale():
	os.mkdir('img_scaled')
	for filename in glob.glob(f'{img_dir}*'):
	print('Downscaling', filename)
	img = Image.open(filename)
	img = ImageOps.exif_transpose(img)

	w = img.size[0]
	h = img.size[1]
	factor = max(w, h) / basewidth

	img = img.resize((int(w/factor), int(h/factor)), Image.LANCZOS)

	new_filename = filename.replace(img_dir, scaled_img_dir)
	img.save(new_filename)

	def decimal_coords(ref: str, coords: Tuple[float, float, float]):
	decimal_degrees = coords[0] + coords[1] / 60.0 + coords[2] / 3600.0
	if ref == 'S' or ref == 'W':
	decimal_degrees = -decimal_degrees
	return decimal_degrees

	def get_coordinates() -> Tuple[float, float]:
	for filename in glob.glob(f'{img_dir}*'):
	exif = Image.open(filename)._getexif()

	if exif is not None:
	entry = list(filter(lambda x: x[0] == 34853, exif.items()))
	gps_info = entry[0][1]
	try:
	lat = decimal_coords(gps_info[1], gps_info[2])
	long = decimal_coords(gps_info[3], gps_info[4])
	return [round(lat, 8), round(long, 8)]
	except:
	continue
	return (0.0, 0.0)


	def get_species(filename: str) -> List[str]:
	organ = 'leaf' if leaf_recognizer in filename else 'flower'
	files = {'images': open(filename, 'rb')}
	response = requests.post(base_url, files=files, data={'organs': [organ]})
	json_result = json.loads(response.text)

	names = []
	for result in json_result['results']:
	# print('result:', result)
	score = result['score']
	if score < 0.05:
	break
	lat_name = result['species']['scientificNameWithoutAuthor']
	common_name = '-'
	if len(result['species']['commonNames']) > 0:
	common_name = result['species']['commonNames'][0]
	names.append([lat_name + ' (' + str(score * 100)[:4] + '%)', common_name])
	return names

	def get_table_content() -> str:
	table_content = ''
	for filename in glob.glob(f'{scaled_img_dir}*'):
	print('Identifying', filename)
	names_str = ''
	is_z200 = False
	for name in get_species(filename):
	lat_name = name[0].split(' (')[0]
	names_str += f'<div><b>{name[0]}</b></div>\n'
	names_str += f'<div>{name[1]}</div>\n'
	if ' '.join(lat_name.split(' ')[:2]) in z200:
	is_z200 = True
	names_str += '<div><i>CH-200</i></div>\n'

	tr_entry = tr_template.replace('{{filename}}', filename).replace('{{names}}', names_str)
	if is_z200:
	table_content = tr_entry + table_content
	else:
	table_content += tr_entry
	return table_content


	###

	# Get GPS data
	gps_data = get_coordinates()

	# Downscale all images
	downscale()

	table_content = get_table_content()

	print('Writing file...')
	title = os.getcwd().split('/')[-1]
	html = template.replace('{{table_content}}', table_content) \
	.replace('{{gps_data}}', str(gps_data)).replace('{{title}}', title)

	with open('index.html', 'w') as write_file:
	write_file.write(html)
	print('Done!')