ImageSet Extended Examples

A large group of images captured using a RedEdge over a central California orchard are available for download here.

With this set extracted to a working folder, this notebook will load all of the images in the set and provide more usage examples of ImageSet data.

Load a directory of images into an ImageSet

%load_ext autoreload
%autoreload 2

from ipywidgets import FloatProgress, Layout
from IPython.display import display
import os
from micasense import imageset

## This progress widget is used for display of the long-running process
f = FloatProgress(min=0, max=1, layout=Layout(width="100%"), description="Loading")
display(f)


def update_f(val):
    if (
        val - f.value
    ) > 0.005 or val == 1:  # reduces cpu usage from updating the progressbar by 10x
        f.value = val


images_dir = os.path.expanduser(os.path.join(".", "data", "REDEDGE-MX"))
%time imgset = imageset.ImageSet.from_directory(images_dir, progress_callback=update_f)

CPU times: user 16.8 ms, sys: 8.62 ms, total: 25.5 ms
Wall time: 204 ms

Import Capture data into Pandas DataFrame

import pandas as pd

data, columns = imgset.as_nested_lists()
print("Columns: {}".format(columns))
df = pd.DataFrame.from_records(data, index="timestamp", columns=columns)

Columns: ['timestamp', 'latitude', 'longitude', 'altitude', 'capture_id', 'dls-yaw', 'dls-pitch', 'dls-roll', 'file_paths', 'irr-475', 'irr-560', 'irr-668', 'irr-842', 'irr-717']

Plotting capture metadata

import matplotlib.pyplot as plt

%matplotlib inline
ax = df.plot(
    y=columns[3:],
    subplots=True,
    figsize=(16, 10),
    style=["g", "c", "y", "k", "b", "g", "r", "m", "k"],
)
for a in ax:
    a.legend(loc="right", bbox_to_anchor=(1.1, 0.5), ncol=1, fancybox=True, shadow=True)

Separating ground and flight images

We can plot a histogram of the image altitudes for all images in the flight and use the basic statistics to guess at an altitude below which the panel images were likely taken. This can give us an altitude threshold we can apply to separate images taken in flight and on the ground.

# plot the histogram of the altitude data
df.altitude.hist()
# find the altitude above which the flight images occur
cutoff_altitude = df.altitude.mean() - 3.0 * df.altitude.std()
plt.axvline(x=cutoff_altitude, c="r")
plt.xlabel("Capture altitude (m)")
plt.ylabel("Number of occurances");

Automatically extracting radiances of panel images

Using the Panel class, we can automatically find and compute the radiance of panel regions within panel images. Each Capture object exposes a panel_radiance() method which can be used on panel images to get a list of the radiance of each band (in the native RedEdge band order).

import numpy as np

flight = df.altitude > cutoff_altitude
ground = ~flight
ground_idx = np.arange(len(ground))[ground]
flight_idx = np.arange(len(ground))[flight]
ground_captures = np.array(imgset.captures)[ground_idx]
flight_captures = np.array(imgset.captures)[flight_idx]

panel_radiances = []
dls_irradiances = []
panel_timestamps = []
for cap in ground_captures:
    if cap.panels_in_all_expected_images():
        panel_timestamps.append(cap.utc_time())
        panel_radiances.append(cap.panel_radiance())
        dls_irradiances.append(cap.dls_irradiance())

dls_irradiances = np.asarray(dls_irradiances)
panel_radiances = np.asarray(panel_radiances)

###
panel_reflectance_by_band = [0.49, 0.49, 0.49, 0.49, 0.49]  # RedEdge band_index order
if len(ground_captures) > 0:
    panel_irradiance = ground_captures[0].panel_irradiance(panel_reflectance_by_band)
    plt.figure()
    plt.scatter(ground_captures[0].center_wavelengths(), panel_irradiance)
    plt.xscale("log")
    plt.ylabel("Irradiance (w/m^2/nm)")
    plt.xlabel("Wavelength (nm)")
else:
    print("No ground captures found, skipping panel irradiance plot.")

No ground captures found, skipping panel irradiance plot.

Plotting DLS Irradiances

Once we have extracted the panel radiances from the ground images, we can convert them to irradiance as we did in tutorial #1 and plot the irradiances over time and extract radiance to reflectance conversion factors.

import matplotlib.pyplot as plt
import math

df[df.altitude > cutoff_altitude].plot(y=columns[8:13], figsize=(14, 8))
plt.ylabel("Irradiance (w/m^2/nm)");

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Copyright (c) 2017-2018 MicaSense, Inc. For licensing information see the project git repository