)
print(
"The execption text below may help to find the source of the problem:")
print()
print(e)
# ### Testing image reading and panel detection
#
# The above code checks for the proper libraries to be installed and verifies it can execute `exiftool`. This code opens an example image, reads the metadata, and then uses the `pyzbar` library to find a MicaSense panel in the image.
# In[8]:
from micasense.image import Image
imagePath = os.path.join('.', 'Imagery', 'band1')
imageName = glob.glob(os.path.join(imagePath, 'IMG_0005_1.tif'))[0]
img = Image(imageName)
img.plot_raw(figsize=(8.73, 8.73))
from micasense.panel import Panel
panel = Panel(img)
if not panel.panel_detected():
raise IOError("Panel Not Detected! Check your installation of pyzbar")
else:
panel.plot(figsize=(8, 8))
print('Success! Now you are ready for Part 1 of the tutorial.')
# ---
# Copyright (c) 2017-2018 MicaSense, Inc. For licensing information see the [project git repository](https://github.com/micasense/imageprocessing)
# This notebook shows usage for the Panel class. This type is useful for detecting MicaSense calibration panels and extracting information about the lambertian panel surface.
# In[ ]:
import os, glob
from micasense.image import Image
from micasense.panel import Panel
get_ipython().run_line_magic('matplotlib', 'inline')
imagePath = os.path.join('.', 'data', '0000SET', '000')
imageName = glob.glob(os.path.join(imagePath, 'IMG_0000_1.tif'))[0]
img = Image(imageName)
panel = Panel(img)
if not panel.panel_detected():
raise IOError("Panel Not Detected!")
print("Detected panel serial: {}".format(panel.serial))
mean, std, num, sat_count = panel.raw()
print("Extracted Panel Statistics:")
print("Mean: {}".format(mean))
print("Standard Deviation: {}".format(std))
print("Panel Pixel Count: {}".format(num))
print("Saturated Pixel Count: {}".format(sat_count))
panel.plot()
# ---
# Copyright (c) 2017-2018 MicaSense, Inc. For licensing information see the [project git repository](https://github.com/micasense/imageprocessing)