class ImageTest(unittest.TestCase):
"""Check plant identification"""
def setUp(self):
self.parameters = Parameters()
self.db = DB()
self.image = Image(self.parameters, self.db)
def test_wrong_parameters(self):
"""Check that incompatible parameters are fixed"""
self.image.params.parameters['blur'] = 2
self.image.params.parameters['morph'] = 0
self.image.params.parameters['iterations'] = 0
self.image.load('plant_detection/soil_image.jpg')
self.image.initial_processing()
self.assertEqual(self.image.params.parameters['blur'], 3)
self.assertEqual(self.image.params.parameters['morph'], 1)
self.assertEqual(self.image.params.parameters['iterations'], 1)
def test_image_size_reduction(self):
"""Reduce large image"""
large_image = np.zeros([1000, 2000, 3], np.uint8)
self.image.image_name = None
self.image.save('large', image=large_image)
self.image.load('large.jpg')
new_height = self.image.images['current'].shape[0]
self.assertEqual(new_height, 600)
def tearDown(self):
try:
os.remove('large.jpg')
except OSError:
pass
def _calibration_image_preparation(self, calibration_image):
if calibration_image is not None:
self.image = Image(self.cparams, self.plant_db)
if isinstance(calibration_image, int):
try:
self.image.download(calibration_image)
except IOError:
print("Image download failed for image ID {}.".format(
str(calibration_image)))
sys.exit(0)
else:
self.image.load(calibration_image)
self.calibration_params[
'center_pixel_location'] = self.get_image_center(
self.image.images['current'])
self.image.calibration_debug = self.debug
def _detection_image(self): # get image to process
self.image = Image(self.params, self.plant_db)
# Get image to process
try: # check for API image ID
image_id = self.args['app_image_id']
except KeyError:
image_id = None
if image_id is not None: # download image
try:
self.image.download(image_id)
except IOError:
print("Image download failed for image ID {}.".format(
str(image_id)))
sys.exit(0)
elif self.args['image'] is None: # No image provided. Capture one.
self.image.capture()
if self.args['debug']:
self.image.save('photo')
else: # Image provided. Load it.
filename = self.args['image']
self.image.load(filename)
self.image.debug = self.args['debug']
def setUp(self):
self.parameters = Parameters()
self.db = DB()
self.image = Image(self.parameters, self.db)
class Pixel2coord(object):
"""Image pixel to machine coordinate conversion.
Calibrates the conversion of pixel locations to machine coordinates
in images. Finds object coordinates in image.
"""
def __init__(self, plant_db,
calibration_image=None,
calibration_data=None, load_data_from=None):
"""Set initial attributes.
Arguments:
Database() instance
Optional Keyword Arguments:
calibration_image (str): filename (default: None)
calibration_data: P2C().calibration_params JSON,
or 'file' or 'env_var' string
(default: None)
"""
self.dir = os.path.dirname(os.path.realpath(__file__)) + os.sep
self.parameters_file = "plant-detection_calibration_parameters.json"
self.calibration_params = {}
self.debug = False
self.env_var_name = 'PLANT_DETECTION_calibration'
self.plant_db = plant_db
self.defaults = Parameters().cdefaults
# Data and parameter preparation
self.cparams = Parameters()
self._calibration_data_preparation(calibration_data, load_data_from)
# Image preparation
self.image = None
self._calibration_image_preparation(calibration_image)
self.rotationangle = 0
self.test_rotation = 5 # for testing, add some image rotation
self.viewoutputimage = False # overridden as True if running script
self.json_calibration_data = None
def _calibration_data_preparation(self, calibration_data=None,
load_data_from=None):
for key, value in self.defaults.items():
if key in self.cparams.defaults:
self.cparams.defaults[key] = value
if calibration_data is not None:
self.calibration_params = calibration_data
elif load_data_from == 'file':
# self._load_parameters(self.load_calibration_parameters,
# IOError)
self._load_inputs(self.cparams.load, IOError)
self._additional_calibration_inputs(
self.load_calibration_parameters, IOError)
self.initialize_data_keys()
elif load_data_from == 'env_var':
# Method 1
# self._load_parameters(self.load_calibration_data_from_env,
# ValueError)
# Method 2
# self._load_inputs(
# self.cparams.load_env_var, ValueError)
# self._additional_calibration_inputs(
# self.load_calibration_data_from_env, ValueError)
# self.initialize_data_keys()
# Method 3
self.cparams.load_env_var('calibration')
self.calibration_params = self.cparams.parameters.copy()
else: # load defaults
self.calibration_params = self.defaults
if not self.calibration_params['easy_calibration']:
self.set_calibration_input_params()
def _load_inputs(self, get_inputs, error):
# load only image processing input parameters
try:
message = get_inputs() # Parameters object
if message != "":
raise error("Load Failed.")
except error:
print("Warning: Input parameter load failed. "
"Using Defaults.")
self.calibration_params = self.cparams.defaults.copy()
else:
self.calibration_params = self.cparams.parameters.copy()
def _load_parameters(self, get_parameters, error):
# load all parameters necessary for calibration / coordinate conversion
try:
get_parameters()
except error:
print("Warning: Calibration data load failed. "
"Using defaults.")
self.calibration_params = self.defaults
def _additional_calibration_inputs(self, get_additional, error):
# load extra inputs needed (when using _load_inputs only)
temp_inputs = self.calibration_params
self.calibration_params = {}
try:
get_additional()
except error: # no additional calibration inputs to add
self.calibration_params = temp_inputs
else: # add additional calibration inputs
for key, value in self.calibration_params.items():
if key not in temp_inputs:
temp_inputs[key] = value
self.calibration_params = temp_inputs
@staticmethod
def get_image_center(image):
"""Return the pixel location (X, Y) of the image center."""
return [int(a / 2) for a in image.shape[:2][::-1]]
def _calibration_image_preparation(self, calibration_image):
if calibration_image is not None:
self.image = Image(self.cparams, self.plant_db)
if isinstance(calibration_image, int):
try:
self.image.download(calibration_image)
except IOError:
print("Image download failed for image ID {}.".format(
str(calibration_image)))
sys.exit(0)
else:
self.image.load(calibration_image)
self.calibration_params[
'center_pixel_location'] = self.get_image_center(
self.image.images['current'])
self.image.calibration_debug = self.debug
def save_calibration_parameters(self):
"""Save calibration parameters to file."""
if self.plant_db.tmp_dir is None:
directory = self.dir
else:
directory = self.plant_db.tmp_dir
with open(directory + self.parameters_file, 'w') as oututfile:
json.dump(self.calibration_params, oututfile)
def save_calibration_data_to_env(self):
"""Save calibration parameters to environment variable."""
self.json_calibration_data = self.calibration_params
self.cparams.parameters = self.calibration_params
self.cparams.save_to_env_var('calibration')
def load_calibration_data_from_env(self):
"""Load calibration parameters from environment variable."""
self.calibration_params = ENV.load(self.env_var_name)
if self.calibration_params is None:
raise ValueError("ENV load failed.")
def initialize_data_keys(self):
"""If using JSON with inputs only, create calibration data keys."""
def _check_for_key(key):
try:
self.calibration_params[key]
except KeyError:
self.calibration_params[key] = self.defaults[key]
calibration_keys = ['calibration_circles_xaxis',
'easy_calibration',
'image_bot_origin_location',
'calibration_circle_separation',
'camera_offset_coordinates',
'calibration_iters']
for key in calibration_keys:
_check_for_key(key)
def set_calibration_input_params(self):
"""Set input parameters from calibration parameters."""
self.cparams.parameters['blur'] = self.calibration_params['blur']
self.cparams.parameters['morph'] = self.calibration_params['morph']
self.cparams.parameters['H'] = self.calibration_params['H']
self.cparams.parameters['S'] = self.calibration_params['S']
self.cparams.parameters['V'] = self.calibration_params['V']
def load_calibration_parameters(self):
"""Load calibration parameters from file or use defaults."""
def _load(directory): # Load calibration parameters from file
with open(directory + self.parameters_file, 'r') as inputfile:
self.calibration_params = json.load(inputfile)
try:
_load(self.dir)
except IOError:
self.plant_db.tmp_dir = "/tmp/"
_load(self.plant_db.tmp_dir)
def validate_calibration_data(self, check_image):
"""Check that calibration parameters can be applied to the image."""
# Prepare data
image_center = self.get_image_center(check_image)
image_center = self._block_rotations(
self.calibration_params['total_rotation_angle'], cpl=image_center)
image_location = self.plant_db.coordinates
camera_dz = abs(
self.calibration_params['camera_z'] - image_location[2])
center_deltas = [abs(calibration - current) for calibration, current in
zip(self.calibration_params['center_pixel_location'],
image_center)]
# Check data
check_status = True
if camera_dz > 5:
check_status = False # set True to try camera height compensation
for center_delta in center_deltas:
if center_delta > 5:
check_status = False
return check_status
def _block_rotations(self, angle, cpl=None):
def _determine_turns(angle): # number of 90 degree rotations
turns = -int(angle / 90.)
remain = abs(angle) % 90
if angle < 0:
remain = -remain
if remain > 45:
turns -= 1
if remain < -45:
turns += 1
return turns
def _origin_rot(horiz, vert): # rotate image origin with image
if cpl is None:
# get image origin
origin = self.calibration_params['image_bot_origin_location']
# rotate image origin
if origin[0] == origin[1]:
origin[vert] = int(not origin[vert])
else:
origin[horiz] = int(not origin[horiz])
# set image origin
self.calibration_params['image_bot_origin_location'] = origin
# swap image center pixel horiz/vert
if cpl is None:
center = self.calibration_params['center_pixel_location']
else:
center = cpl
center = center[::-1]
self.calibration_params['center_pixel_location'] = center
return center
turns = _determine_turns(angle)
if turns > 0:
cpl = _origin_rot(0, 1)
if turns < 0:
cpl = _origin_rot(1, 0)
return cpl
def rotationdetermination(self):
"""Determine angle of rotation if necessary."""
threshold = 0
along_x = self.calibration_params['calibration_circles_xaxis']
[[cdx, cdy]] = np.diff(
self.plant_db.calibration_pixel_locations[:2, :2], axis=0)
if cdx == 0:
trig = None
else:
trig = cdy / cdx
difference = abs(cdy)
if not along_x:
if cdy == 0:
trig = None
else:
trig = cdx / cdy
difference = abs(cdx)
if difference > threshold:
if trig is None:
self.rotationangle = 90
else:
rotation_angle_radians = np.arctan(trig)
self.rotationangle = 180. / np.pi * rotation_angle_radians
if abs(cdy) > abs(cdx) and along_x:
self.rotationangle = -self.rotationangle
if abs(cdx) > abs(cdy) and not along_x:
self.rotationangle = -self.rotationangle
self._block_rotations(self.rotationangle)
else:
self.rotationangle = 0
def determine_scale(self):
"""Determine coordinate conversion scale."""
if len(self.plant_db.calibration_pixel_locations) > 1:
calibration_circle_sep = float(
self.calibration_params['calibration_circle_separation'])
object_sep = max(abs(np.diff(
self.plant_db.calibration_pixel_locations[:2, :2], axis=0)[0]))
self.calibration_params['coord_scale'] = round(
calibration_circle_sep / object_sep, 4)
def c2p(self, plant_db):
"""Convert coordinates to pixel locations using image center."""
plant_db.pixel_locations = self.convert(
plant_db.coordinate_locations, to_='pixels')
def p2c(self, plant_db):
"""Convert pixel locations to machine coordinates from image center."""
plant_db.coordinate_locations = self.convert(
plant_db.pixel_locations, to_='coordinates')
def plant_dict_to_pixel_array(self, plant_dict, extend_radius=0):
"""Convert a plant coordinate dictionary to a pixel array."""
pixel_array = np.array(self.convert(
[plant_dict['x'], plant_dict['y'],
plant_dict['radius'] + extend_radius],
to_='pixels'))[0]
return pixel_array
def convert(self, input_, to_=None):
"""Convert between image pixels and bot coordinates."""
# Check and manage input
if to_ is None:
raise TypeError("Conversion direction not provided.")
input_ = np.array(input_)
if len(input_) == 0:
output_ = []
return output_
try:
input_.shape[1]
except IndexError:
input_ = np.vstack([input_])
# Get conversion parameters
bot_location = np.array(self.plant_db.coordinates[:2], dtype=float)
current_z = self.plant_db.coordinates[2]
camera_offset = np.array(
self.calibration_params['camera_offset_coordinates'], dtype=float)
camera_coordinates = bot_location + camera_offset # img center coord
center_pixel_location = self.calibration_params[
'center_pixel_location'][:2]
sign = [1 if s == 1 else -1 for s
in self.calibration_params['image_bot_origin_location']]
coord_scale = np.repeat(self.calibration_params['coord_scale'], 2)
# Adjust scale factor for camera height
calibration_z = self.calibration_params['camera_z']
camera_dz = current_z - calibration_z
coord_scale = coord_scale + camera_dz / 157.3
# Convert
output_ = []
for obj_num, obj_loc in enumerate(input_[:, :2]):
if to_ == 'pixels':
radius = input_[obj_num][2]
result = (
center_pixel_location -
((obj_loc - camera_coordinates) / (sign * coord_scale)))
output_.append([result[0], result[1], radius / coord_scale[0]])
if to_ == 'coordinates':
radius = input_[:][obj_num][2]
result = (
camera_coordinates +
sign * coord_scale * (center_pixel_location - obj_loc))
output_.append([result[0], result[1], coord_scale[0] * radius])
return output_
def calibration(self):
"""Determine pixel to coordinate scale and image rotation angle."""
total_rotation_angle = 0
warning_issued = False
if self.debug:
self.cparams.print_input()
if self.calibration_params['easy_calibration']:
from plant_detection.PatternCalibration import PatternCalibration
pattern_calibration = PatternCalibration(self.calibration_params)
result_flag = pattern_calibration.move_and_capture()
if not result_flag:
fail_flag = True
return fail_flag
result_flag = pattern_calibration.calibrate()
if not result_flag:
fail_flag = True
return fail_flag
self.image.images['marked'] = pattern_calibration.output_img
self.image.grid(self)
fail_flag = False
return fail_flag
for i in range(0, self.calibration_params['calibration_iters']):
self.image.initial_processing()
self.image.find(calibration=True) # find objects
# If not the last iteration, determine camera rotation angle
if i != (self.calibration_params['calibration_iters'] - 1):
# Check number of objects detected and notify user if needed.
if len(self.plant_db.calibration_pixel_locations) == 0:
log("ERROR: Calibration failed. No objects detected.",
message_type='error', title='camera-calibration')
return True
if self.plant_db.object_count > 2:
if not warning_issued:
log(" Warning: {} objects detected. "
"Exactly 2 recommended. "
"Incorrect results possible. Check output.".format(
self.plant_db.object_count),
message_type='warn', title='camera-calibration')
warning_issued = True
if self.plant_db.object_count < 2:
log(" ERROR: {} objects detected. "
"At least 2 required. Exactly 2 recommended.".format(
self.plant_db.object_count),
message_type='error', title='camera-calibration')
return True
# Use detected objects to determine required rotation angle
self.rotationdetermination()
if abs(self.rotationangle) > 120:
log(" ERROR: Excessive rotation required. "
"Check that the calibration objects are "
"parallel with the desired axis and that "
"they are the only two objects detected.",
message_type='error', title='camera-calibration')
return True
self.image.rotate_main_images(self.rotationangle)
total_rotation_angle += self.rotationangle
self.determine_scale()
fail_flag = self._calibration_output(total_rotation_angle)
return fail_flag
def _calibration_output(self, total_rotation_angle):
if self.viewoutputimage:
self.image.images['current'] = self.image.images['marked']
self.image.show()
while abs(total_rotation_angle) > 360:
if total_rotation_angle < 0:
total_rotation_angle += 360
else:
total_rotation_angle -= 360
self.calibration_params['total_rotation_angle'] = round(
total_rotation_angle, 2)
self.calibration_params['camera_z'] = self.plant_db.coordinates[2]
try:
self.calibration_params['coord_scale'] # pylint:disable=W0104
failure_flag = False
except KeyError:
log("ERROR: Calibration failed.",
message_type='error', title='camera-calibration')
failure_flag = True
return failure_flag
def determine_coordinates(self):
"""Use calibration parameters to determine locations of objects."""
self.image.rotate_main_images(
self.calibration_params['total_rotation_angle'])
if self.debug:
self.cparams.print_input()
self.image.initial_processing()
self.image.find(calibration=True)
self.plant_db.print_count(calibration=True) # print detected obj count
self.p2c(self.plant_db)
self.plant_db.print_coordinates()
if self.viewoutputimage:
self.image.grid(self)
self.image.images['current'] = self.image.images['marked']
self.image.show()
return self.plant_db.get_json_coordinates()
class PlantDetection(object):
"""Detect plants in image and output an image with plants marked.
Kwargs:
image (str): filename of image to process (default = None)
None -> take photo instead
coordinates (boolean): use coordinate conversion (default = False)
calibration_img (filename): calibration image filename used to
output coordinates instead of pixel locations (default = None)
calibration_data (dict): calibration data inputs,
overwrites other calibration data inputs (default = None)
known_plants (list): {'x': x, 'y': y, 'radius': radius}
of known (intentional) plants
(default = None)
debug (boolean): output debug images (default = False)
blur (int): blur kernel size (must be odd, default = 5)
morph (int): amount of filtering (default = 5)
iterations (int): number of morphological iterations (default = 1)
array (list): list of morphs to run
[morph kernel size, morph kernel type, morph type, iterations]
example:
[{"size": 5, "kernel": 'ellipse', "type": 'erode', "iters": 2},
{"size": 3, "kernel": 'ellipse', "type": 'dilate', "iters": 8}]
(default = None)
save (boolean): save images (default = True)
clump_buster (boolean): attempt to break
plant clusters (default = False)
HSV_min (list): green lower bound Hue(0-179), Saturation(0-255),
and Value(0-255) (default = [30, 20, 20])
HSV_max (list): green upper bound Hue(0-179), Saturation(0-255),
and Value(0-255) (default = [90, 255, 255])
from_file (boolean): load data from file
plant-detection_inputs.json
plant-detection_p2c_calibration_parameters.json
plant-detection_plants.json
(default = False)
from_env_var (boolean): load data from environment variable,
overriding other parameter inputs
(default = False)
text_output (boolean): print text to STDOUT (default = True)
verbose (boolean): print verbose text to STDOUT.
otherwise, print condensed text output (default = True)
print_all_json (boolean): print all JSON data used to STDOUT
(default = False)
grey_out (boolean): grey out regions in image that have
not been selected (default = False)
draw_contours (boolean): draw an outline around the boundary of
detected plants (default = True)
circle_plants (boolean): draw an enclosing circle around
detected plants (default = True)
GUI (boolean): settings for the local GUI (default = False)
app (boolean): connect to the FarmBot web app (default = False)
app_image_id (string): use image from the FarmBot API (default = None)
Examples:
PD = PlantDetection()
PD.detect_plants()
PD = PlantDetection(
image='plant_detection/soil_image.jpg', morph=3, iterations=10,
debug=True)
PD.detect_plants()
PD = PlantDetection(
image='plant_detection/soil_image.jpg',
blur=9, morph=7, iterations=4,
calibration_img="plant_detection/p2c_test_calibration.jpg")
PD.calibrate()
PD.detect_plants()
PD = PlantDetection(
image='plant_detection/soil_image.jpg', blur=15, grey_out=True,
array=[
{"size": 5, "kernel": 'ellipse', "type": 'erode', "iters": 2},
{"size": 3, "kernel": 'ellipse', "type": 'dilate', "iters": 8}],
debug=True, clump_buster=False,
HSV_min=[30, 15, 15], HSV_max=[85, 245, 245])
PD.detect_plants()
"""
def __init__(self, **kwargs):
"""Read arguments (and change settings) and initialize modules."""
# Default Data Inputs
self.image = None
self.plant_db = DB()
# Default Parameter Inputs
self.params = Parameters()
self.params.add_missing_params('detect')
# Load keyword argument inputs
self._data_inputs(kwargs)
self._parameter_inputs(kwargs)
self.args = kwargs
# Set remaining arguments to defaults
self._set_defaults()
# Changes based on inputs
if self.args['calibration_img'] is not None:
# self.coordinates = True
self.args['coordinates'] = True
if self.args['GUI']:
self.args['save'] = False
self.args['text_output'] = False
if self.args['app']:
self.args['verbose'] = False
self.args['from_env_var'] = True
self.plant_db.app = True
# Remaining initialization
self.p2c = None
self.capture = Capture().capture
self.final_marked_image = None
self.plant_db.tmp_dir = None
def _set_defaults(self):
default_args = {
# Default Data Inputs
'image':
None,
'calibration_img':
None,
'known_plants':
None,
'app_image_id':
None,
'calibration_data':
None,
# Default Program Options
'coordinates':
False,
'from_file':
False,
'from_env_var':
False,
'clump_buster':
False,
'GUI':
False,
'app':
False,
# Default Output Options
'debug':
False,
'save':
True,
'text_output':
True,
'verbose':
True,
'print_all_json':
False,
'output_celeryscript_points':
False,
# Default Graphic Options
'grey_out':
False,
'draw_contours':
True,
'circle_plants':
True,
# Default processing options
'array':
None,
'blur':
self.params.parameters['blur'],
'morph':
self.params.parameters['morph'],
'iterations':
self.params.parameters['iterations'],
'HSV_min': [
self.params.parameters['H'][0], self.params.parameters['S'][0],
self.params.parameters['V'][0]
],
'HSV_max': [
self.params.parameters['H'][1], self.params.parameters['S'][1],
self.params.parameters['V'][1]
],
}
for key, value in default_args.items():
if key not in self.args:
self.args[key] = value
def _data_inputs(self, kwargs):
"""Load data inputs from keyword arguments."""
for key in kwargs:
if key == 'known_plants':
self.plant_db.plants['known'] = kwargs[key]
def _parameter_inputs(self, kwargs):
"""Load parameter inputs from keyword arguments."""
for key in kwargs:
if key == 'blur':
self.params.parameters['blur'] = kwargs[key]
if key == 'morph':
self.params.parameters['morph'] = kwargs[key]
if key == 'iterations':
self.params.parameters['iterations'] = kwargs[key]
if key == 'array':
self.params.array = kwargs[key]
if key == 'HSV_min':
hsv_min = kwargs[key]
self.params.parameters['H'][0] = hsv_min[0]
self.params.parameters['S'][0] = hsv_min[1]
self.params.parameters['V'][0] = hsv_min[2]
if key == 'HSV_max':
hsv_max = kwargs[key]
self.params.parameters['H'][1] = hsv_max[0]
self.params.parameters['S'][1] = hsv_max[1]
self.params.parameters['V'][1] = hsv_max[2]
def _calibration_input(self): # provide inputs to calibration
if self.args['app_image_id'] is not None:
self.args['calibration_img'] = int(self.args['app_image_id'])
if self.args['calibration_img'] is None and self.args['coordinates']:
# Calibration requested, but no image provided.
# Take a calibration image.
self.args['calibration_img'] = self.capture()
# Set calibration input parameters
if self.args['from_env_var']:
calibration_input = 'env_var'
elif self.args['from_file']: # try to load from file
calibration_input = 'file'
else: # Use default calibration inputs
calibration_input = None
# Call coordinate conversion module
self.p2c = Pixel2coord(self.plant_db,
calibration_image=self.args['calibration_img'],
calibration_data=self.args['calibration_data'],
load_data_from=calibration_input)
self.p2c.debug = self.args['debug']
def calibrate(self):
"""Calibrate the camera for plant detection.
Initialize the coordinate conversion module using a calibration image,
perform calibration, and save calibration data.
"""
self._calibration_input() # initialize coordinate conversion module
exit_flag = self.p2c.calibration() # perform calibration
if exit_flag:
sys.exit(0)
self._calibration_output() # save calibration data
def _calibration_output(self): # save calibration data
if self.args['save'] or self.args['debug']:
self.p2c.image.images['current'] = self.p2c.image.images['marked']
self.p2c.image.save('calibration_result')
# Print verbose results
if self.args['verbose'] and self.args['text_output']:
if self.p2c.calibration_params['total_rotation_angle'] != 0:
print(" Note: required rotation of "
"{:.2f} degrees executed.".format(
self.p2c.calibration_params['total_rotation_angle']))
if self.args['debug']:
# print number of objects detected
self.plant_db.print_count(calibration=True)
# print coordinate locations of calibration objects
self.p2c.p2c(self.plant_db)
self.plant_db.print_coordinates()
print('')
# Print condensed output if verbose output is not chosen
if self.args['text_output'] and not self.args['verbose']:
print("Calibration complete. (rotation:{}, scale:{})".format(
self.p2c.calibration_params['total_rotation_angle'],
self.p2c.calibration_params['coord_scale']))
# Send calibration result log toast
if self.args['app']:
log(
'Camera calibration complete; setting pixel coordinate scale'
' to {} and camera rotation to {} degrees.'.format(
self.p2c.calibration_params['coord_scale'],
self.p2c.calibration_params['total_rotation_angle']),
'success', 'Success', ['toast'], True)
# Save calibration data
if self.args['from_env_var']:
# to environment variable
self.p2c.save_calibration_data_to_env()
elif self.args['from_file']: # to file
self.p2c.save_calibration_parameters()
else: # to Parameters() instance
self.params.calibration_data = self.p2c.calibration_params
def _detection_input(self): # provide input to detect_plants
# Load input parameters
if self.args['from_file']:
# Requested to load detection parameters from file
try:
self.params.load('detect')
except IOError:
print("Warning: Input parameter file load failed. "
"Using defaults.")
self.plant_db.load_plants_from_file()
if self.args['app']:
self.plant_db.load_plants_from_web_app()
if self.args['from_env_var']:
# Requested to load detection parameters from json ENV variable
self.params.load_env_var('detect')
# Print input parameters and filename of image to process
if self.args['verbose'] and self.args['text_output']:
self.params.print_input()
print("\nProcessing image: {}".format(self.args['image']))
def _detection_image(self): # get image to process
self.image = Image(self.params, self.plant_db)
# Get image to process
try: # check for API image ID
image_id = self.args['app_image_id']
except KeyError:
image_id = None
if image_id is not None: # download image
try:
self.image.download(image_id)
except IOError:
print("Image download failed for image ID {}.".format(
str(image_id)))
sys.exit(0)
elif self.args['image'] is None: # No image provided. Capture one.
self.image.capture()
if self.args['debug']:
self.image.save('photo')
else: # Image provided. Load it.
filename = self.args['image']
self.image.load(filename)
self.image.debug = self.args['debug']
def _coordinate_conversion(self): # determine detected object coordinates
# Load calibration data
load_data_from = None
calibration_data = None
if self.args['from_env_var']:
load_data_from = 'env_var'
elif self.args['from_file']:
load_data_from = 'file'
else: # use data saved in self.params
calibration_data = self.params.calibration_data
# Initialize coordinate conversion module
self.p2c = Pixel2coord(self.plant_db,
load_data_from=load_data_from,
calibration_data=calibration_data)
self.p2c.debug = self.args['debug']
# Check for coordinate conversion calibration results
present = {
'coord_scale': False,
'camera_z': False,
'center_pixel_location': False,
'total_rotation_angle': False
}
try:
for key in present:
present[key] = self.p2c.calibration_params[key]
except KeyError:
log(
"ERROR: Coordinate conversion calibration values "
"not found. Run calibration first.",
message_type='error',
title='plant-detection')
sys.exit(0)
# Validate coordinate conversion calibration data for image
calibration_data_valid = self.p2c.validate_calibration_data(
self.image.images['current'])
if not calibration_data_valid:
log(
"ERROR: Coordinate conversion calibration values "
"invalid for provided image.",
message_type='error',
title='plant-detection')
sys.exit(0)
# Determine object coordinates
self.image.coordinates(self.p2c,
draw_contours=self.args['draw_contours'])
# Organize objects into plants and weeds
self.plant_db.identify(self.params.parameters)
if self.plant_db.plants['safe_remove']:
self.image.safe_remove(self.p2c)
def _coordinate_conversion_output(self): # output detected object data
# Print and output results
if self.args['text_output']:
self.plant_db.print_count() # print number of objects detected
if self.args['verbose'] and self.args['text_output']:
self.plant_db.print_identified() # print organized plant data
if self.args['output_celeryscript_points']:
self.plant_db.output_celery_script() # print points JSON to stdout
if self.args['app']:
save_detected_plants = self.params.parameters[
'save_detected_plants']
# add detected weeds and points to FarmBot Web App
self.plant_db.upload_plants(save_detected_plants)
if self.args['debug']:
self.image.save_annotated('contours')
self.image.images['current'] = self.image.images['marked']
self.image.save_annotated('coordinates_found')
if self.args['circle_plants']:
self.image.label(self.p2c) # mark objects with colored circles
self.image.grid(self.p2c) # add coordinate grid and features
def detect_plants(self):
"""Detect the green objects in the image."""
# Gather inputs
self._detection_input()
self._detection_image()
# Process image in preparation for detecting plants (blur, mask, morph)
self.image.initial_processing()
# Optionally break up masses by splitting them into quarters
if self.args['clump_buster']:
self.image.clump_buster()
# Optionally grey out regions not detected as objects
if self.args['grey_out']:
self.image.grey()
# Return coordinates if requested
if self.args['coordinates']: # Convert pixel locations to coordinates
self._coordinate_conversion()
self._coordinate_conversion_output()
else: # No coordinate conversion
# get pixel locations of objects
self.image.find(draw_contours=self.args['draw_contours'])
if self.args['circle_plants']:
self.image.label() # Mark plants with red circle
if self.args['debug']:
self.image.save_annotated('contours')
if self.args['text_output']:
self.plant_db.print_count() # print number of objects detected
if self.args['verbose'] and self.args['text_output']:
self.plant_db.print_pixel() # print object pixel location text
self.image.images['current'] = self.image.images['marked']
self._show_detection_output() # show output data
self._save_detection_output() # save output data
def _show_detection_output(self): # show detect_plants output
# Print raw JSON to STDOUT
if self.args['print_all_json']:
print("\nJSON:")
print(self.params.parameters)
print(self.plant_db.plants)
if self.p2c is not None:
print(self.p2c.calibration_params)
# Print condensed inputs if verbose output is not chosen
if self.args['text_output'] and not self.args['verbose']:
print('{}: {}'.format('known plants input',
self.plant_db.plants['known']))
print('{}: {}'.format('parameters input', self.params.parameters))
print('{}: {}'.format('coordinates input',
self.plant_db.coordinates))
def _save_detection_output(self): # save detect_plants output
# Final marked image
if self.args['save'] or self.args['debug']:
self.image.save('marked')
elif self.args['GUI']:
self.final_marked_image = self.image.images['marked']
# Save input parameters
if self.args['from_env_var']:
# to environment variable
self.params.save_to_env_var('detect')
elif self.args['save']:
# to file
self.params.save()
elif self.args['GUI']:
# to file for GUI
self.params.save()
# Save plants
if self.args['save']:
self.plant_db.save_plants()
def take_photo(self):
self.image = Image(self.params, self.plant_db)
self.image.capture()
self.image.save('Seedling_photo_' + strftime("%Y-%m-%d_%H:%M:%S", gmtime()))
class MyFarmware():
def __init__(self, farmwarename):
self.farmwarename = farmwarename
prefix = self.farmwarename.lower().replace('-', '_')
self.input_default_speed = int(os.environ.get(prefix + "_default_speed", 800))
self.x_photo_pos = 400
self.y_photo_pos = 235
self.z_photo_pos = 0
self.image = None
self.plant_db = DB()
self.params = Parameters()
self.plant_detection = None
self.dir = os.path.dirname(os.path.realpath(__file__)) + os.sep
""""self.api = API(self)
self.points = []"""
def mov_robot_origin(self):
log('Execute move: ', message_type='debug', title=str(self.farmwarename))
move_absolute(
location=[0, 0, 0],
offset=[0, 0, 0],
speed=800)
def mov_robot_photo(self):
log('Execute move: ', message_type='debug', title=str(self.farmwarename))
move_absolute(
location=[self.x_photo_pos, self.y_photo_pos, self.z_photo_pos],
offset=[0, 0, 0],
speed=800)
def take_photo(self):
self.image = Image(self.params, self.plant_db)
self.image.capture()
self.image.save('Seedling_photo_' + strftime("%Y-%m-%d_%H:%M:%S", gmtime()))
def process_photo(self):
self.plant_detection = PlantDetection(coordinates=True, app=True)
self.plant_detection.detect_plan()
"""def graph_plant_centroid(self):
def execute_sequence_init(self):"""
def save_data(self, idata):
HEADERS = {
'Authorization': 'bearer {}'.format(os.environ['FARMWARE_TOKEN']),
'content-type': 'application/json'}
def timestamp(value):
"""Add a timestamp to the pin value."""
return {'time': time(), 'value': value}
def append(data):
"""Add new data to existing data."""
try:
existing_data = json.loads(os.environ[LOCAL_STORE])
except KeyError:
existing_data = []
existing_data.append(data)
return existing_data
def wrap(data):
"""Wrap the data in a `set_user_env` Celery Script command to save it."""
return {
'kind': 'set_user_env',
'args': {},
'body': [{
'kind': 'pair',
'args': {
'label': LOCAL_STORE,
'value': json.dumps(data)
}}]}
def post(wrapped_data):
"""Send the Celery Script command."""
payload = json.dumps(wrapped_data)
requests.post(os.environ['FARMWARE_URL'] + 'api/v1/celery_script',
data=payload, headers=HEADERS)
"""log('Data is supposed to be saved')"""
def save_data_csv(value):
"""To save data into a csv file"""
with open(self.dir + 'db_plant_radius_test.csv', mode='a') as dbprt:
db_writer = csv.writer(dbprt, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
db_writer.writerow([strftime("%Y-%m-%d_%H:%M:%S", gmtime()), value])
log('Data is supposed to have been saved into db_plant_radius_test.csv')
LOCAL_STORE = 'test_data'
post(wrap(append(timestamp(save_data_csv(idata)))))
"""post(wrap(append(timestamp(idata))))"""
def plot_data(self):
TIME_SCALE_FACTOR = 60 * 2
DATA_SCALE_FACTOR = 2
RECENT = {'time': None}
def post(wrapped_data):
"""Send the Celery Script command."""
headers = {
'Authorization': 'bearer {}'.format(os.environ['FARMWARE_TOKEN']),
'content-type': 'application/json'}
payload = json.dumps(wrapped_data)
requests.post(os.environ['FARMWARE_URL'] + 'api/v1/celery_script',
data=payload, headers=headers)
def no_data_error():
"""Send an error to the log if there's no data."""
message = 'No data available'
wrapped_message = {
'kind': 'send_message',
'args': {
'message_type': 'error',
'message': message}}
post(wrapped_message)
def get_data():
"""Get existing historical pin data."""
data = json.loads(os.getenv('test_data', '[]'))
if len(data) < 1:
no_data_error()
sys.exit(0)
else:
return data
def reduce_data(data):
"""Reduce the loaded data for plotting."""
times, values = [], []
for record in data:
times.append(round(float(record['time']) / TIME_SCALE_FACTOR))
values.append(round(float(record['value']) / DATA_SCALE_FACTOR))
RECENT['time'] = max(times) * TIME_SCALE_FACTOR
times = abs(np.array(times) - max(times))
all_data = np.column_stack((times, values))
filtered_data = all_data[all_data[:, 0] < 720]
return filtered_data
def plot(data):
"""Plot the reduced data."""
# Create blank plot
p = np.full([512, 24 * 60 / 2], 255, np.uint8)
# Add shaded plot areas
for i in range(512):
if i < 100: # N/A
p[i, :] = 220
elif i > 425: # off
p[i, :] = 220
else: # sensor range (gradient)
p[i, :] = 255 - 175 * ((i - 100) / float(425 - 100))
# Add horizontal gridlines
for i in range(0, 512, 32):
p[i, :] = 100
if i == 384:
p[i, :] = 125
# Add minor vertical gridlines
for i in range(0, 720, 30):
p[:, i] = 100
# Add major vertical gridlines
for i in range(0, 720, 90):
p[:, i - 1:i + 1] = 100
# Add plot border
cv2.rectangle(p, (0, 0), (719, 511), 50, 4)
# Add data
for record in data:
reading_time = int(record[0])
reading_value = int(record[1])
cv2.circle(p, (reading_time, reading_value), 5, 0, 3)
# Flip plot to display oldest to newest, low to high
p = cv2.flip(p, -1)
# Create plot border label area
border = np.full([800, 600], 255, np.uint8)
def _add_labels(image_area, labels):
for label in labels:
cv2.putText(image_area, label['text'].upper(),
label['position'], 0, 0.5, 0, 1)
# Add sensor range text
"""range_labels = [{'text': 'off', 'position': (500, 25)},
{'text': 'wet', 'position': (425, 25)},
{'text': 'dry', 'position': (160, 25)},
{'text': 'n/a', 'position': (75, 25)}]
_add_labels(border, range_labels)"""
# Flip labels to display vertically
full = cv2.flip(cv2.transpose(border), 0)
# Add sensor value labels
value_labels = [{'text': '0', 'position': (760, 560)},
{'text': '512', 'position': (760, 305)},
{'text': '1023', 'position': (760, 50)}]
_add_labels(full, value_labels)
# Add most recent time
time_string = strftime('%b %d %H:%M UTC', gmtime(RECENT['time']))
_add_labels(full, [{'text': time_string, 'position': (650, 580)}])
# Add time offset labels
for i, column in enumerate(range(10, 600, 90)[::-1]):
_add_labels(full, [{'text': '-{} hr'.format(6 + i * 3),
'position': (column, 580)}])
# Add label area to plot area
full[44:556, 40:760] = p
# Add plot title
title = 'Database tests'
cv2.putText(full, title.upper(), (325, 25), 0, 0.75, 0, 2)
return full
def save(image):
"""Save the plot image."""
filename = '/test_data_plot_{}.png'.format(int(time()))
log('Image to be saved in: ' + self.dir + filename)
cv2.imwrite(self.dir + filename, image)
"""PIN = get_env('pin')
IS_SOIL_SENSOR = PIN == 59"""
save(plot(reduce_data(get_data())))
def run(self):
self.mov_robot_origin()
self.mov_robot_photo()
#self.take_photo()
#self.process_photo()
for i in range(0, 500, 21):
self.save_data(i)
self.plot_data()
sys.exit(0)