def main():
# Take in camera options
args = sys.argv[1:]
num_args = len(args)
if num_args != 5:
print("Did not input enough camera options")
sys.exit()
iso, cont, brt, sat, shutter = args
#Set Raspistill image options
dir = "/home/pi/"
fileName = "img_" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".jpg"
cmd = "raspistill --ISO {} --contrast {} -- brightness {} --drc high -k --saturation {} --shutter {} -o ".format(
iso, cont, brt, sat, shutter) + dir + fileName
subprocess.call(cmd, shell=True)
#Print model data
model_wrapper = model.ModelWrapper()
input_shape = model_wrapper.GetInputShape()
output_shape = model_wrapper.GetOutputShape()
preprocessing_metadata = helpers.get_image_preprocessing_metadata(
model_wrapper)
#Open image and run recognition
my_file = Path(fileName)
if not my_file.is_file():
print("File Not Found")
sys.exit()
sample_image = cv2.imread(fileName)
input_data = helpers.prepare_image_for_model(
sample_image,
input_shape.columns,
input_shape.rows,
preprocessing_metadata=preprocessing_metadata)
input_data = model.FloatVector(input_data)
predictions = model_wrapper.Predict(input_data)
prediction_index = int(np.argmax(predictions))
c_file = open("categories.txt", "r")
list_of_categories = [(line.strip()).split() for line in c_file]
c_file.close()
#Print Results
print("Model input shape: [{0.rows}, {0.columns}, {0.channels}]".format(
input_shape))
print("Model output shape: [{0.rows}, {0.columns}, {0.channels}]".format(
output_shape))
print("Category index: {}".format(prediction_index))
print("Confidence: {}".format(predictions[prediction_index]))
print("This object ({}) is a {} with confidence of {}".format(
fileName, list_of_categories[prediction_index],
predictions[prediction_index]))
def main():
camera = cv2.VideoCapture(0)
# request a specific resolution (sometimes the camera has very small default resolution)
helpers.set_camera_resolution(camera, 1280, 720)
with open("categories.txt", "r") as categories_file:
categories = categories_file.read().splitlines()
# Get the model wrapper in order to interact with the model
model_wrapper = model.ModelWrapper()
input_shape = model_wrapper.GetInputShape()
output_shape = model_wrapper.GetOutputShape()
# Get the model-specific preprocessing metadata
preprocessing_metadata = helpers.get_image_preprocessing_metadata(model_wrapper)
while (cv2.waitKey(1) & 0xFF) != 27:
# Get the image from the camera
original = get_image_from_camera(camera)
# Prepare the image to pass to the model. This helper crops and resizes
# the image maintaining proper aspect ratio and return the resultant
# image instead of a numpy array. Additionally, the helper will
# reorder the image from BGR to RGB
image, offset, scale = helpers.prepare_image_for_model(
original, input_shape.columns, input_shape.rows, reorder_to_rgb=True,
ravel=False, preprocessing_metadata=preprocessing_metadata)
# Wrap the resulting numpy array in a FloatVector
input_data = model.FloatVector(image)
# Get the predictions by running the model. `predictions` is returned
# as a flat array
predictions = model_wrapper.Predict(input_data)
# Reshape the output of the model into a tensor that matches the
# expected shape
predictions = np.reshape(
predictions,
(13, 13, 125))
# Do some post-processing to extract the regions from the output of
# the model
regions = helpers.get_regions(
predictions, categories, CONFIDENCE_THRESHOLD, ANCHOR_BOXES)
# Get rid of any overlapping regions for the same object
regions = helpers.non_max_suppression(
regions, OVERLAP_THRESHOLD, categories)
# Draw the regions onto the image
scale = (scale[0] * image.shape[1], scale[1] * image.shape[0])
helpers.draw_regions_on_image(original, regions, offset, scale)
# Display the image
cv2.imshow("Region detection", original)
def main():
init_GPIO()
init_camera()
init_options()
shoot_categories = set([504])
with open("categories.txt", "r") as categories_file:
categories = categories_file.read().splitlines()
model_wrapper = model.ModelWrapper()
input_shape = model_wrapper.GetInputShape()
preprocessing_metadata = helpers.get_image_preprocessing_metadata(
model_wrapper)
while (cv2.waitKey(1) & 0xFF) == 0xFF:
image = get_image_from_camera(camera)
input_data = helpers.prepare_image_for_model(
image,
input_shape.columns,
input_shape.rows,
preprocessing_metadata=preprocessing_metadata)
input_data = model.FloatVector(input_data)
predictions = model_wrapper.Predict(input_data)
top_5 = helpers.get_top_n(predictions, 5)
header_text = ", ".join([
"({:.0%}) {}".format(element[1], categories[element[0]])
for element in top_5
])
helpers.draw_header(image, header_text)
if top_5:
print(header_text)
release_shutter()
for element in top_5:
if verbose:
print(element[0])
if element[0] in shoot_categories:
press_shutter()
break
cv2.imshow("BirdWatcher", image)
rawCapture.truncate(0)
def main():
camera = cv2.VideoCapture(0)
with open("categories.txt", "r") as categories_file:
categories = categories_file.read().splitlines()
model_wrapper = model.ModelWrapper()
input_shape = model_wrapper.GetInputShape()
preprocessing_metadata = helpers.get_image_preprocessing_metadata(
model_wrapper)
while (cv2.waitKey(1) & 0xFF) == 0xFF:
image = get_image_from_camera(camera)
input_data = helpers.prepare_image_for_model(
image,
input_shape.columns,
input_shape.rows,
preprocessing_metadata=preprocessing_metadata)
input_data = model.FloatVector(input_data)
predictions = model_wrapper.Predict(input_data)
top_5 = helpers.get_top_n(predictions, 5)
header_text = ", ".join([
"({:.0%}) {}".format(element[1], categories[element[0]])
for element in top_5
])
helpers.draw_header(image, header_text)
cv2.imshow("ELL model", image)
def main():
"""Entry point for the script when called directly"""
# Open the video camera. To use a different camera, change the camera
# index.
camera = cv2.VideoCapture(0)
# Read the category names
with open("dogs.txt", "r") as dogs_file,\
open("cats.txt", "r") as cats_file:
dogs = dogs_file.read().splitlines()
cats = cats_file.read().splitlines()
# Get the model wrapper in order to interact with the model
model_wrapper = model.ModelWrapper()
# Get the model's input dimensions. We'll use this information later to
# resize images appropriately.
input_shape = model_wrapper.GetInputShape()
# Get the model-specific preprocessing metadata
preprocessing_metadata = helpers.get_image_preprocessing_metadata(
model_wrapper)
while (cv2.waitKey(1) & 0xFF) == 0xFF:
# Get an image from the camera. If you'd like to use a different image,
# load the image from some other source.
image = get_image_from_camera(camera)
# Prepare the image to pass to the model. This helper:
# - crops and resizes the image maintaining proper aspect ratio
# - reorders the image channels if needed
# - returns the data as a ravelled numpy array of floats so it can be
# handed to the model
input_data = helpers.prepare_image_for_model(
image,
input_shape.columns,
input_shape.rows,
preprocessing_metadata=preprocessing_metadata)
# Wrap the resulting numpy array in a FloatVector
input_data = model.FloatVector(input_data)
# Get the predicted classes using the model's predict function on the
# image input data. The predictions are returned as a numpy array with the
# probability that the image # contains the class represented by that
# index.
predictions = model_wrapper.Predict(input_data)
# Let's grab the value of the top prediction and its index, which
# represents the top most confident match and the class or category it
# belongs to.
top_n = helpers.get_top_n(predictions, 1, threshold=0.05)
# See whether the prediction is in one of our groups
group = ""
label = ""
if top_n:
top = top_n[0][0]
if prediction_index_in_set(top, dogs):
group = "Dog"
elif prediction_index_in_set(top, cats):
group = "Cat"
header_text = ""
if group:
# A group was detected, so take action
top = top_n[0]
take_action(group)
header_text = "({:.0%}) {}".format(top[1], group)
helpers.draw_header(image, header_text)
# Display the image using opencv
cv2.imshow("Grouping", image)
#
# Requires: Python 3.x
#
###############################################################################
import cv2
import numpy as np
# Import helper functions
import tutorial_helpers as helpers
# Load the wrapped model's Python module
import model
# Get the model wrapper in order to interact with the model
model_wrapper = model.ModelWrapper()
# Get the input and output shapes
input_shape = model_wrapper.GetInputShape()
output_shape = model_wrapper.GetOutputShape()
# Get the model-specific preprocessing metadata
preprocessing_metadata = helpers.get_image_preprocessing_metadata(
model_wrapper)
print("Model input shape: [{0.rows}, {0.columns}, {0.channels}]".format(
input_shape))
print("Model output shape: [{0.rows}, {0.columns}, {0.channels}]".format(
output_shape))
# Read in the sample image