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Sarah HanIzu SotaniPeter MaNatka WojcikJustin Shenk
Published © GPL3+

Jetson Clean Water AI

Using AI object detection to detect water contamination.

AdvancedFull instructions providedOver 1 day6,504
Artificial Intelligence of Things (AIoT): 3rd Place
AI at the Edge Challenge

Things used in this project

Hardware components

NVIDIA Jetson Nano Developer Kit
NVIDIA Jetson Nano Developer Kit
×1
LattePanda 7-inch 1024 x 600 IPS Display
LattePanda 7-inch 1024 x 600 IPS Display
×1
AmScope Microscope
×1

Software apps and online services

TensorFlow
TensorFlow

Hand tools and fabrication machines

3D Printer (generic)
3D Printer (generic)

Story

Read more

Schematics

NVIDIA Jetson Nano

NVIDIA Jetson Nano

Code

labelmap.pbtxt

Plain text
Label file
item {
  id: 1
  name: 'E Coli'
}

item {
  id: 2
  name: 'Particle'
}

item {
  id: 3
  name: 'Yeast'
}

ssdlite_mobilenet_v3_large_320x320_coco.config

Plain text
config file for mobilenet v3 training
# SSDLite with Mobilenet v3 large feature extractor.
# Trained on COCO14, initialized from scratch.
# 3.22M parameters, 1.02B FLOPs
# TPU-compatible.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 3
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
}
}
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
box_predictor {
convolutional_box_predictor {
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 3
use_depthwise: true
box_code_size: 4
apply_sigmoid_to_scores: false
class_prediction_bias_init: -4.6
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.97,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v3_large'
min_depth: 16
depth_multiplier: 1.0
use_depthwise: true
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.97,
epsilon: 0.001,
}
}
override_base_feature_extractor_hyperparams: true
}
loss {
classific# SSDLite with Mobilenet v3 large feature extractor.
# Trained on COCO14, initialized from scratch.
# 3.22M parameters, 1.02B FLOPs
# TPU-compatible.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields thatation_loss {
weighted_sigmoid_focal {
alpha: 0.75,
gamma: 2.0
}
}
localization_loss {
weighted_smooth_l1 {
delta: 1.0
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 10
max_total_detections: 10
use_static_shapes: true
}
score_converter: SIGMOID
}
}
}
train_config: {
batch_size: 12
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 32
num_steps: 400000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
fine_tune_checkpoint: "/home/airig/workspace/models/research/object_detection/ssd_mobilenet_v3_large_coco_2019_08_14/model.ckpt"
fine_tune_checkpoint_type:  "detection"
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.4
total_steps: 400000
warmup_learning_rate: 0.13333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 10
unpad_groundtruth_tensors: false
}
train_input_reader: {
tf_record_input_reader {
input_path: "/home/airig/workspace/models/research/object_detection/train.record"
}
label_map_path: "/home/airig/workspace/models/research/object_detection/training/labelmap.pbtxt"
}
eval_config: {
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/home/airig/workspace/models/research/object_detection/test.record"
}
label_map_path: "/home/airig/workspace/models/research/object_detection/training/labelmap.pbtxt"
shuffle: false
num_readers: 1
}

clean_water_ai.py

Python
python file for Clean Water AI
# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

# Number of classes the object detector can identify
NUM_CLASSES = 3

## Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

    sess = tf.Session(graph=detection_graph)


# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Initialize webcam feed
video = cv2.VideoCapture(0)
ret = video.set(3,800)
ret = video.set(4,600)
i = 0
while(True):

    # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
    # i.e. a single-column array, where each item in the column has the pixel RGB value
    ret, frame = video.read()
    frame_expanded = np.expand_dims(frame, axis=0)

    # Perform the actual detection by running the model with the image as input
    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: frame_expanded})

    # Draw the results of the detection (aka 'visulaize the results')
    vis_util.visualize_boxes_and_labels_on_image_array(
        frame,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=8,
        min_score_thresh=0.51)
    if i == 20:
        i = 0
        vis_util.save_image_array_as_png(frame, "/home/ai/workspace/tensorflow1/models/research/object_detection/stream/stream.png")
    # All the results have been drawn on the frame, so it's time to display it.
    cv2.imshow('Clean Water AI', frame)
    i += 1
    # Press 'q' to quit

Jetson Clean Water AI Repo

Jetson Clean Water AI Repo

Credits

Sarah Han
13 projects • 79 followers
Software Engineer, Design, 3D
Izu Sotani
1 project • 1 follower
TechEnthusiast, FuturisticOptimist, HackathonContributor, Naturalist, EcologicalBalancer, /Professional:Finance,Technologist ❤︎Healthcare ∞
Peter Ma
49 projects • 394 followers
Prototype Hacker, Hackathon Goer, World Traveler, Ecological balancer, integrationist, technologist, futurist.
Natka Wojcik
1 project • 2 followers
Justin Shenk
3 projects • 27 followers
Machine learning and computer vision research engineer with background in neuroscience.
Thanks to Peter Ma.

Comments

Awards
Artificial Intelligence of Things (AIoT): 3rd Place
AI at the Edge Challenge
Related channels and tags
  • DFRobot
  • Machine Learning & AI
  • Machine Learning & AI
  • NVIDIA
  • TensorFlow