Sarah HanIzu SotaniPeter MaNatka WojcikJustin Shenk
Created February 1, 2020 © GPL3+

Jetson Clean Water AI

Using AI object detection to detect water contamination.

AdvancedFull instructions providedOver 1 day1,495

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

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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

Sarah Han

10 projects • 49 followers
Software Engineer, Design, 3D
Izu Sotani

Izu Sotani

1 project • 0 followers
TechEnthusiast, FuturisticOptimist, HackathonContributor, Naturalist, EcologicalBalancer, /Professional:Finance,Technologist ❤︎Healthcare ∞
Peter Ma

Peter Ma

44 projects • 293 followers
Prototype Hacker, Hackathon Goer, World Traveler, Ecological balancer, integrationist, technologist, futurist.
Natka Wojcik

Natka Wojcik

1 project • 2 followers
Justin Shenk

Justin Shenk

3 projects • 21 followers
Machine learning and computer vision research engineer with background in neuroscience.
Thanks to Peter Ma.

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