ecmv 0.1.4

Support code for the EXE3002 - Classifiers and Machine Vision written assignment

Exeter Collage Machine Vision

Code for the EXE3002 - Classifiers and Machine Vision written assignment

Index

• Overview
• Installation
• Documentation
• Examples
• Support

Overview

This package aims to provide a robust framework within which to demonstrate machine vision skills

It should serve as the starting point the code associated with the machine vision aspect of the EXE3002 Classifiers and Machine Vision written assignment.

Installation

Install the package via pip or your favourite package manager

$ pip install ecmv

When you first import the module you will be asked to download the dataset. The module cannot be used without doing this step. The dataset is about 205.73 Mb and may take a few minutes to download.

$ python -c 'import ecmv'

Rice image dataset not found in PATH_TO_DATASET.
Download it? (Required for the ecmv package to function) [y/n]: y
Cleaning dataset
Cloning into '/Users/joe/Library/Application Support/ecmv'...
remote: Enumerating objects: 74714, done.
remote: Counting objects: 100% (3/3), done.
remote: Total 74714 (delta 0), reused 3 (delta 0), pack-reused 74711
Receiving objects: 100% (74714/74714), 205.73 MiB | 4.76 MiB/s, done.
Resolving deltas: 100% (3/3), done.
Updating files: 100% (75002/75002), done.

If any errors are encountered, they should be resolevd automatically. If they persist you can debug them as follows:

1. Set the ECMV_VERBOSE environment variable. This will force the programme to output key information relating to the handling of the dataset

   MacOS & Linux

   $ export ECMV_VERBOSE=True
   

   Windows

   > set ECMV_VERBOSE=True
   

2. Import the ecmv package

   $ python -c "import ecmv"
   

   Dataset location: PATH_TO_DATASET
   ...
   

3. Remove the directory

   MacOS & Linux

   $ rmdir -r  PATH_TO_DATASET
   

   Windows

   > rd /s PATH_TO_DATASET
   

Documentation

Structure

ecmv
├── features
│   ├── Features 
│   └── get_feature_names
└── extract
    ├── apply_to_dataset 
    └── test

Features Module

features.Features

An Enum defining precalculated features

class Features(Enum):
    FName     = 1
    Class     = 2
    Length    = 3
    Width     = 4
    Perimeter = 5

Attributes

Fname : str
  The jpg file name

Class : str
  The rice species identifier

  A ─> Arborio
  B ─> Basmati
  I ─> Ipsala
  J ─> Jasmine
  K ─> Karacadag

Perimiter : float
  The non-dimensional perimiter of the rice grain. 
  (Normalised by the image size)

Length : float
  The non-dimensional length of the rice grain. 
  (Normalised by the image size)

Width : float
  The non-dimensional width of the rice grain. 
  (Normalised by the image size)

features.get_feature_names

An getter for the names of the available preclculated features

def get_feature_names() -> list[str]:
    ...

Returns

names : list[str]
  A list of the names of the available preclculated features

Extract Module

extract.generate_dataset

A function to extract features from the 75,000 images in the CINAR & KOKLU rice dataset.

@check_features
def generate_dataset(*features, shuffle = False, seed = 42) -> pd.DataFrame:
    ...

Parameters

*features : Callable(str) | Feature
    An array of features to be extracted from each image in the dataset.
    Must be either:
        a) A function f(path) -> float accepting a path to an jpg file
        b) A features.Features enum corrasponding to a precalculated
           feature

shuffle : bool = True
    A boolean to determine if the images are to be shuffled prior to extraction.

seed : int = None
    Ensures a repeatable shuffle if not None.

Returns

data : pd.Dataframe
    A pandas dataframe where each row contains the features extracted from an image

extract.test

A function to extract features from the a single im age from the CINAR & KOKLU rice dataset for testing and development purposes.

@check_features
def test(*features, shuffle = False, seed = 42) -> pd.DataFrame:
    ...

Parameters

*features : Callable(str) | Feature
    An array of features to be extracted from each image in the dataset.
    Must be either:
        a) A function f(path) -> float accepting a path to an jpg file
        b) A features.Features enum corrasponding to a precalculated
           feature

shuffle : bool = True
    A boolean to the features shoukld be extracted from a random image

seed : int = None
    Ensures a repeatable shuffle if not None.

Returns

data : pd.Dataframe
    A pandas dataframe where each row contains the features extracted from an image

Examples

Example 1 - Function Test

Example testing a function foo on a single rice image

Code

# example_test.py

import ecmv

from PIL import Image

from ecmv.features import Features
from matplotlib import pyplot as plt


def foo(path):

    with Image.open(path) as im:
        im.show()

    return 0.0


sample = ecmv.extract.test(
    Features.Class, Features.Length, Features.Width, foo, shuffle=True, seed=42
)

print(sample)

Output

$ python example_test.py
  Class    Length     Width  foo
0     I  0.956632  0.482663  0.0

Example 2 - Function Application

Example extracting a feature with function foo from every rice image

# example_dataset.py

import ecmv

import numpy as np
import seaborn as sns

from PIL import Image
from ecmv.features import Features

from matplotlib import pyplot as plt


def mean_red(path):
    with Image.open(path) as im:

        red = im.getchannel("R")

        return np.mean(red)

    return np.NN


df = ecmv.extract.generate_dataset(
    Features.Class,
    Features.Length,
    Features.Width,
    Features.Perimeter,
    mean_red,
)


sns.pairplot(df, hue="Class")
plt.show()

Output

Example 3 - Classification

# example_classify.py

import ecmv

import numpy as np

from ecmv.features import Features

from matplotlib import pyplot as plt

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split


df = ecmv.extract.generate_dataset(
    Features.Class, Features.Length, Features.Width, Features.Perimeter
)

y = df["Class"]
X = df[["Length", "Width", "Perimeter"]]


X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.333, random_state=42
)

classifier = DecisionTreeClassifier(max_depth=2)
classifier.fit(X_train, y_train)

score = classifier.score(X_test, y_test) * 100
print(f"Classifier Score: {score:3.2f}%")

Output

$ python example_classify.py
Classifier Score: 75.99%

Support

If you are struggling to use this code, please contact your supervisor.