Prepare for KNN test in CVSuite
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@ -3,9 +3,9 @@ import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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import csv
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
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from enum import Enum
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import random
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, matthews_corrcoef
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class Tree(Enum):
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@ -19,7 +19,8 @@ class Tree(Enum):
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PLATAAN = 7
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# Open file
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file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
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# file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
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file = open('./out/result-2023-10-10T15.08.36.csv', "r")
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data = list(csv.reader(file, delimiter=","))
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file.close()
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@ -32,7 +33,7 @@ tags_int = []
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for row in data:
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tree = row.pop(0)
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row.pop(1) # TODO: Doe dit niet
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# photoId = row.pop(1)
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id = Tree[tree.upper()]
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# print("Tree name =", tree, " id =", id.value)
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@ -62,26 +63,26 @@ for idx, col in enumerate(data[0]):
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column = np.array(column).reshape(len(column))
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# DEBUG Print resulting column
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print("NORM", header[idx + 1], "\n", column)
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# print("NORM", header[idx + 1], "\n", column)
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# Replace original data array
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data[:, idx] = column
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# # Get a random number for testing
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# validateId = random.randint(0, tags_len - 1)
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tag_true = []
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tag_predict = []
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# tag_true = []
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# tag_predict = []
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print(tags_len)
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# print(tags_len)
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for validateId in range(0, tags_len - 1):
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# Remove object from train set
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validateTag = tags_int[validateId]
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validateObj =np.array([data[validateId]])
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np.delete(tags_int, validateId)
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np.delete(data, validateTag)
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# for validateId in range(0, tags_len - 1):
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# # Remove object from train set
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# validateTag = tags_int[validateId]
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# validateObj =np.array([data[validateId]])
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# np.delete(tags_int, validateId)
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# np.delete(data, validateTag)
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tag_true.append(validateTag)
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# tag_true.append(validateTag)
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# print(validateTag, validateObj)
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@ -89,28 +90,30 @@ for validateId in range(0, tags_len - 1):
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print(tags_int)
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print(data.dtype, type(data), tags_int.dtype, type(tags_int))
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knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
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knn.save('./out/models/knn_nosift.pkl')
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# print (data)
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# print('--------------------')
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# print (validateObj)
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ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
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tag_predict.append(results[0][0])
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# ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
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# tag_predict.append(results[0][0])
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# print( "result: {}\n".format(results) )
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# print( "neighbours: {}\n".format(neighbours) )
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# print( "distance: {}\n".format(dist) )
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# Create a heatmap
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sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
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plt.title( "Confusion Matrix KNN" )
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plt.show()
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# # Create a heatmap
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# sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
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# plt.title( "Confusion Matrix KNN" )
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# plt.show()
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# Score
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print("Accuracy score", accuracy_score(tag_true, tag_predict))
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print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
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print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
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print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
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print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
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print("MCC", matthews_corrcoef(tag_true, tag_predict))
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# print("Accuracy score", accuracy_score(tag_true, tag_predict))
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# print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
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# print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
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# print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
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# print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
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# print("MCC", matthews_corrcoef(tag_true, tag_predict))
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@ -323,6 +323,7 @@
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<child>
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<object class="tk.Text" id="testdata" named="True">
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<property name="height">15</property>
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<property name="state">disabled</property>
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<property name="text" translatable="yes">No tests have been run yet</property>
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<property name="undo">false</property>
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<property name="width">25</property>
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@ -0,0 +1,103 @@
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import cv2 as cv
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import numpy as np
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import csv
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
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import argparse
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from enum import Enum
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parser = argparse.ArgumentParser(prog='KNN Train CLI')
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parser.add_argument('-i', '--input', help='Input CSV file', required=True)
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parser.add_argument('-o', '--output', help='Output model file', required=True)
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class Tree(Enum):
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ACCASIA = 0
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BERK = 1
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EIK = 2
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ELS = 3
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ESDOORN = 4
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ES = 5
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LINDE = 6
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PLATAAN = 7
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class CVSuiteTestKNN:
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def __init__(self, model = None):
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if model is None:
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self.knn = cv.ml.KNearest_create()
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self.trained = False
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else:
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self.knn = cv.ml.KNearest_load(model)
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self.trained = True
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def trainCSV(self, path, output):
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'''
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Takes preprocessed data from CVSuite, normalises it and trains the model
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Function expects first two columns of the dataset to be tag and photoId, the first row should be the CSV header
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'''
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file = open(path, mode='r')
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data = list(csv.reader(file, delimiter=","))
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file.close()
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header = data.pop(0)
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print("CSV tags: ", header)
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# Get classifier tags
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tags_int = []
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for row in data:
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tree = row.pop(0)
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# photoId = row.pop(1)
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id = Tree[tree.upper()]
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# print("Tree name =", tree, " id =", id.value)
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tags_int.append(id.value)
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# Make into numpy array cus OpenCV is dumb af
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tags_len = len(tags_int)
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tags_int = np.array(tags_int, dtype=np.int32)
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# Transform array for normalisation
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data = np.array(data, dtype=np.float32)
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for idx, col in enumerate(data[0]):
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# Get column from data
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column = data[:, idx]
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# Shape it to 2 dimentional
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column = np.array(column).reshape(-1, 1)
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# Perform Min - Max scaling
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# scaler = MinMaxScaler()
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scaler = MaxAbsScaler()
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column = scaler.fit_transform(column)
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# Reshape it back cus scaler is dumb af
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column = np.array(column).reshape(len(column))
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# DEBUG Print resulting column
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# print("NORM", header[idx + 1], "\n", column)
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# Replace original data array
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data[:, idx] = column
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# Pass data to train function
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self.train(data, tags_int, output)
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def train(self, data, tags, output):
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'''
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Data should be normalised before being passed to this function
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This function should not be run from within the suite
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'''
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if self.trained:
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throw("Model already trained!")
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else:
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self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
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self.knn.save(output)
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def predict(self, data):
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return self.knn.predict(data)
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if __name__ == "__main__":
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args = parser.parse_args()
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test = CVSuiteTestKNN()
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test.trainCSV(args.input, args.output)
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19
src/suite.py
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src/suite.py
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@ -19,6 +19,9 @@ from helpers.logger import CVSuiteLogger, C_DBUG
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from helpers.canvas import CVSuiteCanvas
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from helpers.sift import getSiftData
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# Tests
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from helpers.test.knn import CVSuiteTestKNN
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## UI config load
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PROJECT_PATH = pathlib.Path(__file__).parent
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PROJECT_UI = "./src/helpers/gui/main.ui"
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@ -81,6 +84,9 @@ class CVSuite:
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)
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builder.connect_callbacks(self)
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# Model tests
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self.test_knn = CVSuiteTestKNN(config_json["models"]["knn"])
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# Load values from config after UI has been initialised
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self.img_path.set(config_json["path"])
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self.img_size.set(config_json["size"])
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@ -267,6 +273,15 @@ class CVSuite:
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self.log.add(f"Mean {label}", mean[idx])
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self.log.add(f"Std {label}", std[idx])
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def runTest(self, event=None):
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output = self.builder.get_object("testdata")
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output.configure(state="normal")
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output.delete(1.0, "end")
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output.insert("end", "test\n")
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output.configure(state="disabled")
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def updatePath(self):
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"""
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Only update image name and path
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@ -406,6 +421,9 @@ class CVSuite:
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self.log.add("SIFT total response", siftData[5])
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self.log.add("SIFT average response", siftData[6])
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# Run tests
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self.runTest()
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# Write results to CSV file
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if not part_update:
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self.log.update()
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@ -416,7 +434,6 @@ class CVSuite:
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plt.show(block=False) ## Graphs
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self.canvas.draw(size) ## Images
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if __name__ == "__main__":
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app = CVSuite()
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app.run()
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