Upload app.py

app.py

@@ -0,0 +1,134 @@
+import os
+import cv2
+import numpy as np
+from collections import Counter
+from time import time
+import tkinter.filedialog
+from tkinter import *
+import sys
+import gradio as gr
+
+def k_nearest_neighbors(predict, k):
+    distances = []
+    for image in training_data:
+        distances.append([np.linalg.norm(image[0] - predict), image[1]]) # calcul de distance euclidienne
+    distances.sort()
+    votes = [i[1] for i in distances[:k]]
+    votes = ''.join(str(e) for e in votes)
+    votes = votes.replace(',', '')
+    votes = votes.replace(' ', '')
+    result = Counter(votes).most_common(1)[0][0]
+    return result
+
+
+def test():
+    start = time()
+    correct = 0
+    total = 0
+    skipped = 0
+    for i in range(len(x_test)+1):
+        try:
+            prediction = k_nearest_neighbors(x_test[i], 5)
+            if int(prediction) == y_test[i]:
+                correct += 1
+            total += 1
+        except Exception as e:
+            print('An exception occured')
+            skipped += 1
+    accuracy = correct/total
+    end = time()
+    print(end-start)
+    print(accuracy)
+
+def ia_handler(image):
+    pred = k_nearest_neighbors(img, 10)
+    if pred == 0:
+        return 'It\'s a coin'
+    return 'It\'s a banknote'
+
+def main():
+    if len(sys.argv) > 1 and sys.argv[1] == '--cli':
+        root = Tk()
+        root.withdraw()
+        root.update()
+        filename = tkinter.filedialog.askopenfilename(title="Ouvrir fichier", filetypes=[('all files', '.*')]) # sélectionner la photo
+        src = cv2.imread(cv2.samples.findFile(filename), cv2.IMREAD_COLOR) # charger la photo
+        root.destroy()
+        img = resize_img(src)
+        pred = k_nearest_neighbors(img, 10)
+        if pred == '0':
+            print('Coin')
+        else:
+            print('Banknote')
+    else:
+        iface = gr.Interface(fn=ia_handler, inputs="image", outputs="text")
+        iface.launch()
+
+
+def resize_img(img):
+    dim = (150, 150)
+    new_img = cv2.resize(img, dim)
+    return new_img
+
+if __name__=="__main__":
+    coin_datadir_train = '../coins-dataset/classified/train'
+    coin_datadir_test = '../coins-dataset/classified/test'
+    note_datadir_train = '../banknote-dataset/classified/train'
+    note_datadir_test = '../banknote-dataset/classified/test'
+
+    categories = ['1c', '2c', '5c', '10c', '20c', '50c', '1e', '2e', '5e', '10e', '20e', '50e']
+    coin_index = 8
+
+    training_data = []
+
+    for category in categories[:coin_index]:
+        path = os.path.join(coin_datadir_train, category)
+        label = 0
+        for img in os.listdir(path):
+            img_array = cv2.imread(os.path.join(path, img))
+            training_data.append([img_array, label])
+
+    for category in categories[coin_index:]:
+        path = os.path.join(note_datadir_train, category)
+        label = 1
+        for img in os.listdir(path):
+            img_array = resize_img(cv2.imread(os.path.join(path, img)))
+            training_data.append([img_array, label])
+
+
+    testing_data = []
+
+    for category in categories[:coin_index]:
+        path = os.path.join(coin_datadir_test, category)
+        label = 0
+        for img in os.listdir(path):
+            img_array = cv2.imread(os.path.join(path, img))
+            testing_data.append([img_array, label])
+
+    for category in categories[coin_index:]:
+        path = os.path.join(note_datadir_test, category)
+        label = 1
+        for img in os.listdir(path):
+            img_array = resize_img(cv2.imread(os.path.join(path, img)))
+            testing_data.append([img_array, label])
+
+
+    x_train = []
+    y_train = []
+
+    for features, label in training_data:
+        x_train.append(features)
+        y_train.append(label)
+        
+    x_train = np.array(x_train)
+
+
+    x_test = []
+    y_test = []
+
+    for features, label in testing_data:
+        x_test.append(features)
+        y_test.append(label)
+        
+    x_test = np.array(x_test)
+    main()