completed unauthorized animal identification by scikit-learn

animalIdentification.py

+from bing_image_downloader import downloader
+downloader.download("civet",limit =30,output_dir='images',
+                    adult_filter_off=True)
+
+from bing_image_downloader import downloader
+downloader.download("cat",limit =30,output_dir='images',
+                    adult_filter_off=True)
+
+from bing_image_downloader import downloader
+downloader.download("rat",limit =40,output_dir='images',
+                    adult_filter_off=True)
+
+# prepocess
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+
+target = []
+images = []
+flat_data = []
+DATADIR = '/content/images'
+CATEGORIES = ['civet', 'cat', 'snakes', 'rat']
+
+for category in CATEGORIES:
+    class_num = CATEGORIES.index(category)  # label encoding
+    path = os.path.join(DATADIR, category)  # create path to use all images
+    for img in os.listdir(path):
+        img_array = imread(os.path.join(path, img))
+        # print(img_array.shape)
+        # plt.imshow(img_array)
+        img_resized = resize(img_array, (150, 150, 3))  # Normalizes to 0 to 1
+        flat_data.append(img_resized.flatten())
+        images.append(img_resized)
+        target.append(class_num)
+
+flat_data = np.array(flat_data)
+target = np.array(target)
+images = np.array(images)
+
+# split data into Training and Testing
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(flat_data, target,
+                                                    test_size=0.3, random_state=109)
+
+from sklearn.model_selection import GridSearchCV
+from sklearn import svm  # support vector machine for a model
+
+param_grid = [
+
+    {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
+    {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
+]
+
+svc = svm.SVC(probability=True)
+clf = GridSearchCV(svc, param_grid)
+clf.fit(X_train, y_train)
+
+y_pred = clf.predict(X_test)
+y_pred
+
+y_test
+
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+accuracy_score(y_pred, y_test)  # check Accuracy
+
+confusion_matrix(y_pred, y_test)
+
+# save the model using pickle library
+import pickle
+
+pickle.dump(clf, open('img_model.p', 'wb'))
+
+model = pickle.load(open('img_model.p', 'rb'))
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