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demo_app.py

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+from skimage import io
+import base64
+from tensorflow.keras.models import load_model
+import numpy as np
+import gradio
+import moleimages
+
+model = load_model("mymodel-2.h5")
+
+def predict(input):
+    mimg = moleimages.MoleImages()
+    X = mimg.load_image(input)
+    y_pred = model.predict(X)
+    return {"Benign": float(y_pred[0][0]), "Malignant": float(1-y_pred[0][0])}
+
+io = gradio.Interface(fn=predict, inputs='image', outputs='label', analytics_enabled=True,
+	title="Skin Cancer Detection FYP Model Demo", description="FYP Demo by FYP GROUP:- 20F-18 \n [Leader: Humza Ali \n | Members: Dilshad Hussain, Yasir Ali]")
+io.launch()

moleimages.py

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+import numpy as np
+from skimage import io
+from skimage.transform import resize
+
+import matplotlib.pyplot as plt
+import glob
+import h5py
+
+
+class MoleImages():
+    def __init__(self, dir=None):
+        self.dir = dir
+        self.size = None
+
+    def resize_bulk(self, size=(128,128)):
+        '''
+        Resize Images and create matrix
+        Input: size of the images (128,128)
+        Output: Numpy array of (size,num_images)
+        '''
+        self.size = size
+        X = []
+        image_list = glob.glob(self.dir)
+        n_images = len(image_list)
+        print('Resizing {} images:'.format(n_images))
+        for i, imgfile in enumerate(image_list):
+            print('Resizing image {} of {}'.format(i+1, n_images))
+            img = io.imread(imgfile)
+            img = resize(img, self.size)
+            X.append(img)
+        return np.array(X)
+
+    def load_test_images(self, dir_b, dir_m):
+        X = []
+        image_list_b = glob.glob(dir_b + '/*.png')
+        n_images_b = len(image_list_b)
+        print('Loading {} images of class benign:'.format(n_images_b))
+        for i, imgfile in enumerate(image_list_b):
+            print('Loading image {} of {}'.format(i+1, n_images_b))
+            img = io.imread(imgfile)
+            X.append(img)
+        image_list_m = glob.glob(dir_m + '/*.png')
+        n_images_m = len(image_list_m)
+        print('Loading {} images of class benign:'.format(n_images_m))
+        for i, imgfile in enumerate(image_list_m):
+            print('Loading image {} of {}'.format(i+1, n_images_m))
+            img = io.imread(imgfile)
+            X.append(img)
+        y = np.hstack((np.zeros(n_images_b), np.ones(n_images_m)))
+
+        return np.array(X), y.reshape(len(y),1)
+
+    def load_image_from_filename(self, filename, size=(128,128)):
+        self.size = size
+        img = io.imread(filename)
+        img = resize(img, self.size, mode='constant') * 255
+        if img.shape[2] == 4:
+            img = img[:,:,0:3]
+        return img.reshape(1, self.size[0], self.size[1], 3)
+
+    def load_image(self, img, size=(128,128)):
+        self.size = size
+        img = resize(img, self.size, mode='constant', anti_aliasing=True,
+                     anti_aliasing_sigma=None) * 255
+        if img.shape[2] == 4:
+            img = img[:, :, 0:3]
+        return img.reshape(1, self.size[0], self.size[1], 3)
+
+    def save_h5(self, X, filename, dataset):
+        '''
+        Save a numpy array to a data.h5 file specified.
+        Input:
+        X: Numpy array to save
+        filename: name of h5 file
+        dataset: label for the dataset
+        '''
+        with h5py.File(filename, 'w') as hf:
+            hf.create_dataset(dataset, data=X)
+        print('File {} saved'.format(filename))
+
+    def load_h5(self, filename, dataset):
+        '''
+        Load a data.h5 file specified.
+        Input: filename, dataset
+        Output: Data
+        '''
+        with h5py.File(filename, 'r') as hf:
+            return hf[dataset][:]
+
+    def save_png(self, matrix, dir, tag='img', format='png'):
+        for i, img in enumerate(matrix):
+            if dir[-1] != '/':
+                filename = dir + '/' + tag + str(i) + '.' + format
+            else:
+                filename = dir + tag + str(i) + '.' + format
+            print('Saving file {}'.format(filename))
+            io.imsave(filename, img)
+
+
+
+if __name__ == '__main__':
+    # benign = MoleImages('data/malignant/*.jpg')
+    # ben_images = benign.resize_bulk()
+    # print('Shape of benign images: ', ben_images.shape)
+    # benign.save_h5(ben_images, 'benigns.h5', 'benign')
+    benign = MoleImages()
+    X = benign.load_h5('benigns.h5','benign')

mymodel-2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51e475095c9009d75af1daab0fbbd4a773d232a01df7bcb7e32046a476086c8c
+size 10025768