code
codeunknown
python
3 years ago
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#image_batch = "malign.png" classes = os.listdir("/content/drive/MyDrive/DDSM Dataset/") matrix = { "Benign Masses1":0, # bon "Malignant Masses1": 0, # mauvais "Benign Masses2":0, # mauvais "Malignant Masses2": 0 # bon } i, s = 0, 0 for classe in classes: i+=1 path = f"drive/MyDrive/MIAS_Dataset/{classe}/" images = os.listdir(path) for image in images: img = tf.keras.utils.load_img(path+image, target_size=(img_height, img_width)) img_array = tf.keras.utils.img_to_array(img) img_array = tf.expand_dims(img_array, 0) # Create a batch predictions = model.predict(img_array) score = tf.nn.softmax(predictions[0]) if class_names[np.argmax(score)] == classe: matrix[f"{classe}{i}"] += 1 # if the class is correct else: if i == 1 : matrix[f"Benign Masses{2}"] += 1 # if the class is not correct (i==1), then save in the other classe (i==2) else: matrix[f"Malignant Masses{1}"] += 1 # if the class is not correct (i==2), then save in the other classe (i==1) if s%100 == 0: print(f"{round(s*100/3816)}%") s +=1 #print( # "This image most likely belongs to {} with a {:.2f} percent confidence." # .format(class_names[np.argmax(score)], 100 * np.max(score)))