吴裕雄--天生自然PYTHON数据分析：糖尿病视网膜病变数据分析(续五)

from keras import layers
from keras.models import Model
import keras.backend as K

K.clear_session()
densenet = DenseNet121(weights=None,include_top=False,input_shape=(None,None,3))

GAP_layer = layers.GlobalAveragePooling2D()
drop_layer = layers.Dropout(0.5)
dense_layer = layers.Dense(5, activation='sigmoid', name='final_output')

def build_model_sequential():
    model = Sequential()
    model.add(densenet)
    model.add(GAP_layer)
    model.add(drop_layer)
    model.add(dense_layer)
    return model

modelA = build_model_sequential()
modelA.load_weights('F:\\kaggleDataSet\\diabeticRetinopathy\\dense_xhlulu_731.h5')
modelA.summary()

def build_model_functional():
    base_model = densenet
    x = GAP_layer(base_model.layers[-1].output)
    x = drop_layer(x)
    final_output = dense_layer(x)
    model = Model(base_model.layers[0].input, final_output)
    return model

model = build_model_functional() # with pretrained weights, and layers we want
model.summary()

y_test = model.predict(x_test) > 0.5
y_test = y_test.astype(int).sum(axis=1) - 1

import seaborn as sns
import cv2

SIZE=224
def create_pred_hist(pred_level_y,title='NoTitle'):
    results = pd.DataFrame({'diagnosis':pred_level_y})
    f, ax = plt.subplots(figsize=(7, 4))
    ax = sns.countplot(x="diagnosis", data=results, palette="GnBu_d")
    sns.despine()
    plt.title(title)
    plt.show()

create_pred_hist(y_test,title='predicted level distribution in test set')

def gen_heatmap_img(img, model0, layer_name='last_conv_layer',viz_img=None,orig_img=None):
    preds_raw = model0.predict(img[np.newaxis])
    preds = preds_raw > 0.5 # use the same threshold as @xhlulu original kernel
    class_idx = (preds.astype(int).sum(axis=1) - 1)[0]
    class_output_tensor = model0.output[:, class_idx]
    
    viz_layer = model0.get_layer(layer_name)
    grads = K.gradients(class_output_tensor ,viz_layer.output)[0] # gradients of viz_layer wrt output_tensor of predicted class
    pooled_grads=K.mean(grads,axis=(0,1,2))
    iterate=K.function([model0.input],[pooled_grads, viz_layer.output[0]])
    pooled_grad_value, viz_layer_out_value = iterate([img[np.newaxis]])
    for i in range(pooled_grad_value.shape[0]):
        viz_layer_out_value[:,:,i] *= pooled_grad_value[i]
    heatmap = np.mean(viz_layer_out_value, axis=-1)
    heatmap = np.maximum(heatmap,0)
    heatmap /= np.max(heatmap)
    viz_img=cv2.resize(viz_img,(img.shape[1],img.shape[0]))
    heatmap=cv2.resize(heatmap,(viz_img.shape[1],viz_img.shape[0]))
    heatmap_color = cv2.applyColorMap(np.uint8(heatmap*255), cv2.COLORMAP_SPRING)/255
    heated_img = heatmap_color*0.5 + viz_img*0.5
    print('raw output from model : ')
    print_pred(preds_raw)
    if orig_img is None:
        show_Nimages([img,viz_img,heatmap_color,heated_img])
    else:
        show_Nimages([orig_img,img,viz_img,heatmap_color,heated_img])
    plt.show()
    return heated_img

def show_image(image,figsize=None,title=None):
    if figsize is not None:
        fig = plt.figure(figsize=figsize) 
    if image.ndim == 2:
        plt.imshow(image,cmap='gray')
    else:
        plt.imshow(image)
    if title is not None:
        plt.title(title)

def show_Nimages(imgs,scale=1):
    N=len(imgs)
    fig = plt.figure(figsize=(25/scale, 16/scale))
    for i, img in enumerate(imgs):
        ax = fig.add_subplot(1, N, i + 1, xticks=[], yticks=[])
        show_image(img)
        
def print_pred(array_of_classes):
    xx = array_of_classes
    s1,s2 = xx.shape
    for i in range(s1):
        for j in range(s2):
            print('%.3f ' % xx[i,j],end='')
        print('')

NUM_SAMP=10
SEED=77
layer_name = 'relu' #'conv5_block16_concat'
for i, (idx, row) in enumerate(test_df[:NUM_SAMP].iterrows()):
    path=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(image_id)+".jpg"
    ben_img = load_image_ben_orig(path)
    input_img = np.empty((1,224, 224, 3), dtype=np.uint8)
    input_img[0,:,:,:] = preprocess_image(path)
    print('test pic no.%d' % (i+1))
    _ = gen_heatmap_img(input_img[0],model, layer_name=layer_name,viz_img=ben_img)