testing.py

# coding: utf-8

# In[1]:

import numpy as np
import pandas as pd
from keras.models import Sequential, Model
from keras.layers import Dense, Conv2D, Input, MaxPool2D, UpSampling2D, Concatenate, Conv2DTranspose
import tensorflow as tf
from keras.optimizers import Adam
from scipy.misc import imresize
import os
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from PIL import Image
import time
from keras.preprocessing.image import array_to_img , img_to_array , load_img ,ImageDataGenerator 
import cv2
from subprocess import check_output
#print (check_output(["ls", "../Carvana Mask Challenge/"]).decode("utf8"))


# In[2]:

data_dir = "dataset/train/"
mask_dir = "dataset/train_masks/"
all_images = os.listdir(data_dir)
train_images, validation_images = train_test_split(all_images, train_size=0.8, test_size=0.2)
train_images[0]
#content_image=Image.open('train/f00905abd3d7_02.jpg')
#content_image.size


# In[3]:

def grey2rgb(img):
    new_img = []
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            new_img.append(list(img[i][j])*3)
    new_img = np.array(new_img).reshape(img.shape[0], img.shape[1], 3)
    return new_img


# generator that we will use to read the data from the directory
def data_gen_small(data_dir, mask_dir, images, batch_size, dims):
        """
        data_dir: where the actual images are kept
        mask_dir: where the actual masks are kept
        images: the filenames of the images we want to generate batches from
        batch_size: self explanatory
        dims: the dimensions in which we want to rescale our images
        """
        while True:
            batch = np.random.choice(np.arange(len(images)), batch_size)
            imgs = []
            labels = []
            for i in batch:
                # images
                original_img = load_img(data_dir + images[i])
                resized_img = imresize(original_img, dims+[3])
                array_img = img_to_array(resized_img)/255
                imgs.append(array_img)
                
                # masks
                original_mask = load_img(mask_dir + images[i].split(".")[0] + '_mask.gif')
                resized_mask = imresize(original_mask, dims+[3])
                array_mask = img_to_array(resized_mask)/255
                labels.append(array_mask[:, :, 0])
            imgs = np.array(imgs)
            labels = np.array(labels)
            #print labels
            yield imgs, labels.reshape(-1, dims[0], dims[1], 1)

# example use
train_gen = data_gen_small(data_dir, mask_dir, train_images, 1, [512, 512])
img, msk = next(train_gen)

#plt.imshow(img[0])
#plt.imshow(grey2rgb(msk[0]), alpha=0.5)
#plt.show()


# In[4]:

input_size = 512
batch_size = 1
orig_width = 1918
orig_height = 1280
threshold = 0.5
del_threshold = 0.35


# In[ ]:




# In[5]:

df_test = pd.read_csv('dataset/sample_submission.csv')
ids_test = df_test['img'].map(lambda s: s.split('.')[0])

names = []
for id in ids_test:
    names.append('{}.jpg'.format(id))


# In[6]:

# https://www.kaggle.com/stainsby/fast-tested-rle
def run_length_encode(mask):
    '''
    img: numpy array, 1 - mask, 0 - background
    Returns run length as string formated
    '''
    inds = mask.flatten()
    runs = np.where(inds[1:] != inds[:-1])[0] + 2
    runs[1::2] = runs[1::2] - runs[:-1:2]
    rle = ' '.join([str(r) for r in runs])
    return rle


# In[7]:

# rles = []
def down(input_layer, filters, pool=True):
    conv1 = Conv2D(filters, (3, 3), padding='same', activation='relu')(input_layer)
    residual = Conv2D(filters, (3, 3), padding='same', activation='relu')(conv1)
    if pool:
        max_pool =Conv2D(filters, (3, 3), strides=(2,2),padding='same', activation='relu')(residual)
        return max_pool, residual
    else:
        return residual

def up(input_layer, residual, filters):
    filters=int(filters)
    upsample =Conv2DTranspose(filters,(4,4),padding='same',activation='relu',strides=2)(input_layer)
    upconv = Conv2D(filters, kernel_size=(2, 2), padding="same")(upsample)
    concat = Concatenate(axis=3)([residual, upconv])
    conv1 = Conv2D(filters, (3, 3), padding='same', activation='relu')(concat)
    conv2 = Conv2D(filters, (3, 3), padding='same', activation='relu')(conv1)
    return conv2


# In[8]:

# Make a custom U-nets implementation.
filters = 64
input_layer = Input(shape = [input_size, input_size, 3])
layers = [input_layer]
residuals = []

# Down 1, 128
d1, res1 = down(input_layer, filters)
residuals.append(res1)

filters *= 2

# Down 2, 64
d2, res2 = down(d1, filters)
residuals.append(res2)

filters *= 2

# Down 3, 32
d3, res3 = down(d2, filters)
residuals.append(res3)

filters *= 2

# Down 4, 16
d4, res4 = down(d3, filters)
residuals.append(res4)

filters *= 2

# Down 5, 8
d5 = down(d4, filters, pool=False)

# Up 1, 16
up1 = up(d5, residual=residuals[-1], filters=filters/2)

filters /= 2

# Up 2,  32
up2 = up(up1, residual=residuals[-2], filters=filters/2)

filters /= 2

# Up 3, 64
up3 = up(up2, residual=residuals[-3], filters=filters/2)

filters /= 2

# Up 4, 128
up4 = up(up3, residual=residuals[-4], filters=filters/2)

out = Conv2D(filters=1, kernel_size=(1, 1), activation="sigmoid")(up4)

model = Model(input_layer, out)

# model.summary()


# In[9]:

def dice_coef(y_true, y_pred):
    smooth = 1e-5
    
    y_true = tf.round(tf.reshape(y_true, [-1]))
    y_pred = tf.round(tf.reshape(y_pred, [-1]))
    
    isct = tf.reduce_sum(y_true * y_pred)
    
    return 2 * isct / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred))


# In[10]:

model.compile(optimizer=Adam(1e-4), loss='binary_crossentropy', metrics=[dice_coef])
model.load_weights(filepath='u_net_deconv.h5')


# In[ ]:

rles = []
masks = []
ind = 0
print('Predicting on {} samples with batch_size = {}...'.format(len(ids_test), batch_size))
for start in tqdm(range(0, len(ids_test), batch_size)):
# for start in tqdm(range(0, 20, batch_size)):
    x_batch = []
    end = min(start + batch_size, len(ids_test))
    ids_test_batch = ids_test[start:end]
    for id in ids_test_batch.values:
        img = cv2.imread('dataset/test/{}.jpg'.format(id))
        print img.shape
        img = cv2.resize(img, (input_size, input_size))
        x_batch.append(img)
    x_batch = np.array(x_batch, np.float32) / 255
    preds = model.predict_on_batch(x_batch)
    preds = np.squeeze(preds, axis=3)
    for pred in preds:
        prob = cv2.resize(pred, (orig_width, orig_height))
        
        mask = prob > threshold
        masks.append(mask)
        
        prob[prob >= (threshold+del_threshold)] = 1
        prob[prob <= (threshold-del_threshold)] = 0
        aa = prob < (threshold+del_threshold)
        bb = prob > (threshold-del_threshold)
        prob[aa&bb] = 0.5
        
        prob *= 255
        cv2.imwrite('dataset/trimap/trimap'+str(ind)+'.jpg', prob)
        
        ind += 1
        # mask = prob > threshold
        # rle = run_length_encode(mask)
        # rles.append(rle)


'''
rles = []
for i in range(0,len(ids_test)):
    img = cv2.imread('dataset/output/output'+str(i)+'.jpg')
    
    # img = img | mask
    rle = run_length_encode(img)
    rles.append(rle)
'''


# In[ ]:

'''
print("Generating submission file...")
df = pd.DataFrame({'img': names, 'rle_mask': rles})
df.to_csv('submission.csv.gz', index=False, compression='gzip')
'''