pythondeep-learningtflearn

How do I make this CNN detect RGB images?

I have a CNN model I made following a tutorial (https://www.youtube.com/watch?v=NMd7WjZiCzc). The model is made to detect images of cats and dogs.

The model only detects takes in black and white images. I was wondering how I could change the code so it can take in RGB images.

import cv2
import numpy as np
from random import shuffle
from tqdm import tqdm
import os

TRAIN_DIR=r'C:\Users\snehal\Desktop\Ansh stuff\Object detection\train'
TEST_DIR=r'C:\Users\snehal\Desktop\Ansh stuff\Object detection\test'
IMG_SIZE=50
LR=1e-3
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR,'2conv-basic')

def Label_img(img):
    label = img.split('.')[-3]
    if label == 'cat':
        return [1,0]
    elif label == 'dog':
        return [0,1]

def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = Label_img(img)
        path = os.path.join(TRAIN_DIR,img)
        img = cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE),(IMG_SIZE,IMG_SIZE))
        training_data.append([np.array(img),np.array(label)])
    shuffle(training_data)
    np.save('train_data.npy',training_data)
    return training_data

def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR,img)
        img_num = img.split('.')[0]
        img= cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE),(IMG_SIZE,IMG_SIZE))
        testing_data.append([np.array(img),img_num])
    np.save('testing_data.npy',testing_data)
    return testing_data

#train_data = create_train_data()
#if U already have train data then:
train_data = np.load('train_data.npy',allow_pickle=True)
print('data has been loaded')

import tflearn
from tflearn.layers.conv import conv_2d,max_pool_2d
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.estimator import regression

import tensorflow as tf
tf.reset_default_graph()

convnet = input_data(shape=[None,IMG_SIZE,IMG_SIZE,1],name='input')

convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)


convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = fully_connected(convnet,1024,activation='relu')
convnet = dropout(convnet,0.8)


convnet = fully_connected(convnet,2,activation='softmax')
convnet = regression(convnet,
                     optimizer='adam',
                     learning_rate= LR,
                     loss='categorical_crossentropy',
                     name='targets')

model = tflearn.DNN(convnet)

if os.path.exists('{}.meta'.format(MODEL_NAME)):
    model.load(MODEL_NAME)
    print('model has been loaded')

#train = train_data[:-500]
#test = train_data[-500:]
#
#X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
#Y = np.array([i[1] for i in train])
#
#test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
#test_y = np.array([i[1] for i in test])
#
#model.fit({'input':X},{'targets':Y},
#          n_epoch=20,validation_set=({'input':test_x},{'targets':test_y}),
#          snapshot_step=500,show_metric=True,run_id=MODEL_NAME)
##
#model.save(MODEL_NAME)


import matplotlib.pyplot as plt

#test_data = process_test_data()
#if u already have the data
test_data = np.load('testing_data.npy',allow_pickle=True)

fig = plt.figure()

for num,data in enumerate(test_data[:12]):
    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(3,4,num+1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
    model_out = model.predict([data])[0]

    if np.argmax(model_out) ==1: str_label = 'Dog'
    else: str_label = 'Cat'

    y.imshow(orig,cmap='gray')
    y.set_title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
plt.show()

I want it to train on RGB images and be able to test it on RGB images.

Solution

  • Gray-scale images have 1 channel and RGB images have 3 channel. So, in order to deal with RGB images, you need to perform the following changes in your code:

    1. Take color image as input instead of grayscale
    2. Change input_data shape from 1 channel to 3 channel
    3. Change training and testing data shape from 1 channel to 3 channel
    4. Change img_data shape from 1 channel to 3 channel

    Updated Code:

    import cv2
import numpy as np
from random import shuffle
from tqdm import tqdm
import os

TRAIN_DIR=r'C:\Users\snehal\Desktop\Ansh stuff\Object detection\train'
TEST_DIR=r'C:\Users\snehal\Desktop\Ansh stuff\Object detection\test'
IMG_SIZE=50
LR=1e-3
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR,'2conv-basic')

def Label_img(img):
    label = img.split('.')[-3]
    if label == 'cat':
        return [1,0]
    elif label == 'dog':
        return [0,1]

def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = Label_img(img)
        path = os.path.join(TRAIN_DIR,img)

        # 1) Taking color image as input and resizing it
        img = cv2.resize(cv2.imread(path),(IMG_SIZE,IMG_SIZE), interpolation = cv2.INTER_AREA)

        training_data.append([np.array(img),np.array(label)])
    shuffle(training_data)
    np.save('train_data.npy',training_data)
    return training_data

def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR,img)
        img_num = img.split('.')[0]

        # 1) Taking color image as input and resizing it
        img = cv2.resize(cv2.imread(path),(IMG_SIZE,IMG_SIZE), interpolation = cv2.INTER_AREA)

        testing_data.append([np.array(img),img_num])
    np.save('testing_data.npy',testing_data)
    return testing_data

#Since create_train_data() is modified this function needs to be called
train_data = create_train_data()
train_data = np.load('train_data.npy',allow_pickle=True)
print('data has been loaded')

import tflearn
from tflearn.layers.conv import conv_2d,max_pool_2d
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.estimator import regression

import tensorflow as tf

tf.reset_default_graph()

# 2) Changing input shape from 1 channel to 3 channel
convnet = input_data(shape=[None,IMG_SIZE,IMG_SIZE,3],name='input')

convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)


convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,32,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = conv_2d(convnet,64,2,activation='relu')
convnet = max_pool_2d(convnet,2)

convnet = fully_connected(convnet,1024,activation='relu')
convnet = dropout(convnet,0.8)


convnet = fully_connected(convnet,2,activation='softmax')
convnet = regression(convnet,
                     optimizer='adam',
                     learning_rate= LR,
                     loss='categorical_crossentropy',
                     name='targets')

model = tflearn.DNN(convnet)

# if os.path.exists('{}.meta'.format(MODEL_NAME)):
#     model.load(MODEL_NAME)
#     print('model has been loaded')

train = train_data[:-500]
test = train_data[-500:]

# 3) Changing training data shape from 1 channel to 3 channel
X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
Y = np.array([i[1] for i in train])

# 3) Changing testing data shape from 1 channel to 3 channel
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
test_y = np.array([i[1] for i in test])

model.fit({'input':X},{'targets':Y},
         n_epoch=20,validation_set=({'input':test_x},{'targets':test_y}),
         snapshot_step=500,show_metric=True,run_id=MODEL_NAME)

model.save(MODEL_NAME)


import matplotlib.pyplot as plt

#Since process_test_data() is modified this function needs to be called
test_data = process_test_data()
test_data = np.load('testing_data.npy',allow_pickle=True)

fig = plt.figure()

for num,data in enumerate(test_data[:12]):
    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(3,4,num+1)
    orig = img_data

    # 4) Chaging img_data shape from 1 channel to 3 channel
    data = img_data.reshape(IMG_SIZE,IMG_SIZE,3)
    model_out = model.predict([data])[0]

    if np.argmax(model_out) ==1: str_label = 'Dog'
    else: str_label = 'Cat'

    y.imshow(orig,cmap='gray')
    y.set_title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
plt.show()