Clever Geek Handbook

Bagaimana saya mengajari komputer saya bermain berpasangan menggunakan OpenCV dan Deep Learning

Sedikit kesenangan dengan computer vision dan CNN dengan database kecil.

, CNN, , . , , . , Spot it! ( , ).





, , : , . Spot it!, . . , . 55 , , .









Cobalah sendiri: apa simbol umum pada kartu yang ditunjukkan di atas?
: , ?

?

data science . , . 330 . . : (CNN)? !









, , ? , : . , . . , : , ( ) ( ) , ( ) ( ) . .





Lab . L , a , b — . OpenCV:





import cv2
import imutils
imgname = 'picture1'
image = cv2.imread(f’{imgname}.jpg’)
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)





Dari kiri ke kanan: gambar asli, komponen lampu, komponen a dan komponen b
: , , a b

, :





clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
cl = clahe.apply(l)
limg = cv2.merge((cl,a,b))
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)





Kiri ke kanan: gambar asli, komponen ringan, dengan kontras yang ditingkatkan, diubah kembali ke RGB
: , , , RGB

:





resized = cv2.resize(final, (800, 800))
#  
cv2.imwrite(f'{imgname}processed.jpg', blurred)

!









. OpenCV. -, ( , 190), . :





image = cv2.imread(f’{imgname}processed.jpg’)
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
thresh = cv2.threshold(gray, 190, 255, cv2.THRESH_BINARY)[1]
#  
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
output = image.copy()
#    
for c in cnts:
    cv2.drawContours(output, [c], -1, (255, 0, 0), 3)





Gambar yang diproses diubah menjadi hitam dan putih, dipisahkan dengan ambang batas dan dengan garis tepi
, -,

, , : . , .





#   ,  
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
#     
mask = np.zeros(gray.shape,np.uint8)
mask = cv2.drawContours(mask, [cnts], -1, 255, cv2.FILLED)
#    
fg_masked = cv2.bitwise_and(image, image, mask=mask)
#   (  )
mask = cv2.bitwise_not(mask)
bk = np.full(image.shape, 255, dtype=np.uint8)
bk_masked = cv2.bitwise_and(bk, bk, mask=mask)
#     
final = cv2.bitwise_or(fg_masked, bk_masked)





Topeng, latar belakang, latar depan, digabungkan
, , ,

! , — . , . :





#       (  )
gray = cv2.cvtColor(final, cv2.COLOR_RGB2GRAY)
thresh = cv2.threshold(gray, 195, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.bitwise_not(thresh)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:10]
#   
i = 0
for c in cnts:
    if cv2.contourArea(c) > 1000:
        #  ,  
        mask = np.zeros(gray.shape, np.uint8)
        mask = cv2.drawContours(mask, [c], -1, 255, cv2.FILLED)
        #  
        fg_masked = cv2.bitwise_and(image, image, mask=mask)
        mask = cv2.bitwise_not(mask)
        bk = np.full(image.shape, 255, dtype=np.uint8)
        bk_masked = cv2.bitwise_and(bk, bk, mask=mask)
        finalcont = cv2.bitwise_or(fg_masked, bk_masked)
        #    
        output = finalcont.copy()
        x,y,w,h = cv2.boundingRect(c)
        # squares io rectangles
        if w < h:
            x += int((w-h)/2)
            w = h
        else:
            y += int((h-w)/2)
            h = w
        #    
        roi = finalcont[y:y+h, x:x+w]
        roi = cv2.resize(roi, (400,400))
        #  
        cv2.imwrite(f"{imgname}_icon{i}.jpg", roi)
        i += 1
Dibagi dengan ambang pintu, dengan kontur tertentu, gambar hantu dan hati (dipotong dengan topeng)
, , ( )

! . , , 57 ( 57 ). :





symbols
 ├── test
 │   ├── anchor
 │   ├── apple
 │   │   ...
 │   └── zebra
 ├── train
 │   ├── anchor
 │   ├── apple
 │   │   ...
 │   └── zebra
 └── validation
     ├── anchor
     ├── apple
     │   ...
     └── zebra

, ( 2500)! , GitHub. , 






(CNN)





. CNN. CNN .





. . softmax 57 .





:





# 
from keras import layers
from keras import models
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# ,    57  (    )
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(400, 400, 3)))
model.add(layers.MaxPooling2D((2, 2)))  
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5)) 
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(57, activation='softmax'))
model.compile(loss='categorical_crossentropy',       optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])

. — . , , , . Keras:





#  
train_dir = 'symbols/train'
validation_dir = 'symbols/validation'
test_dir = 'symbols/test'
#     ImageDataGenerator  Keras (  )
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.1, zoom_range=0.1, horizontal_flip=True, vertical_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(train_dir, target_size=(400,400), batch_size=20, class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(validation_dir, target_size=(400,400), batch_size=20, class_mode='categorical')

, :





Hantu asli di sebelah kiri, gambar lain menunjukkan contoh augmentasi data
,

, .





history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=100, validation_data=validation_generator, validation_steps=50)
#     
model.save('models/model.h5')

, , . :





Hasil model dasar

, . ( ) . .





Hasil model akhir

: . , 0,995 .





. , , , . :





  • - : .





  • ( ).





  • . ( ).





GitHub , main.py.





:





? , ! , . ! :





Manusia salju? Dimana?
? ?

? : , ! : 55 . 1485 . 140 . , !





Menurut saya tidak terlalu sulit untuk membuat model 100%. Ini dapat dilakukan dengan menggunakan pembelajaran transfer, misalnya. Untuk memahami apa yang dilakukan model, kita dapat merender lapisan untuk gambar uji. Apa yang harus dicoba lain kali!





Semoga Anda menikmati membaca posting ini! ❀







More articles:


All Articles