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added train-model.py, collect-data.py and prediction.py file #39

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added train-model.py, collect-data.py and prediction.py file #39

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81 changes: 81 additions & 0 deletions collect-data .py
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import cv2
import os

if not os.path.exists("data"): #True
os.makedirs("data")
os.makedirs("data/train")
os.makedirs("data/test")
os.makedirs("data/train/0")
os.makedirs("data/train/1")
os.makedirs("data/train/2")
os.makedirs("data/train/3")
os.makedirs("data/train/4")
os.makedirs("data/train/5")
os.makedirs("data/test/0")
os.makedirs("data/test/1")
os.makedirs("data/test/2")
os.makedirs("data/test/3")
os.makedirs("data/test/4")
os.makedirs("data/test/5")


mode = 'train'
directory = 'data/'+mode+'/' #data/train/

cap=cv2.VideoCapture(0)

while True:
_, frame = cap.read()
frame = cv2.flip(frame, 1)

cv2.putText(frame, "Vipul818-Expressando - TDOC 2021", (175, 450), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,255), 3)

count = {'zero': len(os.listdir(directory+"/0")),
'one': len(os.listdir(directory+"/1")),
'two': len(os.listdir(directory+"/2")),
'three': len(os.listdir(directory+"/3")),
'four': len(os.listdir(directory+"/4")),
'five': len(os.listdir(directory+"/5"))}

cv2.putText(frame, "MODE : "+mode, (30, 50), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,255), 1)
cv2.putText(frame, "IMAGE COUNT", (10, 100), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,255), 1)
cv2.putText(frame, "ZERO : "+str(count['zero']), (10, 120), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
cv2.putText(frame, "ONE : "+str(count['one']), (10, 140), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
cv2.putText(frame, "TWO : "+str(count['two']), (10, 160), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
cv2.putText(frame, "THREE : "+str(count['three']), (10, 180), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
cv2.putText(frame, "FOUR : "+str(count['four']), (10, 200), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
cv2.putText(frame, "FIVE : "+str(count['five']), (10, 220), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)


x1 = int(0.5*frame.shape[1])
y1 = 10
x2 = frame.shape[1]-10
y2 = int(0.5*frame.shape[1])
cv2.rectangle(frame, (x1-1, y1-1), (x2+1, y2+1), (255,0,0) ,3)
roi = frame[y1:y2, x1:x2]
roi = cv2.resize(roi, (200, 200))
cv2.putText(frame, "R.O.I", (440, 350), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,225,0), 3)
cv2.imshow("Frame", frame)

roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
_, roi = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)
cv2.imshow("ROI", roi)

interrupt = cv2.waitKey(10)
if interrupt & 0xFF == 27:
break
if interrupt & 0xFF == ord('0'):
cv2.imwrite(directory+'0/'+str(count['zero'])+'.jpg', roi)
if interrupt & 0xFF == ord('1'):
cv2.imwrite(directory+'1/'+str(count['one'])+'.jpg', roi)
if interrupt & 0xFF == ord('2'):
cv2.imwrite(directory+'2/'+str(count['two'])+'.jpg', roi)
if interrupt & 0xFF == ord('3'):
cv2.imwrite(directory+'3/'+str(count['three'])+'.jpg', roi)
if interrupt & 0xFF == ord('4'):
cv2.imwrite(directory+'4/'+str(count['four'])+'.jpg', roi)
if interrupt & 0xFF == ord('5'):
cv2.imwrite(directory+'5/'+str(count['five'])+'.jpg', roi)

cap.release()
cv2.destroyAllWindows()
54 changes: 54 additions & 0 deletions prediction.py
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from keras.models import model_from_json
import operator
import cv2

json_file = open("model-bw.json", "r")
model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(model_json)
loaded_model.load_weights("model-bw.h5")
print("Loaded model from disk")

cap = cv2.VideoCapture(0)

categories = {0: 'ZERO', 1: 'ONE', 2: 'TWO', 3: 'THREE', 4: 'FOUR', 5: 'FIVE'}

while True:
_, frame = cap.read()
frame = cv2.flip(frame, 1)

x1 = int(0.5*frame.shape[1])
y1 = 10
x2 = frame.shape[1]-10
y2 = int(0.5*frame.shape[1])

cv2.putText(frame, "Vipul818-Expressando - TDOC 2021", (175, 450), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,0), 3)
cv2.rectangle(frame, (x1-1, y1-1), (x2+1, y2+1), (255,255,255) ,3)
roi = frame[y1:y2, x1:x2]

roi = cv2.resize(roi, (64, 64))
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
cv2.putText(frame, "R.O.I", (440, 350), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,225,0), 3)

_, test_image = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)
cv2.imshow("ROI", test_image)

result = loaded_model.predict(test_image.reshape(1, 64, 64, 1))
prediction = {'ZERO': result[0][0],
'ONE': result[0][1],
'TWO': result[0][2],
'THREE': result[0][3],
'FOUR': result[0][4],
'FIVE': result[0][5]}
prediction = sorted(prediction.items(), key=operator.itemgetter(1), reverse=True) #(0.9 = FIVE, 0.7, 0.6, 0.5, 0.4)
cv2.putText(frame, "PREDICTION:", (30, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
cv2.putText(frame, prediction[0][0], (80, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
cv2.imshow("Frame", frame)

interrupt = cv2.waitKey(10)
if interrupt & 0xFF == 27:
break


cap.release()
cv2.destroyAllWindows()
52 changes: 52 additions & 0 deletions train_model.py
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from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense

classifier = Sequential()

classifier.add(Convolution2D(32, (3, 3), input_shape=(64, 64, 1), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))

classifier.add(Convolution2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))

classifier.add(Flatten())

classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dense(units=6, activation='softmax'))

classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])


from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255) #epoch

training_set = train_datagen.flow_from_directory('data/train',
target_size=(64, 64),
batch_size=5,
color_mode='grayscale',
class_mode='categorical')

test_set = test_datagen.flow_from_directory('data/test',
target_size=(64, 64),
batch_size=5,
color_mode='grayscale',
class_mode='categorical')

classifier.fit_generator(
training_set,
epochs=10,
validation_data=test_set)

#Saving
model_json = classifier.to_json()
with open("model-bw.json", "w") as json_file:
json_file.write(model_json)
classifier.save_weights('model-bw.h5')