diff --git a/collect-data .py b/collect-data .py new file mode 100644 index 0000000..7750a43 --- /dev/null +++ b/collect-data .py @@ -0,0 +1,81 @@ +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() diff --git a/prediction.py b/prediction.py new file mode 100644 index 0000000..9d0a5b9 --- /dev/null +++ b/prediction.py @@ -0,0 +1,54 @@ +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() diff --git a/train_model.py b/train_model.py new file mode 100644 index 0000000..ad5707a --- /dev/null +++ b/train_model.py @@ -0,0 +1,52 @@ +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') +