Open innovation project

2ndprogram.py

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+import cv2
+img=cv2.imread("pic2.jpg")
+img1=cv2.resize(img,(300,300))
+gray_image=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
+
+ret,thresh1=cv2.threshold(gray_image,120,255,cv2.THRESH_BINARY)
+ret,thresh2=cv2.threshold(gray_image,120,255,cv2.THRESH_BINARY_INV)
+ret,thresh3=cv2.threshold(gray_image,120,255,cv2.THRESH_TRUNC)
+ret,thresh4=cv2.threshold(gray_image,120,255,cv2.THRESH_TOZERO)
+ret,thresh5=cv2.threshold(gray_image,120,255,cv2.THRESH_TOZERO_INV)
+thresh6=cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,199,5)
+thresh7=cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,199,5)
+ret,thresh8=cv2.threshold(gray_image,120,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+cv2.imshow("original",img1)
+cv2.imshow('Binary Threshold', thresh1)
+cv2.imshow('Binary Threshold Inverted', thresh2)
+cv2.imshow('Truncated Threshold', thresh3)
+cv2.imshow('Set to 0', thresh4)
+cv2.imshow('Set to 0 Inverted', thresh5)
+cv2.imshow('Adaptive Mean', thresh6)
+cv2.imshow('Adaptive Gaussian', thresh7)
+cv2.imshow('Otsu Threshold', thresh8) 
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()

camera2.py

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+import cv2
+
+cap=cv2.VideoCapture(0)
+while (cap.isOpened()):
+    ret,img=cap.read()
+    img=cv2.flip(img,1)
+    cv2.rectangle(img,(30,30),(280,280),(255,255,1),3)
+    cv2.imshow("frame",img)
+    img1=img[30:280,30:280]
+    imcopy=img1.copy()
+    cv2.imshow("frame2",img1)
+    white=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
+    blur=cv2.GaussianBlur(white,(5,5),0)
+    ret, thresh1 = cv2.threshold(blur, 10, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    hand_resize = cv2.resize(thresh1, (130, 100))
+    cv2.imshow("Threshold", thresh1)
+    contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    cv2.drawContours(imcopy, contours, -1, (0, 255, 0))
+    cv2.imshow('Draw Contours',imcopy)
+
+    k = 0xFF & cv2.waitKey(10)
+    if k == 27:
+        break
+cap.release()
+cv2.destroyAllWindows()

collect-data.py

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+import cv2
+import os
+
+if not os.path.exists("data"):
+    os.makedirs("data")
+    os.makedirs("data/train")
+    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")
+
+
+mode = 'train'
+directory = 'data/'+mode+'/'
+
+# url = '<YOUR IP ADDRESS>/video'
+# cap=cv2.VideoCapture(url)
+
+cap=cv2.VideoCapture(0)
+
+while True:
+    _, frame = cap.read()
+    frame = cv2.flip(frame, 1)
+
+    cv2.putText(frame, "sayantani-12", (150, 300), 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, (10, 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), (2,50,255) ,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()

collectdata1.py

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+import cv2
+import os
+
+if not os.path.exists("data1"):
+    os.makedirs("data1")
+    os.makedirs("data1/train")
+    os.makedirs("data1/train/hello")
+    os.makedirs("data1/train/peace")
+    os.makedirs("data1/train/best of luck")
+    os.makedirs("data1/train/call")
+    os.makedirs("data1/train/yo man!")
+
+
+
+mode = 'train'
+directory = 'data1/'+mode+'/'
+
+cap=cv2.VideoCapture(0)
+
+while True:
+    _, frame = cap.read()
+    frame = cv2.flip(frame, 1)
+
+    cv2.putText(frame, "sayantani-12", (150, 300), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,255), 3)
+
+    count = {'hello': len(os.listdir(directory+"hello")),
+             'peace': len(os.listdir(directory+"peace")),
+             'best of luck': len(os.listdir(directory+"best of luck")),
+             'call': len(os.listdir(directory+"call")),
+             'yo': len(os.listdir(directory+"yo man!"))}
+
+    cv2.putText(frame, "MODE : "+mode, (10, 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, "hell0 : "+str(count['hello']), (10, 120), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
+    cv2.putText(frame, "peace : "+str(count['peace']), (10, 140), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
+    cv2.putText(frame, "best of luck : "+str(count['best of luck']), (10, 160), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
+    cv2.putText(frame, "call : "+str(count['call']), (10, 180), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 1)
+    cv2.putText(frame, "yo! : "+str(count['yo']), (10, 200), 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), (255,50,255) ,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, (100,225,50), 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+'hello/'+str(count['hello'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('1'):
+        cv2.imwrite(directory+'peace/'+str(count['peace'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('2'):
+        cv2.imwrite(directory+'best of luck/'+str(count['best of luck'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('3'):
+        cv2.imwrite(directory+'call/'+str(count['call'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('4'):
+        cv2.imwrite(directory+'yo man!/'+str(count['yo'])+'.jpg', roi)
+
+
+cap.release()
+cv2.destroyAllWindows()

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, "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()

prediction1.py

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+from keras.models import model_from_json
+import operator
+import cv2
+
+json_file = open("model-bw1.json", "r")
+model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(model_json)
+loaded_model.load_weights("model-bw1.h5")
+print("Loaded model from disk")
+
+cap = cv2.VideoCapture(0) 
+
+categories = {0: "best of luck", 1: "call", 2: "hello", 3: "peace" , 4: "yo man!"}
+
+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, "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 = {'best of luck': result[0][0], 
+                  'call': result[0][1], 
+                  'hello': result[0][2],
+                  'peace': result[0][3],
+                  'yo man!': result[0][4]} 
+    prediction = sorted(prediction.items(), key=operator.itemgetter(1), reverse=True) 
+    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()

train.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=5, 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) 
+
+training_set = train_datagen.flow_from_directory('data1/train',
+                                                 target_size=(64, 64),
+                                                 batch_size=5,
+                                                 color_mode='grayscale',
+                                                 class_mode='categorical')
+
+test_set = test_datagen.flow_from_directory('data1/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-bw1.json", "w") as json_file:
+    json_file.write(model_json)
+classifier.save_weights('model-bw1.h5')
+