Open-Innovation

dhwanit/check.py

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+import cv2
+
+(width, height) = (130, 100)
+
+# url = '<YOUR IP ADDRESS>/video'
+# cap=cv2.VideoCapture(url)
+
+
+cap=cv2.VideoCapture(0)
+
+while (cap.isOpened()):
+    ret, img = cap.read()
+    img=cv2.flip(img, 1)
+    cv2.rectangle(img, (20, 20), (250, 250), (255, 0, 0), 3)
+    #cv2.imshow("RGB Output", img)
+    img1 = img[20:250,20:250]       
+    imCopy1 = img1.copy()
+    imCopy2 = img1.copy()
+    imCopy3 = img1.copy()
+    imCopy4 = img1.copy()
+    imCopy5 = img1.copy()
+    gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    ret, thresh1 = cv2.threshold(blur, 10, 255, cv2.THRESH_TRUNC + cv2.THRESH_OTSU)
+    hand_resize = cv2.resize(thresh1, (width, height))
+    #cv2.imshow("Threshold1", thresh1)
+    ret, thresh2 = cv2.threshold(blur, 10, 255, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
+    hand_resize = cv2.resize(thresh2, (width, height))
+    #cv2.imshow("Threshold2", thresh2)
+    ret, thresh3 = cv2.threshold(blur, 10, 255, cv2.THRESH_TRIANGLE)
+    hand_resize = cv2.resize(thresh3, (width, height))
+    #cv2.imshow("Threshold3", thresh3)
+    contours1, hierarchy = cv2.findContours(thresh2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    cv2.drawContours(imCopy1, contours1, -1, (0, 255, 0))
+    cv2.imshow('Contours1', imCopy1)
+    contours2, hierarchy = cv2.findContours(thresh2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cv2.drawContours(imCopy2, contours2, -1, (0, 255, 0))
+    cv2.imshow('Contours2', imCopy2)
+    contours3, hierarchy = cv2.findContours(thresh2, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    cv2.drawContours(imCopy3, contours3, -1, (0, 255, 0))
+    cv2.imshow('Contours3', imCopy3)
+    contours4, hierarchy = cv2.findContours(thresh2, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+    cv2.drawContours(imCopy4, contours4, -1, (0, 255, 0))
+    cv2.imshow('Contours4', imCopy4)
+    k = cv2.waitKey(10) & 0xFF 
+    if k == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

dhwanit/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, "dhwanit75 - 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(100) 
+    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()

dhwanit/model-bw.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 64, 64, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 64, 64, 1], "dtype": "float32", "filters": 64, "kernel_size": [5, 5], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": [5, 5], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 6, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

dhwanit/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()

dhwanit/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(64, (5, 5), input_shape=(64, 64, 1), activation='relu'))
+classifier.add(Convolution2D(64, (3, 3), activation='relu'))
+classifier.add(MaxPooling2D(pool_size=(2, 2)))
+
+classifier.add(Convolution2D(32, (5, 5), activation='relu'))
+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')
+