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hand-calculator.py
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import cv2
import numpy as np
import pickle
import math
import sqlite3
from collections import Counter, deque
from keras.models import load_model
model = load_model('cnn_model_keras2.h5')
def get_image_size():
img = cv2.imread('gestures/0/100.jpg', 0)
return img.shape
image_x, image_y = get_image_size()
def get_hand_hist():
with open("hist", "rb") as f:
hist = pickle.load(f)
return hist
def keras_process_image(img):
img = cv2.resize(img, (image_x, image_y))
img = np.array(img, dtype=np.float32)
img = np.reshape(img, (1, image_x, image_y, 1))
return img
def keras_predict(model, image):
processed = keras_process_image(image)
pred_probab = model.predict(processed)[0]
pred_class = list(pred_probab).index(max(pred_probab))
return max(pred_probab), pred_class
def get_pred_text_from_db(pred_class):
conn = sqlite3.connect("gesture_db.db")
cmd = "SELECT g_name FROM gesture WHERE g_id="+str(pred_class)
cursor = conn.execute(cmd)
for row in cursor:
return row[0]
def get_operator(pred_text):
try:
pred_text = int(pred_text)
except:
return ""
operator = ""
if pred_text == 1:
operator = "+"
elif pred_text == 2:
operator = "-"
elif pred_text == 3:
operator = "*"
elif pred_text == 4:
operator = "/"
elif pred_text == 5:
operator = "%"
elif pred_text == 6:
operator = "**"
elif pred_text == 7:
operator = ">>"
elif pred_text == 8:
operator = "<<"
elif pred_text == 9:
operator = "&"
elif pred_text == 0:
operator = "|"
return operator
def start_calculator():
x, y, w, h = 300, 100, 300, 300
hist = get_hand_hist()
flag = {"first": False, "operator": False, "second": False, "clear": False}
count_same_frames = 0
first, operator, second = "", "", ""
pred_text = ""
calc_text = ""
info = "Enter first number"
count_clear_frames = 0
cam = cv2.VideoCapture(1)
while True:
_, img = cam.read()
img = cv2.flip(img, 1)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([imgHSV], [0, 1], hist, [0, 180, 0, 256], 1)
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
cv2.filter2D(dst,-1,disc,dst)
blur = cv2.GaussianBlur(dst, (11,11), 0)
blur = cv2.medianBlur(blur, 15)
thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
thresh = cv2.merge((thresh,thresh,thresh))
thresh = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
thresh = thresh[y:y+h, x:x+w]
#print(thresh)
contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
old_pred_text = pred_text
if len(contours) > 0:
contour = max(contours, key = cv2.contourArea)
if cv2.contourArea(contour) > 10000:
x1, y1, w1, h1 = cv2.boundingRect(contour)
save_img = thresh[y1:y1+h1, x1:x1+w1]
if w1 > h1:
save_img = cv2.copyMakeBorder(save_img, int((w1-h1)/2) , int((w1-h1)/2) , 0, 0, cv2.BORDER_CONSTANT, (0, 0, 0))
elif h1 > w1:
save_img = cv2.copyMakeBorder(save_img, 0, 0, int((h1-w1)/2) , int((h1-w1)/2) , cv2.BORDER_CONSTANT, (0, 0, 0))
pred_probab, pred_class = keras_predict(model, save_img)
if pred_probab*100 > 70:
pred_text = get_pred_text_from_db(pred_class)
if old_pred_text == pred_text:
count_same_frames += 1
else:
count_same_frames = 0
if pred_text == "C":
if count_same_frames > 5:
count_same_frames = 0
first, second, operator, pred_text, calc_text = '', '', '', '', ''
flag['first'], flag['operator'], flag['second'], flag['clear'] = False, False, False, False
info = "Enter first number"
elif pred_text == "Confirm" and count_same_frames > 15:
count_same_frames = 0
if flag['clear']:
first, second, operator, pred_text, calc_text = '', '', '', '', ''
flag['first'], flag['operator'], flag['second'], flag['clear'] = False, False, False, False
info = "Enter first number"
elif second != '':
flag['second'] = True
info = "Clear screen"
second = ''
flag['clear'] = True
calc_text += "= "+str(eval(calc_text))
elif first != '':
flag['first'] = True
info = "Enter operator"
first = ''
elif pred_text != "Confirm":
if flag['first'] == False:
if count_same_frames > 15:
count_same_frames = 0
first += pred_text
calc_text += pred_text
elif flag['operator'] == False:
operator = get_operator(pred_text)
if count_same_frames > 15:
count_same_frames = 0
flag['operator'] = True
calc_text += operator
info = "Enter second number"
operator = ''
elif flag['second'] == False:
if count_same_frames > 15:
second += pred_text
calc_text += pred_text
count_same_frames = 0
if count_clear_frames == 30:
first, second, operator, pred_text, calc_text = '', '', '', '', ''
flag['first'], flag['operator'], flag['second'], flag['clear'] = False, False, False, False
info = "Enter first number"
count_clear_frames = 0
blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
cv2.putText(blackboard, "Predicted text - " + pred_text, (30, 40), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 0))
cv2.putText(blackboard, "Operator " + operator, (30, 80), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 127))
cv2.putText(blackboard, calc_text, (30, 240), cv2.FONT_HERSHEY_TRIPLEX, 2, (255, 255, 255))
cv2.putText(blackboard, info, (30, 440), cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 255, 255) )
cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
res = np.hstack((img, blackboard))
cv2.imshow("Calculator", res)
cv2.imshow("thresh", thresh)
if cv2.waitKey(1) == ord('q'):
break
keras_predict(model, np.zeros((50, 50), dtype = np.uint8))
start_calculator()