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collect-data
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import cv2
import numpy as np
import os
import string
# Create the directory structure
if not os.path.exists("data"):
os.makedirs("data")
if not os.path.exists("data/train"):
os.makedirs("data/train")
if not os.path.exists("data/test"):
os.makedirs("data/test")
for i in range(3):
if not os.path.exists("data/train/" + str(i)):
os.makedirs("data/train/"+str(i))
if not os.path.exists("data/test/" + str(i)):
os.makedirs("data/test/"+str(i))
for i in string.ascii_uppercase:
if not os.path.exists("data/train/" + i):
os.makedirs("data/train/"+i)
if not os.path.exists("data/test/" + i):
os.makedirs("data/test/"+i)
# Train or test
mode = 'train'
directory = 'data/'+mode+'/'
minValue = 70
cap = cv2.VideoCapture(0)
interrupt = -1
while True:
_, frame = cap.read()
# Simulating mirror image
frame = cv2.flip(frame, 1)
# Getting count of existing images
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")),
# 'six': len(os.listdir(directory+"/6")),
'a': len(os.listdir(directory+"/A")),
'b': len(os.listdir(directory+"/B")),
'c': len(os.listdir(directory+"/C")),
'd': len(os.listdir(directory+"/D")),
'e': len(os.listdir(directory+"/E")),
'f': len(os.listdir(directory+"/F")),
'g': len(os.listdir(directory+"/G")),
'h': len(os.listdir(directory+"/H")),
'i': len(os.listdir(directory+"/I")),
'j': len(os.listdir(directory+"/J")),
'k': len(os.listdir(directory+"/K")),
'l': len(os.listdir(directory+"/L")),
'm': len(os.listdir(directory+"/M")),
'n': len(os.listdir(directory+"/N")),
'o': len(os.listdir(directory+"/O")),
'p': len(os.listdir(directory+"/P")),
'q': len(os.listdir(directory+"/Q")),
'r': len(os.listdir(directory+"/R")),
's': len(os.listdir(directory+"/S")),
't': len(os.listdir(directory+"/T")),
'u': len(os.listdir(directory+"/U")),
'v': len(os.listdir(directory+"/V")),
'w': len(os.listdir(directory+"/W")),
'x': len(os.listdir(directory+"/X")),
'y': len(os.listdir(directory+"/Y")),
'z': len(os.listdir(directory+"/Z"))
}
# Printing the count in each set to the screen
# cv2.putText(frame, "MODE : "+mode, (10, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# cv2.putText(frame, "IMAGE COUNT", (10, ), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "ZERO : "+str(count['zero']), (10, 70), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "ONE : "+str(count['one']), (10, 80), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "TWO : "+str(count['two']), (10, 90), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# cv2.putText(frame, "THREE : "+str(count['three']), (10, 180), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# cv2.putText(frame, "FOUR : "+str(count['four']), (10, 200), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# cv2.putText(frame, "FIVE : "+str(count['five']), (10, 220), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# cv2.putText(frame, "SIX : "+str(count['six']), (10, 230), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "a : "+str(count['a']), (10, 100), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "b : "+str(count['b']), (10, 110), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "c : "+str(count['c']), (10, 120), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "d : "+str(count['d']), (10, 130), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "e : "+str(count['e']), (10, 140), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "f : "+str(count['f']), (10, 150), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "g : "+str(count['g']), (10, 160), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "h : "+str(count['h']), (10, 170), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "i : "+str(count['i']), (10, 180), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "k : "+str(count['k']), (10, 190), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "l : "+str(count['l']), (10, 200), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "m : "+str(count['m']), (10, 210), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "n : "+str(count['n']), (10, 220), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "o : "+str(count['o']), (10, 230), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "p : "+str(count['p']), (10, 240), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "q : "+str(count['q']), (10, 250), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "r : "+str(count['r']), (10, 260), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "s : "+str(count['s']), (10, 270), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "t : "+str(count['t']), (10, 280), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "u : "+str(count['u']), (10, 290), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "v : "+str(count['v']), (10, 300), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "w : "+str(count['w']), (10, 310), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "x : "+str(count['x']), (10, 320), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "y : "+str(count['y']), (10, 330), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
cv2.putText(frame, "z : "+str(count['z']), (10, 340), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,255), 1)
# Coordinates of the ROI
x1 = int(0.5*frame.shape[1])
y1 = 10
x2 = frame.shape[1]-10
y2 = int(0.5*frame.shape[1])
# Drawing the ROI
# The increment/decrement by 1 is to compensate for the bounding box
cv2.rectangle(frame, (220-1, 9), (620+1, 419), (255,0,0) ,1)
# Extracting the ROI
roi = frame[10:410, 220:520]
# roi = cv2.resize(roi, (64, 64))
cv2.imshow("Frame", frame)
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),2)
# #blur = cv2.bilateralFilter(roi,9,75,75)
th3 = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
ret, test_image = cv2.threshold(th3, minValue, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
#time.sleep(5)
#cv2.imwrite("/home/rc/Downloads/soe/im1.jpg", roi)
#test_image = func("/home/rc/Downloads/soe/im1.jpg")
test_image = cv2.resize(test_image, (300,300))
cv2.imshow("test", test_image)
#_, mask = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY)
#kernel = np.ones((1, 1), np.uint8)
#img = cv2.dilate(mask, kernel, iterations=1)
#img = cv2.erode(mask, kernel, iterations=1)
# do the processing after capturing the image!
# roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# _, roi = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)
##gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
##cv2.imshow("GrayScale", gray)
##blur = cv2.GaussianBlur(gray,(5,5),2)
#blur = cv2.bilateralFilter(roi,9,75,75)
##th3 = cv2.adaptiveThreshold(frame,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
##ret, res = cv2.threshold(th3, minValue, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# cv2.imshow("ROI", roi)
#roi = frame
interrupt = cv2.waitKey(10)
if interrupt & 0xFF == 27: # esc key
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)
# if interrupt & 0xFF == ord('6'):
# cv2.imwrite(directory+'6/'+str(count['six'])+'.jpg', roi)
if interrupt & 0xFF == ord('a'):
cv2.imwrite(directory+'A/'+str(count['a'])+'.jpg', roi)
if interrupt & 0xFF == ord('b'):
cv2.imwrite(directory+'B/'+str(count['b'])+'.jpg', roi)
if interrupt & 0xFF == ord('c'):
cv2.imwrite(directory+'C/'+str(count['c'])+'.jpg', roi)
if interrupt & 0xFF == ord('d'):
cv2.imwrite(directory+'D/'+str(count['d'])+'.jpg', roi)
if interrupt & 0xFF == ord('e'):
cv2.imwrite(directory+'E/'+str(count['e'])+'.jpg', roi)
if interrupt & 0xFF == ord('f'):
cv2.imwrite(directory+'F/'+str(count['f'])+'.jpg', roi)
if interrupt & 0xFF == ord('g'):
cv2.imwrite(directory+'G/'+str(count['g'])+'.jpg', roi)
if interrupt & 0xFF == ord('h'):
cv2.imwrite(directory+'H/'+str(count['h'])+'.jpg', roi)
if interrupt & 0xFF == ord('i'):
cv2.imwrite(directory+'I/'+str(count['i'])+'.jpg', roi)
if interrupt & 0xFF == ord('j'):
cv2.imwrite(directory+'J/'+str(count['j'])+'.jpg', roi)
if interrupt & 0xFF == ord('k'):
cv2.imwrite(directory+'K/'+str(count['k'])+'.jpg', roi)
if interrupt & 0xFF == ord('l'):
cv2.imwrite(directory+'L/'+str(count['l'])+'.jpg', roi)
if interrupt & 0xFF == ord('m'):
cv2.imwrite(directory+'M/'+str(count['m'])+'.jpg', roi)
if interrupt & 0xFF == ord('n'):
cv2.imwrite(directory+'N/'+str(count['n'])+'.jpg', roi)
if interrupt & 0xFF == ord('o'):
cv2.imwrite(directory+'O/'+str(count['o'])+'.jpg', roi)
if interrupt & 0xFF == ord('p'):
cv2.imwrite(directory+'P/'+str(count['p'])+'.jpg', roi)
if interrupt & 0xFF == ord('q'):
cv2.imwrite(directory+'Q/'+str(count['q'])+'.jpg', roi)
if interrupt & 0xFF == ord('r'):
cv2.imwrite(directory+'R/'+str(count['r'])+'.jpg', roi)
if interrupt & 0xFF == ord('s'):
cv2.imwrite(directory+'S/'+str(count['s'])+'.jpg', roi)
if interrupt & 0xFF == ord('t'):
cv2.imwrite(directory+'T/'+str(count['t'])+'.jpg', roi)
if interrupt & 0xFF == ord('u'):
cv2.imwrite(directory+'U/'+str(count['u'])+'.jpg', roi)
if interrupt & 0xFF == ord('v'):
cv2.imwrite(directory+'V/'+str(count['v'])+'.jpg', roi)
if interrupt & 0xFF == ord('w'):
cv2.imwrite(directory+'W/'+str(count['w'])+'.jpg', roi)
if interrupt & 0xFF == ord('x'):
cv2.imwrite(directory+'X/'+str(count['x'])+'.jpg', roi)
if interrupt & 0xFF == ord('y'):
cv2.imwrite(directory+'Y/'+str(count['y'])+'.jpg', roi)
if interrupt & 0xFF == ord('z'):
cv2.imwrite(directory+'Z/'+str(count['z'])+'.jpg', roi)
cap.release()
cv2.destroyAllWindows()
"""
d = "old-data/test/0"
newd = "data/test/0"
for walk in os.walk(d):
for file in walk[2]:
roi = cv2.imread(d+"/"+file)
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
_, mask = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)
cv2.imwrite(newd+"/"+file, mask)
"""