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data_collection_binary.py
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data_collection_binary.py
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import cv2
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import os, os.path
from keras.models import load_model
import traceback
#model = load_model('C:\\Users\\devansh raval\\PycharmProjects\\pythonProject\\cnn9.h5')
capture = cv2.VideoCapture(0)
hd = HandDetector(maxHands=1)
hd2 = HandDetector(maxHands=1)
# #training data
# count = len(os.listdir("D:https://sign2text_dataset_2.0/Binary_imgs//A"))
#testing data
count = len(os.listdir("D:https://test_data_2.0//Gray_imgs//A"))
p_dir = "A"
c_dir = "a"
offset = 30
step = 1
flag=False
suv=0
#C:\Users\devansh raval\PycharmProjects\pythonProject
white=np.ones((400,400),np.uint8)*255
cv2.imwrite("C:\\Users\\devansh raval\\PycharmProjects\\pythonProject\\white.jpg",white)
while True:
try:
_, frame = capture.read()
frame = cv2.flip(frame, 1)
hands= hd.findHands(frame, draw=False, flipType=True)
img_final=img_final1=img_final2=0
if hands:
hand = hands[0]
x, y, w, h = hand['bbox']
image = frame[y - offset:y + h + offset, x - offset:x + w + offset]
#image1 = imgg[y - offset:y + h + offset, x - offset:x + w + offset]
roi = image #rgb image without drawing
# roi1 = image1 #rdb image with drawing
# #for simple gray image without draw
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (1, 1), 2)
#
# #for binary image
gray2 = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur2 = cv2.GaussianBlur(gray2, (5, 5), 2)
th3 = cv2.adaptiveThreshold(blur2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
ret, test_image = cv2.threshold(th3, 27, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
#
#
test_image1=blur
img_final1 = np.ones((400, 400), np.uint8) * 148
h = test_image1.shape[0]
w = test_image1.shape[1]
img_final1[((400 - h) // 2):((400 - h) // 2) + h, ((400 - w) // 2):((400 - w) // 2) + w] = test_image1
img_final = np.ones((400, 400), np.uint8) * 255
h = test_image.shape[0]
w = test_image.shape[1]
img_final[((400 - h) // 2):((400 - h) // 2) + h, ((400 - w) // 2):((400 - w) // 2) + w] = test_image
hands = hd.findHands(frame, draw=False, flipType=True)
if hands:
# #print(" --------- lmlist=",hands[1])
hand = hands[0]
x, y, w, h = hand['bbox']
image = frame[y - offset:y + h + offset, x - offset:x + w + offset]
white = cv2.imread("C:\\Users\\devansh raval\\PycharmProjects\\pythonProject\\white.jpg")
# img_final=img_final1=img_final2=0
handz = hd2.findHands(image, draw=False, flipType=True)
if handz:
hand = handz[0]
pts = hand['lmList']
# x1,y1,w1,h1=hand['bbox']
os = ((400 - w) // 2) - 15
os1 = ((400 - h) // 2) - 15
for t in range(0, 4, 1):
cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1),
(0, 255, 0), 3)
for t in range(5, 8, 1):
cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1),
(0, 255, 0), 3)
for t in range(9, 12, 1):
cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1),
(0, 255, 0), 3)
for t in range(13, 16, 1):
cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1),
(0, 255, 0), 3)
for t in range(17, 20, 1):
cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1),
(0, 255, 0), 3)
cv2.line(white, (pts[5][0] + os, pts[5][1] + os1), (pts[9][0] + os, pts[9][1] + os1), (0, 255, 0),
3)
cv2.line(white, (pts[9][0] + os, pts[9][1] + os1), (pts[13][0] + os, pts[13][1] + os1), (0, 255, 0),
3)
cv2.line(white, (pts[13][0] + os, pts[13][1] + os1), (pts[17][0] + os, pts[17][1] + os1),
(0, 255, 0), 3)
cv2.line(white, (pts[0][0] + os, pts[0][1] + os1), (pts[5][0] + os, pts[5][1] + os1), (0, 255, 0),
3)
cv2.line(white, (pts[0][0] + os, pts[0][1] + os1), (pts[17][0] + os, pts[17][1] + os1), (0, 255, 0),
3)
for i in range(21):
cv2.circle(white, (pts[i][0] + os, pts[i][1] + os1), 2, (0, 0, 255), 1)
cv2.imshow("skeleton", white)
# cv2.imshow("5", skeleton5)
hands = hd.findHands(white, draw=False, flipType=True)
if hands:
hand = hands[0]
x, y, w, h = hand['bbox']
cv2.rectangle(white, (x - offset, y - offset), (x + w, y + h), (3, 255, 25), 3)
image1 = frame[y - offset:y + h + offset, x - offset:x + w + offset]
roi1 = image1 #rdb image with drawing
#for gray image with drawings
gray1 = cv2.cvtColor(roi1, cv2.COLOR_BGR2GRAY)
blur1 = cv2.GaussianBlur(gray1, (1, 1), 2)
test_image2= blur1
img_final2= np.ones((400, 400), np.uint8) * 148
h = test_image2.shape[0]
w = test_image2.shape[1]
img_final2[((400 - h) // 2):((400 - h) // 2) + h, ((400 - w) // 2):((400 - w) // 2) + w] = test_image2
#cv2.imshow("aaa",white)
# cv2.imshow("gray",img_final2)
cv2.imshow("binary", img_final)
# cv2.imshow("gray w/o draw", img_final1)
# img = img_final.reshape(1, 400, 400, 1)
# # print(model.predict(img))
# prob = np.array(model.predict(img)[0], dtype='float32')
# ch1 = np.argmax(prob, axis=0)
# prob[ch1] = 0
# ch2 = np.argmax(prob, axis=0)
# prob[ch2] = 0
# ch3 = np.argmax(prob, axis=0)
# prob[ch3] = 0
# ch1 = chr(ch1 + 65)
# ch2 = chr(ch2 + 65)
# ch3 = chr(ch3 + 65)
# frame = cv2.putText(frame, "Predicted " + ch1 + " " + ch2 + " " + ch3, (x - offset - 150, y - offset - 10),
# cv2.FONT_HERSHEY_SIMPLEX,
# 1, (255, 0, 0), 1, cv2.LINE_AA)
#cv2.rectangle(frame, (x - offset, y - offset), (x + w, y + h), (3, 255, 25), 3)
# frame = cv2.putText(frame, "dir=" + c_dir + " count=" + str(count), (50,50),
# cv2.FONT_HERSHEY_SIMPLEX,
# 1, (255, 0, 0), 1, cv2.LINE_AA)
cv2.imshow("frame", frame)
interrupt = cv2.waitKey(1)
if interrupt & 0xFF == 27:
# esc key
break
if interrupt & 0xFF == ord('n'):
p_dir = chr(ord(p_dir) + 1)
c_dir = chr(ord(c_dir) + 1)
if ord(p_dir)==ord('Z')+1:
p_dir="A"
c_dir="a"
flag = False
# #training data
# count = len(os.listdir("D:https://sign2text_dataset_2.0/Binary_imgs//" + p_dir + "//"))
# test data
count = len(os.listdir("D:https://test_data_2.0/Gray_imgs//" + p_dir + "//"))
if interrupt & 0xFF == ord('a'):
if flag:
flag=False
else:
suv=0
flag=True
print("=====",flag)
if flag==True:
if suv==50:
flag=False
if step%2==0:
# #this is for training data collection
# cv2.imwrite("D:\\sign2text_dataset_2.0\\Binary_imgs\\" + p_dir + "\\" + c_dir + str(count) + ".jpg", img_final)
# cv2.imwrite("D:\\sign2text_dataset_2.0\\Gray_imgs\\" + p_dir + "\\" + c_dir + str(count) + ".jpg", img_final1)
# cv2.imwrite("D:\\sign2text_dataset_2.0\\Gray_imgs_with_drawing\\" + p_dir + "\\" + c_dir + str(count) + ".jpg", img_final2)
# this is for testing data collection
# cv2.imwrite("D:\\test_data_2.0\\Binary_imgs\\" + p_dir + "\\" + c_dir + str(count) + ".jpg",
# img_final)
cv2.imwrite("D:\\test_data_2.0\\Gray_imgs\\" + p_dir + "\\" + c_dir + str(count) + ".jpg",
img_final1)
cv2.imwrite(
"D:\\test_data_2.0\\Gray_imgs_with_drawing\\" + p_dir + "\\" + c_dir + str(count) + ".jpg",
img_final2)
count += 1
suv += 1
step+=1
except Exception:
print("==",traceback.format_exc() )
capture.release()
cv2.destroyAllWindows()
# img_final=cv2.resize(img_final,(224,224));
# img_finalf=np.ones((400,400,3),np.uint8)*255;
# print("img final shape= ", img_final)
# for i in range(400):
# for j in range(400):
# if(img_final[i][j]==255):
# img_finalf[i][j]=[255,255,255]
# else:
# img_finalf[i][j]=[0,0,0];
# print("img final f shape= ", img_finalf)
# image = cv2.medianBlur(test_image, 5)
# kernel = np.ones((3, 3), np.uint8)
# kernel1 = np.ones((1, 1), np.uint8)
# dilate = cv2.dilate(image, kernel, iterations=1)
# dilate = cv2.erode(dilate, kernel1, iterations=1)
# cv2.imshow("gray",gray)
# cv2.imshow("blurr",blur)
# cv2.imshow("adapt threshold",th3)
# cv2.imshow("roi",test_image)
# white increase
# if flag:
# if step % 2 == 0:
# cv2.imwrite("D:\\sign_data\\B\\b" + str(count) + ".jpg", img_final)
# print(count)
# count += 1
# step += 1