def read_from_folder(dir_):
"""
Loads data and preprocess and save the images
"""
images = []
labels = []
img_dir = '/home/aarav/Downloads/Compressed/' + dir_
dataset_dir = '/home/aarav/Desktop/MajorProject/Dataset/' + dir_
print("LOADING DATA FROM Digits: ", end="")
for folder in os.listdir(img_dir):
print(folder, end=' | ')
#making sub folder fo alphabets
if not os.path.exists(dataset_dir + folder):
os.makedirs(dataset_dir + folder)
for image in os.listdir(img_dir + "/" + folder):
print(image)
if image.endswith('txt'):
continue
temp_img = cv2.imread(img_dir + '/' + folder + '/' + image)
mask1 = segment(temp_img)
handFound, hand, contours_of_hand = utils.get_my_hand(mask1)
print(hand.shape)
if (handFound):
cv2.imwrite(dataset_dir + folder + '/' + image, hand)
break
def add_frame(self, image):
mask = utils.segment(image)
face, foundFace = utils.detect_face(image)
mask = utils.eliminate_face(face, foundFace, mask)
if self.__using_stabilization:
mask = utils.stabilize(foundFace, self.__no_of_frames + 1, image,
face, mask)
hand, hand_contour = utils.get_my_hand(mask, True)
hand_pose, direction = 'None', 'None'
if hand_contour is not None:
motion_detected = self.__motion.get_hand_motion(hand_contour)
else:
return
if not motion_detected:
hand_pose = recognise_hand_pose(
hand,
directly_from_hand=True,
model_path='Models/silatra_gesture_signs.sav')
else:
direction = motion_detected
self.__observations.append((hand_pose, direction))
self.__no_of_frames += 1
def recognise_hand_pose(image,
directly_from_hand=False,
model_path='Models/silatra_digits_and_letters.sav',
using_stabilization=False,
no_of_frames=1):
'''
### SiLaTra Hand Pose Recognition
Provides classification for input hand pose image.
Inputs: (a) Mandatory Parameter - Image for which Hand Pose Classification is to be performed.
(b) Optional Parameters (Use them only if you understand them):
(1) directly_from_hand - boolean - Set this to true if you are passing already cropped hand region in `image` parameter.
(2) model_path - String - If an alternate model is to be used, pass the path of its .sav file.
(3) using_stabilization - boolean - If you intend to use Object stabilization, set this to True. Only use this option if you are classifying hand poses from a continuous feed, else its useless.
(4) no_of_frames - Integer - ONLY TO BE USED IF using_stabilization IS True, pass the number of the frame from the continuous feed you are processing.
'''
import pickle
from sklearn.neighbors import KNeighborsClassifier
if not directly_from_hand:
mask = utils.segment(image)
face, foundFace = utils.detect_face(image)
mask = utils.eliminate_face(face, foundFace, mask)
if using_stabilization:
mask = utils.stabilize(foundFace, no_of_frames, image, face, mask)
hand = utils.get_my_hand(mask)
if hand is False: return 'No hand pose in image'
features = utils.extract_features(hand)
else:
features = utils.extract_features(image)
classifier = pickle.load(open(model_path, 'rb'))
hand_pose = classifier.predict([features])[0]
return hand_pose
def recognise_hand_pose(image, directly_from_hand=False, model_path='Models/digits_and_letters.sav', using_stabilization=False, no_of_frames=1):
import pickle
from sklearn.neighbors import KNeighborsClassifier
if not directly_from_hand:
mask = utils.segment(image)
face, foundFace = utils.detect_face(image)
mask = utils.eliminate_face(face, foundFace, mask)
if using_stabilization: mask = utils.stabilize(foundFace, no_of_frames, image, face, mask)
hand = utils.get_my_hand(mask)
if hand is False: return 'No hand pose in image'
features = utils.extract_features(hand)
else: features = utils.extract_features(image)
classifier = pickle.load(open(model_path, 'rb'))
hand_pose = classifier.predict([features])[0]
return hand_pose
# pred = dsk.findSign(img1)
# print("Received Sign:",pred)
# addToQueue(pred)
# pred = predictSign()
# # pred = -1
# print("Stable Sign:",pred)
try:
mask = silatra.segment(img_np)
_,thresh = cv2.threshold(mask,127,255,0)
hand_contour = get_my_hand(thresh, return_only_contour=True)
# hull = cv2.convexHull(hand_contour)
# final_image = np.zeros(img_np.shape, np.uint8)
# cv2.drawContours(final_image, [hand_contour], 0, (0, 255, 0), 2)
M = cv2.moments(hand_contour)
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
if prev_x is 0 and prev_y is 0: prev_x, prev_y = 0, 0
delta_x, delta_y, slope, direction = prev_x-cx, prev_y-cy, 0, 'None'
if delta_x**2+delta_y**2 > THRESHOLD**2:
if delta_x is 0 and delta_y > 0: slope = 999 # inf
elif delta_x is 0 and delta_y < 0: slope = -999 # -inf
maxArea1 = w*h
faceRect = (x,y,w,h)
foundFace = True
'''
mask1 = FaceEliminator.eliminateFace(mask1, foundFace, faceRect)
if displayWindows:
cv2.imshow("Mask12",mask1)
if displayWindows:
cv2.imshow("Originl Img",img_np)
# contour of hand is useless delete k rna h isko
handFound, hand, contours_of_hand = utils.get_my_hand(mask1)
if recognitionMode == "SIGN":
if handFound:
if displayWindows:
cv2.imshow("Your hand",hand)
features = utils.extract_features(hand, gridSize)
pred = utils.predictSign(classifier,features)
print(pred)
else:
pred = -1
utils.addToQueue(pred)
pred = utils.getConsistentSign(displayWindows)
# pred = -1
try:
print(
' ' * 160 +
'\rProcessing image: %3d, Label = %s, From Location: %s' %
(i, label, DATA_LOC),
end='\r')
image = cv2.imread(DATA_LOC + str(label) + "/" + str(i) +
'.png')
if image.shape[0] == 0: continue
mask, _, _ = silatra.segment(image)
_, thresh = cv2.threshold(mask, 127, 255, 0)
hand = get_my_hand(thresh)
features = extract_features(hand, grid)
to_write_data = ''
for feature in features:
to_write_data += str(feature) + ','
to_write_data += label + '\n'
dump_file.write(to_write_data)
total_images_parsed += 1
except Exception as e:
print(e)
continue
total = (time.time() - start)
# print(' '*160+'\rTotal time required = %3.3fs' % (total))
print('Total images parsed: %d' % (total_images_parsed))
This function returns a mask with white areas signifying skin and black areas otherwise.
Returns: mask
"""
import cv2
from numpy import array, uint8
blurred_img = cv2.GaussianBlur(src_img, (5, 5), 0)
blurred_img = cv2.medianBlur(blurred_img, 5)
blurred_img = cv2.cvtColor(blurred_img, cv2.COLOR_BGR2YCrCb)
lower = array([0, 137, 100], uint8)
upper = array([255, 200, 150], uint8)
mask = cv2.inRange(blurred_img, lower, upper)
open_kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))
close_kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (7, 7))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, open_kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel)
return mask
#read_from_folder('Digits/')
#read_from_folder('Letters/')
mask1 = segment(test_image)
'''
src1_mask=cv2.cvtColor(mask1,cv2.COLOR_GRAY2BGR)#change mask to a 3 channel image
out=cv2.subtract(src1_mask,test_image)
out=cv2.subtract(src1_mask,out)
'''
handFound, hand, contours_of_hand = utils.get_my_hand(test_image, mask1)
import pickle
dir_path = os.path.dirname(os.path.realpath(__file__))
print('dir path is \n ', dir_path)
sys.path.insert(0, dir_path+"/Modules")
sys.path.insert(0, dir_path)
import utils
import FaceEliminator
from keras.preprocessing import image
# Following modules are used specifically for Gesture recognition
currentModuleName = __file__.split(os.path.sep)[-1]
print('current module name \n',currentModuleName)
from tensorflow import keras
model = keras.models.load_model('/home/aarav/Desktop/MajorProject/Models/m.h5')
#test_image = cv2.imread('/home/aarav/Desktop/MajorProject/Dataset/Letters/a/100.png')
test_image = cv2.imread('/home/aarav/Desktop/1.png')
gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
handFound, hand, contours_of_hand = utils.get_my_hand(gray)
hand= cv2.resize(hand, (64,64))
hand=hand/255
img = image.img_to_array(hand)
img = np.expand_dims(img, axis = 0)
pred= model.predict_classes(img)
#l= max(pred[0])
print(pred)
def getHandImage(scr_img):
handFound, hand, contours_of_hand = utils.get_my_hand(mask1)
return hand
