def main():
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
last_time = time.time()
while (True):
original_img = np.array(ImageGrab.grab(bbox=(4, 27, 645, 510)))
original_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
original_img = cv2.resize(original_img, (80, 60))
print(np.shape(original_img))
cv2.imshow('window', original_img)
keys = key_check()
print('{}'.format(keys))
output = keys_to_output(keys)
training_data.append([original_img, output])
print('FPS: {}'.format(round(1 / (time.time() - last_time))))
last_time = time.time()
if len(training_data) % 500 == 0:
print(len(training_data))
np.save(file_name, training_data)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destoyAllWindows()
break
def camera_thread():
while True:
ret, frame = camera.read()
cv2.imshow("video", frame)
# cv2.waitKey(0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destoyAllWindows()
def btnTrain():
recognizer = cv2.face.LBPHFaceRecognizer_create()
path = 'dataSet'
def getImagesWithID(path):
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
faces = []
IDs = []
for imagePath in imagePaths:
faceImg = Image.open(imagePath).convert('L')
faceNp = np.array(faceImg, 'uint8')
ID = int(os.path.split(imagePath)[-1].split(".")[1])
faces.append(faceNp)
IDs.append(ID)
cv2.imshow("Trainer", faceNp)
cv2.waitKey(100)
return IDs, faces
Ids, faces = getImagesWithID(path)
recognizer.train(faces, np.array(Ids))
recognizer.save('recognizer/trainnerData.yml')
cv2.destoyAllWindows()
for i in xrange(1, len(pts)):
if pts[i - 1] is None or pts[i] is None:
continue
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
cv2.putText(frame, "(" + str(int(x)) + ", " + str(int(y)) + ")", (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
if capture_data:
captured_data.append((x, y, radius))
if len(captured_data) == 5:
total = 0
for xyr in captured_data:
total += xyr[2]
avg = float(total) / float(len(captured_data))
captured_data = []
capture_data = False
log.write("Average radius: " + str(avg) + "\n")
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
elif key == ord("c"):
capture_data = True
captured_data = []
camera.release()
log.close()
cv2.destoyAllWindows()
import cv2
import numpy as np
hand = cv2.imread('capture.png', 0)
ret, the = cv2.threshold(hand, 70, 255, cv2.THRESH_BINARY)
#_,contours,_= cv2.findContours(the.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
result = cv2.findContours(the.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
contours, hierarchy = result if len(result) == 2 else result[1:3]
hull = [cv2.convexHull(c) for c in contours]
final = cv2.drawContours(hand, hull, -1, (255, 0, 0))
cv2.imshow('Orignal Image', hand)
cv2.imshow('Thresh', the)
cv2.imshow('convex Hull', final)
cv2.waitKey(0)
cv2.destoyAllWindows()
def show(caption, img):
cv2.imshow(caption, img)
cv2.waitKey(0)
cv2.destoyAllWindows()
import cv2 #Vamos a utilizar la libreria para imagenes
import sys #Interaccion con el sistema
imagen = cv2.imread('srt.jpg') # Leemos la imagen que queremos mostrar
cv2.putText(
imagen, "SUPERCHARGED", (140, 280), cv2.FONT_HERSHEY_SIMPLEX, 4,
(130, 255, 250), 4, cv2.LINE_AA
) # Escribimos texto en la imagen especificando imagen, titulo,posicion (x,y), tipo de letra , color de texto, parametros propios del sistema
cv2.putText(
imagen, "JESUS SALATIEL", (140, 640), cv2.FONT_HERSHEY_SIMPLEX, 4,
(230, 255, 128), 4, cv2.LINE_AA
) # Escribimos texto en la imagen especificando imagen, titulo,posicion (x,y), tipo de letra , color de texto, parametros propios del sistema
cv2.imshow(
"Esta imagen esta genial",
imagen) #Titulo de la ventana y la imagen que se va mmostrar en ella
key = cv2.waitKey(
0
) & 0xFF # Activamos la opcion de espera por teclado para esperar que presione una tecla
if key == ord(
"q"
): # Comparamos que la letra presionada sea q para poder salir del visor
cv2.imwrite("srt_salida.png",
imagen) #Guardamos una opia de la imagen con distinto nombre
cv2.destoyAllWindows() #Destruimos el proceso de la ventana
import cv2 as cv
import numpy as np
x = np.uint8([250])
y = np.uint8([10])
print(cv.add(x,y))
# saturated operation : all operations are limited to a fixed range between min and max value
# 이 경우 더한값이 255보다 크면 255로 값이 정해짐
print(x+y)
# modulo operation : returns the remainder or signed remainder of a division
# 이 경우 더한 값이 255보다 크면 256으로 나눈 나머지가 됨 (250+10)%256 = 4
# Image Blending
# 블렌딩이나 투명감 주기 위해 이미지에 다른 가중치 부여
# ex) g(X) = (1-a)f0(x) + af1(x)
# 두 사진의 크기 동일해야함.
img1 = cv.imread('Kyungheeuniv.jpg')
img2 = cv.imread('son1.jpg')
dst = cv.addWeighted(img1,0.7,img2,0.3,0)
cv.imshow('dst',dst)
cv.waitKey(0)
cv.destoyAllWindows()