def scale1(img, width, height):
log.add("Editing image")
pts1 = np.float32([[158, 52], [553, 51], [111, 425], [594, 434]])
pts2 = np.float32([[0, 0], [width - 1, 0], [0, height - 1], [width - 1, height - 1]])
retval = cv2.getPerspectiveTransform(pts1, pts2)
warp = cv2.warpPerspective(img, retval, (width, height))
log.add("Image transformed")
# plt.subplot(131),plt.imshow(warp),plt.title('Output')
# plt.show()
return warp
def take(p):
global capture
if capture == "false":
capture = cv2.VideoCapture(-1)
log.add("Taking picture...")
#capture = cv2.VideoCapture(-1)
for i in range(50):
ret, frame = capture.read()
cv2.imwrite(p, frame)
#capture.release()
log.add("Picture saved!")
#!/usr/local/bin/python
import os,sys,cv2
__homedir__ = os.path.dirname(os.path.realpath(__file__))
os.chdir(__homedir__)
from lib import log,webcam,analyze,util
log.add("Script succesfully started")
util.state(__homedir__,"Script wordt gestart")
util.state(__homedir__,"Maken van foto")
#webcam.take(__homedir__ + "\\files\\bord.jpg")
util.newboard(__homedir__)
util.state(__homedir__,"Afbeelding bewerken en analyseren")
analyze.start(__homedir__ + "\\files\\bord.jpg",8,0)
#log.add(analyze.cell(__homedir__ + "\\files\\vak\\x1-y1.jpg"))
util.state(__homedir__,"Controle op mogelijke zetten")
bord = [[0 for x in xrange(8)] for x in xrange(8)]
for ix in range(8):
for iy in range(8):
log.add(str(ix+1) + " , " + str(iy+1) + " = " + str(analyze.cell(__homedir__,ix+1,iy+1)))
bord[ix][iy] = analyze.cell(__homedir__,ix+1,iy+1)
log.add(bord)
util.board2file(bord,__homedir__ + "\\files\\bord.txt")
def start(imgpath, size, border):
log.add("Analyzing image...")
log.add("Loading board image")
img = cv2.imread(imgpath)
log.add("Image loaded")
log.add("Scaling image...")
img = scale(img, 400, 400)
log.add("Image scaled. Saving...")
cv2.imwrite("files\\bord_scaled.jpg", img)
log.add("Image saved")
log.add("Chopping image...")
chop(img, size, size, border)
log.add("Image chopped")
def scale(img, width, height):
log.add("Editing image")
corners1 = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
corners2 = cv2.GaussianBlur(corners1, (5, 5), 0)
# plt.subplot(133),plt.imshow(corners2),plt.title('Blur')
corners = cv2.adaptiveThreshold(corners2, 255, 1, 1, 11, 2)
log.add("Looking for contours")
contours, hierarchy = cv2.findContours(corners, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
biggest = None
max_area = 0
log.add("Comparing contours")
for i in contours:
area = cv2.contourArea(i)
if area > 100:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
if area > max_area and len(approx) == 4:
biggest = approx
max_area = area
log.add("Found contour")
log.add("Transforming image")
biggest = rectify(biggest)
pts1 = np.float32(biggest)
pts2 = np.float32([[0, 0], [width - 1, 0], [0, height - 1], [width - 1, height - 1]])
retval = cv2.getPerspectiveTransform(pts1, pts2)
warp = cv2.warpPerspective(img, retval, (width, height))
log.add("Image transformed")
# plt.subplot(131),plt.imshow(warp),plt.title('Output')
# plt.subplot(132),plt.imshow(corners),plt.title('Input corners')
# plt.show()
return warp