def main():
for i in list(range(3))[::-1]:
print(i + 1)
time.sleep(1)
while (True):
screen = grab_screen(region=(300, 350, 850, 640))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
vertices = np.array([[0, 160], [0, 125], [200, 125], [200, 160]],
np.int32)
#screen = take_part(screen, [vertices])
#screen = cv2.resize(screen, (100,75))
cv2.imshow('window1', screen)
keys = key_check()
out = grab_keys(keys)
training_data.append([screen, out])
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
if len(training_data) % 5000 == 0:
print(len(training_data))
np.save(f_name, training_data)
def main(macro=template, threshold=0.6):
#jump_time = 0.56s
#jump_height = 118
jump = False
y_prev = None
y_h = 600
while (True):
screen = grab_screen(region=(300, 350, 650, 540))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(screen, (5, 5), 0)
_, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
_, ctrs, _ = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
rects.sort(key=lambda x: x[0])
if jump:
try:
y = rects[0][1]
if abs(y - y_start) < 5 and y_prev < y:
jump_time = time.time() - jump_time
print('jump time', jump_time)
print('height', y_start - y_h)
jump = False
y_h = 600
if y < y_h:
y_h = y
y_prev = y
except:
pass
k = key_check()
if 'up' in k and not jump:
jump_time = time.time()
y_start = rects[0][1]
y_prev = y_start
jump = True
print('tstart ', jump_time, 'y_start', y_start)
elif 'R' in k:
jump = False
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
cv2.imshow('xxx', screen)
def main():
last_time = time.time()
for i in list(range(2))[::-1]:
print(i+1)
time.sleep(1)
while(True):
screen0 = grab_screen(region=(300,450,650,540))
vertices = np.array([[0,160],[0,120], [500,120], [500,160]], np.int32)
l_gray = np.array([140,140,140])
u_gray = np.array([150,150,150])
screen0 = cv2.inRange(screen0, l_gray, u_gray)
gray= screen0
edges = gray
#edges = cv2.Canny(gray, thr1, thr2)
#cv2.imshow('img01', edges)
edges = cv2.dilate(edges, None)
#cv2.imshow('img02', edges)
edges = cv2.erode(edges, None)
#cv2.imshow('img03', edges)
contur_bag = []
_, conturs, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
for contur in conturs:
contur_bag.append([contur, cv2.isContourConvex(contur), cv2.contourArea(contur)])
try:
max_contur = max(contur_bag, key=lambda contur: contur[2])
except ValueError:
continue
mask = np.zeros(edges.shape)
cv2.fillConvexPoly(mask, max_contur[0], (255))
#cv2.imshow('imgx', mask)
mask = cv2.dilate(mask, None, iterations=diliter)
mask = cv2.erode(mask, None, iterations= eroditer)
mask = cv2.GaussianBlur(mask, (blur, blur), 2)
mask = cv2.resize(mask, (100,75))
cv2.imshow('',mask)
#moves = list(np.around(modelx.predict([screen.reshape(-1,100,75,1)])[0]))
moves = (np.argmax(np.around(modelx.predict([mask.reshape(-1,100,75,1)])[0])))
#print(np.argmax(np.around(modelx.predict([screen.reshape(-1,100,75,1)])[0])))
#moves= moves[0]
#print(moves)
print((modelx.predict([mask.reshape(-1,100,75,1)])[0]))
if moves == 0 and max(modelx.predict([mask.reshape(-1,100,75,1)])[0]) > 0.7:
forward()
elif moves == 1 and max(modelx.predict([mask.reshape(-1,100,75,1)])[0]) > 0.75:
fl()
elif moves == 2 and max(modelx.predict([mask.reshape(-1,100,75,1)])[0]) > 0.75:
fr()
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main(macro=template, threshold=0.6):
last_time = time.time()
w1, h1 = template[0].shape[::-1]
w2, h2 = template[1].shape[::-1]
w3, h3 = template[2].shape[::-1]
w4, h4 = template[3].shape[::-1]
w5, h5 = template[4].shape[::-1]
w6, h6 = template[5].shape[::-1]
x_prev = None
nframes = 0
x0 = 100
front = 80
while (True):
#screen = grab_screen(region=(300,350,650,540))
screen = grab_screen(region=(300, 450, 750, 550))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(screen, (5, 5), 0)
_, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
_, ctrs, _ = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
rects = list(filter(lambda x: x[0] > front, rects))
rects.sort(key=lambda x: x[0])
res = [
cv2.matchTemplate(screen, t, cv2.TM_CCOEFF_NORMED)
for t in template
]
loc = [np.where(r >= threshold) for r in res]
points = []
for l in loc[2:]:
points += zip(*l)
nframes += 1
try:
position = loc[0][1][0]
i, closest = argmin(points)
x = rects[0][0]
if x_prev is None:
x_prev = x
else:
dist = x_prev - x
if dist < 0:
nframes = 0
x_prev = x
elif dist > x0:
v = dist / nframes
nframes = 0
x_prev = x
print('current speed', v)
obj = object_type(i, map(lambda x: x[0], loc[2:]))
d = closest[1] - position
if obj == 2 or obj == 3:
k = 50.0 / 120.0
r = 130 + (time.time() - last_time) * k
if d < r:
yd = loc[0][0][0]
yb = closest[0]
h = (yd + h1) - (yb + h5)
if h + 5 < h2:
PressKey(Up)
elif h - 10 < h1:
PressKey(Down)
time.sleep(0.3)
ReleaseKey(Down)
else:
k = 50.0 / 120.0
r = 177 + (time.time() - last_time) * k
if d < r:
PressKey(Up)
except:
pass
if len(rects) > 0:
rect = merge(rects, 50)
cv2.rectangle(screen, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0),
3)
if x_prev is not None:
cv2.rectangle(screen, (x, 0), (x_prev, 100), (0, 255, 0), 3)
cv2.imshow('xxx', screen)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main(macro = template, threshold= 0.6):
last_time = time.time()
w1, h1 = template[0].shape[::-1]
w2, h2 = template[1].shape[::-1]
w3, h3 = template[2].shape[::-1]
w4, h4 = template[3].shape[::-1]
w5, h5 = template[4].shape[::-1]
w6, h6 = template[5].shape[::-1]
for i in list(range(2))[::-1]:
print(i+1)
time.sleep(1)
while(True):
screen = grab_screen(region=(300,350,650,540))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(screen, (5, 5), 0)
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
im, ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
res = [cv2.matchTemplate(screen,t,cv2.TM_CCOEFF_NORMED) for t in template]
loc = [np.where(r >= threshold) for r in res]
points = []
for l in loc[2:]:
points += zip(*l)
#for i in range(len(template[2:])):
# points += zip(*loc[i])
try:
position = loc[0][1][0]
i, closest = argmin(points)
obj = object_type(i, map(lambda x: x[0], loc[2:]))
#print(obj)
d = closest[1] - position
if obj == 2 or obj == 3:
#print('bird', d)
if d < 130:
yd = loc[0][0][0]
yb = closest[0]
h = (yd + h1) - (yb + h5)
#print('bird height', h, h2)
if h+5 < h2:
PressKey(Up)
elif h-10 < h1:
PressKey(Down)
time.sleep(0.3)
ReleaseKey(Down)
else:
if d < 180 and time.time()-last_time < 30:
PressKey(Up)
elif d < 185 and time.time()-last_time < 60 and time.time()-last_time > 30:
PressKey(Up)
elif d < 190 and time.time()-last_time > 60:
PressKey(Up)
except:
pass
for t, l in zip(template,loc):
w, h = t.shape[::-1]
for pt in zip(*l[::-1]):
cv2.rectangle(screen, pt, (pt[0] + w, pt[1] + h), (0,0,255), 1)
for rect in rects:
cv2.rectangle(screen, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
roi = cv2.resize(roi, (28, 28))
nbr = list(np.around(modelx.predict([roi.reshape(-1,28,28,1)])[0]))
print(nbr)
cv2.imshow('xxx',screen)
#moves = list(np.around(modelx.predict([screen.reshape(1,100,75,1)])[0]))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main():
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
while (True):
screen0 = grab_screen(region=(300, 450, 650, 540))
#screen = cv2.cvtColor(screen0, cv2.COLOR_BGR2GRAY)
#vertices = np.array([[0,160],[0,120], [500,120], [500,160]], np.int32)
l_gray = np.array([140, 140, 140])
u_gray = np.array([150, 150, 150])
#screen0 = take_part(screen0, [vertices])
screen0 = cv2.inRange(screen0, l_gray, u_gray)
#cv2.imshow('img000', screen0)
gray = screen0
#gray = cv2.cvtColor(screen0,cv2.COLOR_BGR2GRAY)
#cv2.imshow('img001', gray)
#gray = cv2.inRange(gray, 110, 160)
#cv2.imshow('img01', gray)
edges = gray
#edges = cv2.Canny(gray, thr1, thr2)
#cv2.imshow('img01', edges)
edges = cv2.dilate(edges, None)
#cv2.imshow('img02', edges)
edges = cv2.erode(edges, None)
#cv2.imshow('img03', edges)
contur_bag = []
_, conturs, _ = cv2.findContours(edges, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE)
for contur in conturs:
contur_bag.append(
[contur,
cv2.isContourConvex(contur),
cv2.contourArea(contur)])
try:
max_contur = max(contur_bag, key=lambda contur: contur[2])
except ValueError:
continue
mask = np.zeros(edges.shape)
cv2.fillConvexPoly(mask, max_contur[0], (255))
#cv2.imshow('imgx', mask)
mask = cv2.dilate(mask, None, iterations=diliter)
mask = cv2.erode(mask, None, iterations=eroditer)
mask = cv2.GaussianBlur(mask, (blur, blur), 2)
cv2.imshow('imgx2', mask)
'''
mask_stack_bgr = np.dstack([mask]*3)
mask_stack_bgr = mask_stack_bgr.astype('float32') / 255.0
img = screen0.astype('float32') / 255.0
masked = (mask_stack_bgr * img) + ((1-mask_stack_bgr) * color)
masked = (masked * 255).astype('uint8')
cv2.imshow('img', masked)
'''
mask = cv2.resize(mask, (100, 75))
#screen=cv2.Canny(screen,200,300)
#cv2.imshow('window1', screen)
try:
keys = key_check()
out = grab_keys(keys)
training_data.append([mask, out])
except KeyError:
pass
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
if len(training_data) % 200 == 0:
#print(len(training_data))
np.save(f_name, training_data)