global data

data = []

if is_fixed_size: # TODO: When resizing we can use minibatch input.

resized_image = image.resize(tuple(reversed(model_image_size)), Image.BICUBIC)

image_data = np.array(resized_image, dtype='float32')

else:

# Due to skip connection + max pooling in YOLO_v2, inputs must have

# width and height as multiples of 32.

new_image_size = (image.width - (image.width % 32),image.height - (image.height % 32))

resized_image = image.resize(new_image_size, Image.BICUBIC)

image_data = np.array(resized_image, dtype='float32')

print(image_data.shape)

image_data /= 255.

image_data = np.expand_dims(image_data, 0) # Add batch dimension.

out_boxes, out_scores, out_classes = sess.run(

[boxes, scores, classes],

feed_dict={

yolo_model.input: image_data,

input_image_shape: [image.size[1], image.size[0]],

K.learning_phase(): 0

})

print('Found {} boxes'.format(len(out_boxes)))

for i, c in reversed(list(enumerate(out_classes))):

predicted_class = class_names[c]

box = out_boxes[i]

score = out_scores[i]

label = '{} {:.2f}'.format(predicted_class, score)

top, left, bottom, right = box

top = max(0, np.floor(top + 0.5).astype('int32'))

left = max(0, np.floor(left + 0.5).astype('int32'))

bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))

right = min(image.size[0], np.floor(right + 0.5).astype('int32'))

coords = (left,top,right,bottom)

minion = {'class': predicted_class, 'score': score, 'location': coords}

global loaded

model_path = os.path.expanduser('YAD2K/model_data/yolo.h5')

anchors_path = os.path.expanduser('YAD2K/model_data/yolo_anchors.txt')

classes_path = os.path.expanduser('YAD2K/model_data/league_classes.txt')

sess = K.get_session() # TODO: Remove dependence on Tensorflow session.

with open(classes_path) as f:

class_names = f.readlines()

class_names = [c.strip() for c in class_names]

with open(anchors_path) as f:

anchors = f.readline()

anchors = [float(x) for x in anchors.split(',')]

anchors = np.array(anchors).reshape(-1, 2)

yolo_model, _ = create_model(anchors, class_names)

yolo_model.load_weights('trained_stage_3_best.h5')

# Verify model, anchors, and classes are compatible

num_classes = len(class_names)

num_anchors = len(anchors)

# TODO: Assumes dim ordering is channel last

model_output_channels = yolo_model.layers[-1].output_shape[-1]

assert model_output_channels == num_anchors * (num_classes + 5), 'Mismatch between model and given anchor and class sizes. '

print('{} model, anchors, and classes loaded.'.format(model_path))

# Check if model is fully convolutional, assuming channel last order.

model_image_size = yolo_model.layers[0].input_shape[1:3]

is_fixed_size = model_image_size != (None, None)

# Generate colors for drawing bounding boxes.

hsv_tuples = [(x / len(class_names), 1., 1.) for x in range(len(class_names))]

colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))

colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))

random.seed(10101) # Fixed seed for consistent colors across runs.

random.shuffle(colors) # Shuffle colors to decorrelate adjacent classes.

random.seed(None) # Reset seed to default.

# Generate output tensor targets for filtered bounding boxes.

# TODO: Wrap these backend operations with Keras layers.

yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))

input_image_shape = K.placeholder(shape=(2, ))

boxes, scores, classes = yolo_eval(yolo_outputs,input_image_shape,score_threshold=args.score_threshold,iou_threshold=args.iou_threshold)

# Save the output into a compact JSON file.

outfile = open('output/game_data.json', 'w')

# This will be appended with an object for every frame.

data_to_write = []

loaded = True

win32gui.RedrawWindow(hWnd, None, None, win32con.RDW_INVALIDATE | win32con.RDW_ERASE)

with mss.mss() as sct:

monitor = {'top': 0, 'left': 0, 'width': 1920, 'height': 1080}

while 'Screen capturing':

last_time = time.time()

# Get raw pixels from the screen, save it to a Numpy array

img = np.array(sct.grab(monitor))

img = Image.fromarray(img)

img.load()

background = Image.new("RGB",img.size,(255,255,255))

background.paste(img,mask=img.split()[3])

test_yolo(background,is_fixed_size,model_image_size,sess,boxes,scores,classes,yolo_model,input_image_shape,class_names,colors)

win32gui.RedrawWindow(hWnd, None, None, win32con.RDW_INVALIDATE | win32con.RDW_ERASE)

# img = test_yolo(background)

# basewidth = 700

# wpercent = (basewidth/float(img.size[0]))

# hsize = int((float(img.size[1])*float(wpercent)))

# img = img.resize((basewidth,hsize), Image.ANTIALIAS)

# img = np.array(img)

# # Display the picture

# cv2.imshow('OpenCV/Numpy normal', img)

# Press "q" to quit

if cv2.waitKey(25) & 0xFF == ord(';'):

cv2.destroyAllWindows()

break

sess.close()

global data

if message == win32con.WM_PAINT:

hdc, paintStruct = win32gui.BeginPaint(hWnd)

dpiScale = win32ui.GetDeviceCaps(hdc, win32con.LOGPIXELSX) / 60.0

fontSize = 80

# http://msdn.microsoft.com/en-us/library/windows/desktop/dd145037(v=vs.85).aspx

lf = win32gui.LOGFONT()

lf.lfFaceName = "Times New Roman"

lf.lfHeight = int(round(dpiScale * fontSize))

#lf.lfWeight = 150

# Use nonantialiased to remove the white edges around the text.

# lf.lfQuality = win32con.NONANTIALIASED_QUALITY

hf = win32gui.CreateFontIndirect(lf)

win32gui.SelectObject(hdc, hf)

rect = win32gui.GetClientRect(hWnd)

# http://msdn.microsoft.com/en-us/library/windows/desktop/dd162498(v=vs.85).aspx

if not loaded:

win32gui.DrawText(hdc,'Loading Neural Net...',-1,rect,win32con.DT_CENTER | win32con.DT_NOCLIP | win32con.DT_SINGLELINE | win32con.DT_VCENTER)

color = win32api.RGB(0,0,255)

color2 = win32api.RGB(255,255,0)

brush = win32gui.CreateSolidBrush(color)

fontSize = 12

lf = win32gui.LOGFONT()

lf.lfFaceName= 'Times New Roman'

lf.lfHeight = int(round(dpiScale * fontSize))

lf.lfQuality = win32con.NONANTIALIASED_QUALITY

hf = win32gui.CreateFontIndirect(lf)

win32gui.SelectObject(hdc, hf)

win32gui.SetTextColor(hdc,color2)

for minion in data:

win32gui.DrawText(hdc,minion['class'] + ' ' + str(int(minion['score'] * 100)),-1,minion['location'],win32con.DT_BOTTOM | win32con.DT_SINGLELINE | win32con.DT_NOCLIP)

win32gui.FrameRect(hdc,minion['location'],brush)

win32gui.EndPaint(hWnd, paintStruct)

return 0

elif message == win32con.WM_DESTROY:

print('Closing the window.')

win32gui.PostQuitMessage(0)

return 0

else:

hInstance = win32api.GetModuleHandle()

className = 'MyWindowClassName'

wndClass = win32gui.WNDCLASS()

wndClass.style = win32con.CS_HREDRAW | win32con.CS_VREDRAW

wndClass.lpfnWndProc = wndProc

wndClass.hInstance = hInstance

wndClass.hCursor = win32gui.LoadCursor(None, win32con.IDC_ARROW)

wndClass.hbrBackground = win32gui.GetStockObject(win32con.WHITE_BRUSH)

wndClass.lpszClassName = className

wndClassAtom = win32gui.RegisterClass(wndClass)

# http://msdn.microsoft.com/en-us/library/windows/desktop/ff700543(v=vs.85).aspx

# Consider using: WS_EX_COMPOSITED, WS_EX_LAYERED, WS_EX_NOACTIVATE, WS_EX_TOOLWINDOW, WS_EX_TOPMOST, WS_EX_TRANSPARENT

# The WS_EX_TRANSPARENT flag makes events (like mouse clicks) fall through the window.

exStyle = win32con.WS_EX_COMPOSITED | win32con.WS_EX_LAYERED | win32con.WS_EX_NOACTIVATE | win32con.WS_EX_TOPMOST | win32con.WS_EX_TRANSPARENT

# http://msdn.microsoft.com/en-us/library/windows/desktop/ms632600(v=vs.85).aspx

# Consider using: WS_DISABLED, WS_POPUP, WS_VISIBLE

style = win32con.WS_DISABLED | win32con.WS_POPUP | win32con.WS_VISIBLE

# http://msdn.microsoft.com/en-us/library/windows/desktop/ms632680(v=vs.85).aspx

hWindow = win32gui.CreateWindowEx(exStyle,wndClassAtom,None, # WindowName

style,

0, # x

0, # y

1920, # width

1080, # height

None, # hWndParent

None, # hMenu

hInstance,

None # lpParam

)

# http://msdn.microsoft.com/en-us/library/windows/desktop/ms633540(v=vs.85).aspx

win32gui.SetLayeredWindowAttributes(hWindow, 0x00ffffff, 255, win32con.LWA_COLORKEY | win32con.LWA_ALPHA)

# http://msdn.microsoft.com/en-us/library/windows/desktop/dd145167(v=vs.85).aspx

#win32gui.UpdateWindow(hWindow)

# http://msdn.microsoft.com/en-us/library/windows/desktop/ms633545(v=vs.85).aspx

win32gui.SetWindowPos(hWindow, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOACTIVATE | win32con.SWP_NOMOVE | win32con.SWP_NOSIZE | win32con.SWP_SHOWWINDOW)

# http://msdn.microsoft.com/en-us/library/windows/desktop/ms633548(v=vs.85).aspx

#win32gui.ShowWindow(hWindow, win32con.SW_SHOW)

t1 = threading.Thread(target=run_nn, args=(hWindow,))

t1.setDaemon(False)

t1.start()