def __enter__(self):
from utils import Rect
from pyglet.gl import GLint, glGetIntegerv, GL_SCISSOR_BOX, glScissor
ob = (GLint * 4)()
glGetIntegerv(GL_SCISSOR_BOX, ob)
ob = list(ob)
box = [int(i) for i in self.box]
nb = Rect(*ob).intersect(Rect(*box))
if nb:
glScissor(nb.x, nb.y, nb.width, nb.height)
self.ob, self.nb = ob, nb
return self
def draw_cross_road(self):
self.background = Image.new('RGB', (self.width, self.height),
color=Colors['background'])
self.drawing = ImageDraw.Draw(self.background)
p1 = (self.width * 0.4, self.height * 0.4)
p2 = (self.width * 0.4, self.height * 0.6)
p3 = (self.width * 0.6, self.height * 0.4)
p4 = (self.width * 0.6, self.height * 0.6)
crossing_area_width = 10
self.pedestrians_rectangles.clear()
self.pedestrians_rectangles.append(Rect(0, 0, p1[0], p1[1]))
self.pedestrians_rectangles.append(Rect(0, p2[1], p2[0], self.height))
self.pedestrians_rectangles.append(Rect(p3[0], 0, self.width, p3[1]))
self.pedestrians_rectangles.append(
Rect(p4[0], p4[1], self.width, self.height))
self.shared_rectangles.clear()
self.shared_rectangles.append(
Rect(p1[0] - crossing_area_width, p1[1], p2[0], p2[1]))
self.shared_rectangles.append(
Rect(p2[0], p2[1], p4[0], p4[1] + crossing_area_width))
self.shared_rectangles.append(
Rect(p3[0], p3[1], p4[0] + crossing_area_width, p4[1]))
self.shared_rectangles.append(
Rect(p1[0], p1[1] - crossing_area_width, p3[0], p3[1]))
for r in self.pedestrians_rectangles:
r.draw(self.drawing, color=Colors['pedestrian_area'])
for r in self.shared_rectangles:
r.draw(self.drawing, color=Colors['shared_area'])
def get_sidebar_coords(self, ui_el):
if self.top_o is None and self.bottom_o is None \
and self.left_o is None and self.right_o is None:
# Let's see if we're being applied on a GridMenu-like thing, fail otherwise
entry_width = getattr(ui_el.view, "entry_width", None)
cols = getattr(ui_el, "cols", None)
if not entry_width and not cols:
return self.get_coords_for_unrecognized_ui_el(ui_el)
else:
# Simplified case - GridMenu with a sidebar on the right
left_offset = ui_el.view.entry_width*ui_el.cols
sidebar_width = ui_el.o.width-left_offset
return Rect(left_offset, 0, ui_el.o.width-1, ui_el.o.height)
else:
return Rect(self.left_o, self.top_o, self.right_o, self.bottom_o)
def get_accuracy(net, inputs, net_config, threshold=0.9):
bbox_list, conf_list = forward(net, inputs, net_config)
anno = inputs['anno']
image = inputs['raw']
count_anno = 0.0
for r in anno:
count_anno += 1
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] * net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
if conf < threshold:
continue
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects, net_config)
count_cover = 0.0
count_error = 0.0
count_pred = 0.0
for rect in acc_rects:
if rect.true_confidence < threshold:
continue
else:
count_pred += 1
x1 = rect.cx - rect.width / 2.
x2 = rect.cx + rect.width / 2.
y1 = rect.cy - rect.height / 2.
y2 = rect.cy + rect.height / 2.
iscover = False
for r in anno:
if overlap_union(x1, y1, x2, y2, r.x1, r.y1, r.x2,
r.y2) >= 0.5: # 0.2 is for bad annotation
iscover = True
break
if iscover:
count_cover += 1
else:
count_error += 1
# cv2.rectangle(image, (int(x1),int(y1)),(int(x2),int(y2)),
# (255,0,0), 2)
# cv2.rectangle(image, (int(r.x1),int(r.y1)),(int(r.x2),int(r.y2)),
# (0,255,0), 2)
# if count_pred!=0 and count_anno!=0:
# print 1-count_cover/count_pred, count_cover/count_anno
# plt.imshow(image)
# plt.show()
return (count_cover, count_error, count_anno, count_pred)
def draw_bar(self, c, top_y):
# type: (Canvas, int) -> None
outline_coords = Rect(
self.margin, top_y, self.o.width - self.margin,
min(top_y + self.bar_height, self.o.height - self.margin))
bar_width = outline_coords.right - outline_coords.left - self.padding * 2
bar_width *= (self.progress / 100.0)
bar_coords = Rect(outline_coords.left + self.padding,
outline_coords.top + self.padding,
outline_coords.left + self.padding + int(bar_width),
outline_coords.bottom - self.padding)
c.rectangle(outline_coords, fill=False, outline=True)
c.rectangle(bar_coords, fill=True, outline=False)
def __init__(self, parent, width=800, height=600):
super().__init__(parent, 'tooltip', 'window title', True, width,
height)
from button import Button
self.ui = []
Button(self, self.ui, 'save', Rect(742, 560, 10, 10),
lambda: self.hide(), '')
self.show()
def get_page_size(page, offset=50):
rects = [Rect((p['area']['x'], p['area']['y'],
p['area']['w'], p['area']['h']))
for p in page]
bottom_right = [[r.bottom, r.right] for r in rects]
bottom_right = np.array(bottom_right)
max_br = bottom_right.max(axis=0) + offset
for idx, (p, rect) in enumerate(zip(page, rects)):
page[idx]['rect'] = rect
return int(max_br[0]), int(max_br[1]), page
def find_intersection_rect(rect_one, rect_two):
""" Given two rectangles, find the common section. """
new_tl_x = max(rect_one.tl_x, rect_two.tl_x)
new_tl_y = max(rect_one.tl_y, rect_two.tl_y)
new_br_x = min(rect_one.br_x, rect_two.br_x)
new_br_y = min(rect_one.br_y, rect_two.br_y)
new_rect = Rect(new_tl_x, new_tl_y, new_br_x, new_br_y)
return new_rect
def init_pedestrian(self):
size = Sizes['pedestrian']
i = 0
while (i < self.num_pedestrians):
x = np.random.randint(size, self.width - size)
y = np.random.randint(size, self.height - size)
r = Rect(x, y, x + size, y + size)
if r.within_any(self.pedestrians_rectangles):
p = Pedestrian(r, size=size, env=self)
p.draw(self.drawing, color=Colors['pedestrian'])
self.pedestrians.append(p)
i += 1
def init_vehicle(self):
size = Sizes['vehicle']
i = 0
while (i < self.num_vehicles):
x = np.random.randint(size, self.width - size)
y = np.random.randint(size, self.height - size)
r = Rect(x, y, x + size, y + size)
if not r.intersects_any(self.pedestrians_rectangles):
v = Vehicle(r, size=size, env=self)
v.draw(self.drawing, color=Colors['vehicle'])
self.vehicles.append(v)
i += 1
def get_transform(self):
'''
Map the SVG bounding box onto the plot area.
'''
# Shrink either x_size or y_size to match aspect ratio of SVG
svg_ratio = self.svg_bounding_box.ratio
plot_ratio = 1.0 * self.plot_size_mm.y / self.plot_size_mm.x
if svg_ratio > plot_ratio:
# svg is taller than plot - shrink the x
self.plot_size_mm.x = self.plot_size_mm.y / svg_ratio
else:
# svg is wider than plot - shrink the y
self.plot_size_mm.y = self.plot_size_mm.x * svg_ratio
if self.plot_from_origin:
# output extents are (0, 0 -> x_size, y_size) + offset, if plot_from_origin
self.plot_extents = Rect(self.offset_mm, self.offset_mm + self.plot_size_mm)
else:
# output extents are (bed_centre +/- size / 2) + offset, if plot_from_centre
self.plot_extents = Rect((self.plot_bed_mm.size - self.plot_size_mm), (self.plot_bed_mm.size + self.plot_size_mm)) / 2.0 + self.offset_mm
# output extents are clipped to bed_size
self.plot_extents = self.plot_extents.clip(self.plot_bed_mm)
print(f'Plot extents: {self.plot_extents}')
# Transform SVG bounding box to plot extents
# flip the SVG y values, since the coordinate systems are different
temp = self.svg_bounding_box.corner0.y
self.svg_bounding_box.corner0.y = self.svg_bounding_box.corner1.y
self.svg_bounding_box.corner1.y = temp
scale = self.plot_extents.size / self.svg_bounding_box.size
offset = self.plot_extents.corner0 - scale * self.svg_bounding_box.corner0
print(f'scale: {scale}; offset: {offset}')
return scale, offset
def get_aoi(self):
"""
Get the current area of interest.
Returns
=======
rect: Rect object
Area of interest
"""
rect_aoi = ueye.IS_RECT()
ueye.is_AOI(self.h_cam, ueye.IS_AOI_IMAGE_GET_AOI, rect_aoi,
ueye.sizeof(rect_aoi))
return Rect(rect_aoi.s32X.value, rect_aoi.s32Y.value,
rect_aoi.s32Width.value, rect_aoi.s32Height.value)
def get_accuracy(net, inputs, net_config, threshold=0.9):
bbox_list, conf_list = forward(net, inputs, net_config, True)
anno = inputs['anno']
count_anno = 0.0
for r in anno:
count_anno += 1
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] * net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
if conf < threshold:
continue
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects, net_config)
count_cover = 0.0
count_error = 0.0
count_pred = 0.0
for rect in acc_rects:
if rect.true_confidence < threshold:
continue
else:
count_pred += 1
x1 = rect.cx - rect.width / 2.
x2 = rect.cx + rect.width / 2.
y1 = rect.cy - rect.height / 2.
y2 = rect.cy + rect.height / 2.
iscover = False
for r in anno:
if overlap_union(x1, y1, x2, y2, r.x1, r.y1, r.x2,
r.y2) >= 0.5:
iscover = True
break
if iscover:
count_cover += 1
else:
count_error += 1
return (count_cover, count_error, count_anno, count_pred)
def __init__(self):
super().__init__(None, 'ergodox-gui-configurator')
self.viewing_layer = 0
self.keys = []
scale = 4
offset = 50
self.read_data('dvorak.json')
self.read_data()
for key in self.data['matrix']:
Button(
self,
self.keys,
key['layers'][0]['label'],
Rect(
offset + key['x'] * scale,
offset + key['y'] * scale,
key['w'] * scale,
key['h'] * scale
),
lambda: KeycapConfigWindow(self).show()
)
self.active_layer = 0
self.ui = []
Checkbox(self, self.ui, 'LED 1', Rect(500, 500, 70, 50), self.data['layers'][self.active_layer]['leds']['led1'] * 2, self.check_led_button1_state)
Checkbox(self, self.ui, 'LED 1', Rect(550, 500, 70, 50), self.data['layers'][self.active_layer]['leds']['led2'] * 2, self.check_led_button2_state)
Checkbox(self, self.ui, 'LED 1', Rect(600, 500, 70, 50), self.data['layers'][self.active_layer]['leds']['led3'] * 2, self.check_led_button3_state)
#lb = LayerButton(self, 0, 0, self.layers)
#Button(self, 'write', Rect(742, 560, button_size[0], button_size[1]),
# lambda: self.write(), self.ui, '')
self.show()
def _find_shapes_for_nodes_by_page(self, page_no):
translated_page_no = self._page_no_translation(page_no)
shapes = []
if translated_page_no in self.data.pages.keys(
): #get_inherited dictionary
shapes += self.data.db_manipulator.fetch_shapes(
self.data.pages[translated_page_no])
for dictionary in self.data.shape_dictionaries:
if dictionary.page == translated_page_no:
shapes += self.data.db_manipulator.fetch_shapes(
dictionary.db_id)
self.data.add_shapes(shapes)
#page_dictionaries = self.data.db_manipulator.fetch_dictionaries_for_page(self.data.current_document.db_id, self._page_no_translation(page_no))
self.data.add_blits(
self.data.db_manipulator.fetch_blits(
self.data.current_document.db_id, translated_page_no),
translated_page_no)
blit_rects = []
for blit in self.data.blits[translated_page_no]:
rect = Rect(blit.x, blit.y, blit.w, blit.h)
rect.shape = blit.shape
blit_rects.append(rect)
#for node in self.hocr_pages[translated_page_no].chars:
for node in self.text_model[translated_page_no].chars:
node_rect = Rect(*node.rect)
for blit_rect in blit_rects:
if node_rect.intersect(blit_rect):
node.shapes.append(blit_rect.shape)
node.blits.append(blit_rect)
for node in self.text_model[translated_page_no].lines:
node_rect = Rect(*node.rect)
for blit_rect in blit_rects:
if node_rect.intersect(blit_rect):
#node.shapes.append(blit_rect.shape)
node.blits.append(blit_rect)
def checkControlLine(self, img):
print('[INFO] checkControlLine')
hls = cv.cvtColor(img, cv.COLOR_RGBA2RGB)
hls = cv.cvtColor(hls, cv.COLOR_RGB2HLS)
# show_image(hls)
kernelErode = np.ones((5, 5), np.uint8)
kernelDilate = np.ones((20, 20), np.uint8)
# Threshold the HSV image to get only blue colors
threshold = cv.inRange(hls, CONTROL_LINE_COLOR_LOWER,
CONTROL_LINE_COLOR_UPPER)
# show_image(threshold)
threshold = cv.erode(threshold, kernelErode, iterations=1)
threshold = cv.dilate(threshold, kernelDilate, iterations=1)
threshold = cv.GaussianBlur(threshold, (5, 5), 2, 2)
# show_image(threshold)
im2, contours, hierarchy = cv.findContours(threshold, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
# cv.drawContours(img, contours, -1, (0,255,0), 3)
# show_image(img)
# show_image(im2)
controlLineRect = None
for contour in contours:
x, y, w, h = cv.boundingRect(contour)
# rect = cv.minAreaRect(contour)
# ((x, y) , (w, h), angle) = rect
print('[INFO] boundingRect', x, y, w, h)
# TODO: maybe ask CJ should we change the constants control line?s
# ('[INFO] boundingRect', 1344, 0, 70, 112)
if (CONTROL_LINE_POSITION_MIN < x and x < CONTROL_LINE_POSITION_MAX
and CONTROL_LINE_MIN_HEIGHT < h
and CONTROL_LINE_MIN_WIDTH < w
and w < CONTROL_LINE_MAX_WIDTH):
print('[INFO] controlLine found!')
# cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
# show_image(img)
controlLineRect = Rect(x, y, w, h)
# DEPRECATED ROTATED RECTANGLE WAY
# box = cv.boxPoints(rect)
# box = np.int0(box)
# cv.drawContours(img,[box],0,(0,0,255),2)
# show_image(img)
# controlLineRect = rect
return controlLineRect
def bounding_rect(self):
N = len(self.positions)
xs = np.empty(N)
ys = np.empty(N)
for i, position in enumerate(self.positions):
xs[i] = position.center().x
ys[i] = position.center().y
rect = Rect(
min_x=np.min(xs),
min_y=np.min(ys),
max_x=np.max(xs),
max_y=np.max(ys),
)
return rect
def select_shape(self, node):
if not node.shape_selected:
node_rect = Rect(*node.rect)
shapes_by_coverage_and_size = {}
for i in range(len(node.shapes)):
blit = node.blits[i]
shapes_by_coverage_and_size[(blit.coverage_percent(node_rect),
blit.size)] = (node.shapes[i],
blit)
shapes = []
blits = []
for shape_key in sorted(shapes_by_coverage_and_size.keys(),
key=itemgetter(0, 1),
reverse=True):
shapes.append(shapes_by_coverage_and_size[shape_key][0])
blits.append(shapes_by_coverage_and_size[shape_key][1])
node.shapes = shapes
node.blits = blits
node.shape_selected = True
return node.shapes[0]
def __init__(
self,
settings,
svg_path,
gcode_path,
plot_from_origin,
x_offset_mm,
y_offset_mm,
x_size_mm,
y_size_mm,
rotate,
):
# Check File Validity
if not os.path.isfile(svg_path):
raise ValueError('File \''+svg_path+'\' not found.')
if not svg_path.endswith('.svg'):
raise ValueError('File \''+svg_path+'\' is not an SVG file.')
self.settings = settings
self.svg_path = svg_path
self.gcode_path = gcode_path
self.gcode_file = GCodeFile(self.gcode_path, self.settings)
# Get the svg Input File
input_file = open(self.svg_path, 'r')
self.svg_root = ET.parse(input_file).getroot()
input_file.close()
self.rotate_rads = rotate * pi / 180
self.svg_bounding_box = self.get_svg_bounding_box()
bed_area_mm = Vector2(self.settings.bed_area_mm)
self.plot_bed_mm = Rect(Vector2(), bed_area_mm)
self.plot_size_mm = Vector2(x_size_mm or bed_area_mm.x, y_size_mm or bed_area_mm.y)
self.offset_mm = Vector2(x_offset_mm, y_offset_mm)
self.plot_from_origin = plot_from_origin
self.scale, self.offset = self.get_transform()
def forward_test(net, inputs, net_config, enable_ip_split):
net.phase = 'test'
bbox_list, conf_list = forward(net,
inputs,
net_config,
deploy=True,
enable_ip_split=enable_ip_split)
img = np.copy(inputs["raw"])
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] * net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
if conf < 0.9:
continue
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects, net_config)
for rect in acc_rects:
if rect.true_confidence < 0.9:
continue
cv2.rectangle(
img,
(rect.cx - int(rect.width / 2), rect.cy - int(rect.height / 2)),
(rect.cx + int(rect.width / 2), rect.cy + int(rect.height / 2)),
(255, 0, 0), 2)
return img
def get_some_nonfaces(imgs, labels, num_examples):
'''Get some random nonface images of size BOX_SIZE'''
nonfaces = []
pbar = tqdm(total=num_examples, desc='Extracting some nonfaces')
while True:
for img_id, img in imgs.items():
# random scale
scale = choice(SCALES)
scaled_img = rescale(img, scale, multichannel=False)
# random position
try:
y = randrange(scaled_img.shape[0] - BOX_SIZE.h)
x = randrange(scaled_img.shape[1] - BOX_SIZE.w)
except ValueError: # images to small to extract a rect
continue
random_rect = Rect(x, y, BOX_SIZE.w, BOX_SIZE.h)
if not random_rect.overlap(*labels[img_id], threshold=0.4):
nonfaces.append(random_rect.extract_from_img(scaled_img))
pbar.update()
if len(nonfaces) >= num_examples:
return nonfaces
t_adjust = t_to_steady_state
# List of stimulation time to get the min #
t_stim_temp = []
# External stimulations for each region at each iteration #
ext_stim = np.zeros((len(t), len(regions)))
# Loop for all regions stimulated #
for zone, params_signal in ext_stim_dict.items():
for i in range(len(params_signal['t_stim'])):
# List of all times of stimulation #
t_stim_temp.append(params_signal['t_stim'][i])
# Gaussian filter on a rectangular signal (same size of the simulation) #
# ext_stim_temp = gaussian_filter(Rect(t,params_signal['t_stim'][i], params_signal['t_stim'][i]+params_signal['dur_stim'][i]), 1/step)
ext_stim_temp = Rect(
t, params_signal['t_stim'][i],
params_signal['t_stim'][i] + params_signal['dur_stim'][i])
# Normalization and set of the amplitude #
ext_stim[:,
regions.index(zone)] += (ext_stim_temp *
params_signal['max_ampl_stim'][i] /
max(ext_stim_temp))
# The for loop works as a if; if there is no stimulation, t_adjust stay to its init value, else we keep the early one #
t_adjust = min(t_stim_temp) - t_stim_Asier_data
# -------------- Connexions to ignore; by keeping them at steady-state --------- #
# We keep the initial value #
connexion_ignored = {} # Empty dictionary to avoid error in solve()
# Experiment to ignore pathway; values need to be array in case there are several of them #
#connexion_ignored = {'Striatum' : ['GPe']} # Indirect pathway ignored
def rect(self):
'''Returns a Rect suitable for collision detection'''
return Rect(self.x - self.hw, self.y - self.hh, self.img.width,
self.img.height)
def rect(self):
'''Returns a Rect suitable for collision'''
x, y = self.world.world_to_screen(self.x, self.y)
x += self.offset[0]
y += self.offset[1] - self.world.half_stack
return Rect(x, y, self.world.tile_size[0], -self.world.tile_size[1])
def test(config):
""" Takes the config, run test program
"""
data_mean = load_data_mean(config["data"]["idl_mean"],
config["net"]["img_width"],
config["net"]["img_height"],
image_scaling=1.0)
num_test_images = 599
# Warning: load_idl returns an infinite generator. Calling list() before islice() will hang.
test_list = list(
itertools.islice(
load_idl(config["data"]["test_idl"], data_mean, config["net"],
False), 0, num_test_images))
img = np.copy(test_list[-1]["raw"])
# plt.imshow(img)
net = apollocaffe.ApolloNet()
net.phase = 'test'
forward(net, test_list[0], config["net"], True)
net.load("data/snapshot/reinspect_hcs_800000.h5")
annolist = al.AnnoList()
net_config = config["net"]
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
if config.has_key("conf_th"):
conf_th = config["conf_th"]
else:
conf_th = 0.6
mae = 0.
for i in range(num_test_images):
inputs = test_list[i]
bbox_list, conf_list = forward(net, inputs, net_config, True)
img = np.copy(inputs["raw"])
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] *
net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
# notice the output rect [cx, cy, w, h]
# cx means center x-cord
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects)
display = True
if display:
for rect in acc_rects:
if rect.true_confidence < conf_th:
continue
cv2.rectangle(img, (rect.cx - int(rect.width / 2),
rect.cy - int(rect.height / 2)),
(rect.cx + int(rect.width / 2),
rect.cy + int(rect.height / 2)), (255, 0, 0), 2)
# cv2.circle(img,
# (rect.cx, rect.cy),
# ((rect.width + rect.height)/4),
# (255,0,0),
# 2)
img_name = './data/tmp/%05d.jpg' % i
plt.imsave(img_name, img)
plt.figure(figsize=(15, 10))
plt.imshow(img)
anno = al.Annotation()
anno.imageName = inputs["imname"]
# count
number = 0
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width / 2.
r.x2 = rect.cx + rect.width / 2.
r.y1 = rect.cy - rect.height / 2.
r.y2 = rect.cy + rect.height / 2.
r.score = rect.true_confidence
anno.rects.append(r)
if r.score > conf_th:
number += 1
annolist.append(anno)
mae += abs(number - len(inputs["rects"]))
print anno.imageName, number, len(
inputs["rects"]), abs(number - len(inputs["rects"]))
print mae / num_test_images
def convert_deploy_2_train(boot_deploy_list,
data_mean,
net_config,
max_heads=999999,
threshold=0.9,
jitter=True,
if_random=True):
annos = []
cnt = 0
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
for dic in boot_deploy_list:
anno = Annotation()
anno.imageName = dic["imname"]
bbox_list = dic["bbox"]
conf_list = dic["conf"]
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] *
net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
if conf < threshold:
continue
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects, net_config)
for rect in acc_rects:
if rect.true_confidence >= threshold:
r = AnnoRect()
r.x1 = int(rect.cx - rect.width / 2.)
r.x2 = int(rect.cx + rect.width / 2.)
r.y1 = int(rect.cy - rect.height / 2.)
r.y2 = int(rect.cy + rect.height / 2.)
anno.rects.append(r)
annos.append(anno)
cnt += len(anno.rects)
if cnt >= max_heads:
break
print 'deployed', len(annos), 'images with', cnt, 'heads'
while True:
if if_random:
random.shuffle(annos)
for anno in annos:
if jitter:
jit_image, jit_anno = annotation_jitter(
anno,
target_width=net_config["img_width"],
target_height=net_config["img_height"])
else:
jit_image = imread(anno.imageName)
jit_anno = anno
image = image_to_h5(jit_image, data_mean, image_scaling=1.0)
boxes, box_flags = annotation_to_h5(jit_anno,
net_config["grid_width"],
net_config["grid_height"],
net_config["region_size"],
net_config["max_len"])
yield {
"num": len(annos),
"imname": anno.imageName,
"raw": jit_image,
"image": image,
"boxes": boxes,
"box_flags": box_flags,
'anno': jit_anno
}
class App:
# restart BUTTON
IMG_ID = 0
BTN_W, BTN_H = 32, 32
BTN_RESTART = Rect(0, 96, BTN_W, BTN_H, COLKEY)
def __init__(self):
global WINDOW_HEIGHT, WINDOW_WIDTH, CAPTION, FPS, COLKEY
pyxel.init(WINDOW_WIDTH, WINDOW_HEIGHT, caption=CAPTION, fps=FPS)
# === Generate Instances ===
self.player = Player()
self.enemy = Enemy()
self.backgnd = BackGround()
# TODO: make game title
self.player.beGameover() # not good way ...
pyxel.load("asset/asset.pyxel")
pyxel.run(self.update, self.draw)
def update(self):
self.backgnd.update() # execution order: back layer to front layer
self.enemy.update()
self.player.update()
# score update
if Player.getState() != "IDLE":
Score.update()
# === Restart ===
if Player.getState() == "IDLE":
if pyxel.btnp(pyxel.KEY_SPACE):
Score.saveHighScore()
# initialize state
Score.initialize()
self.backgnd.initialize()
self.enemy.initialize()
self.player.initialize()
music.start_music()
def draw(self):
global WINDOW_HEIGHT, WINDOW_WIDTH, DEBUG
pyxel.cls(ColPal.gray_dark)
self.backgnd.blt()
self.enemy.blt()
self.player.blt()
# show score
pyxel.text(WINDOW_WIDTH - 50, 5,
"HI {} {}".format(Score.getHighScore(),
Score.getScore()), ColPal.white)
# if gameover, show restart button
if Player.getState() == "IDLE":
pyxel.text(WINDOW_WIDTH // 2 - 5, WINDOW_HEIGHT // 2 - 5,
"GAME OVER\n[SPACE] TO CONTINUE", ColPal.white)
pyxel.blt(WINDOW_WIDTH // 2 - 48, WINDOW_HEIGHT // 2 - 16,
App.IMG_ID, *App.BTN_RESTART.getRect())
if DEBUG:
pyxel.text(0, 20, "Frame: {}".format(pyxel.frame_count),
ColPal.orange)
def get_char_rect(self):
node = self.text_model[self.text_model.current_page].current_char_node
rect = Rect(node.x, node.y, node.w, node.h)
return rect
def test(config):
"""Test the model and output to test/output."""
net = apollocaffe.ApolloNet()
net_config = config["net"]
data_config = config["data"]
solver = config["solver"]
image_mean = load_data_mean(
data_config["idl_mean"], net_config["img_width"],
net_config["img_height"], image_scaling=1.0)
input_gen_test = load_idl(data_config["test_idl"],
image_mean, net_config, jitter=False)
forward(net, input_gen_test.next(), config["net"])
try:
net.load(solver["weights"])
except:
pass
net.phase = 'test'
test_loss = []
for i in range(solver["test_iter"]):
input_test = input_gen_test.next()
image = input_test["raw"]
tic = time.time()
bbox, conf = forward(net, input_test, config["net"], True)
print "forward deploy time", time.time() - tic
bbox_list = bbox
conf_list = conf
pix_per_w = 32
pix_per_h = 32
all_rects = [[[] for x in range(net_config['grid_width'])] for y in range(net_config['grid_height'])]
for n in range(len(bbox_list)):
for k in range(net_config['grid_width'] * net_config['grid_height']):
y = int(k / net_config['grid_width'])
x = int(k % net_config['grid_width'])
bbox = bbox_list[n][k]
conf = conf_list[n][k,1].flatten()[0]
abs_cx = pix_per_w/2 + pix_per_w*x + int(bbox[0,0,0])
abs_cy = pix_per_h/2 + pix_per_h*y+int(bbox[1,0,0])
w = bbox[2,0,0]
h = bbox[3,0,0]
all_rects[y][x].append(Rect(abs_cx,abs_cy,w,h,conf))
acc_rects = stitch_rects(all_rects)
#print acc_rects
for idx, rect in enumerate(acc_rects):
if rect.true_confidence < 0.9:
print 'rejected', rect.true_confidence
continue
else:
print 'found', rect.true_confidence
cv2.rectangle(image, (rect.cx-int(rect.width/2), rect.cy-int(rect.height/2)),
(rect.cx+int(rect.width/2), rect.cy+int(rect.height/2)),
(255,0,0),
2)
cv2.imwrite("test_output/img_out%s.jpg" % i, image)
def labels_to_dict(labels):
'''ndarray of labels (Nx5) to dict of Rect lists'''
labels_dict = {k: [] for k in np.unique(labels[:, 0])}
for k, i, j, h, l in labels:
labels_dict[k].append(Rect(j, i, l, h))
return labels_dict