def setWH(self, dt):
x, y = director.window.width, director.window.height
nmin = sc.scale(int((self.x) - (self.bgImage.width / 2)),
int((self.y) - (self.bgImage.height / 2)))
nmax = sc.scale(int((self.x) + (self.bgImage.width / 2)),
int((self.y) + (self.bgImage.height / 2)))
self.width_range = [int(nmin[0]), int(nmax[0])]
self.height_range = [int(nmin[1]), int(nmax[1])]
def setWH(self, dt):
x, y = director.window.width, director.window.height
nmin = sc.scale(int(self.parent.x + (self.x - (self.width / 2))),
int(self.parent.y + (self.y - (self.height / 2))))
nmax = sc.scale(int(self.parent.x + (self.x + (self.width / 2))),
int(self.parent.y + (self.y + (self.height / 2))))
self.width_range = [int(nmin[0]), int(nmax[0])]
self.height_range = [int(nmin[1]), int(nmax[1])]
def setWH(self, dt):
x, y = director.window.width, director.window.height
nmin = sc.scale(int((self.px + self.x) - (self.bgImage.width / 2)),
int((self.py + self.y) - (self.bgImage.height / 2)))
nmax = sc.scale(int((self.px + self.x) + (self.bgImage.width / 2)),
int((self.py + self.y) + (self.bgImage.height / 2)))
if not self.scrollManager == None:
nmin = [
nmin[0], (nmin[1] + self.scrollManager.get_children()[0].y)
]
nmax = [
nmax[0], (nmax[1] + self.scrollManager.get_children()[0].y)
]
self.width_range = [int(nmin[0]), int(nmax[0])]
self.height_range = [int(nmin[1]), int(nmax[1])]
def normalize(allTriPolygones):
# 平移变换, 将模型重心作为空间原点
mp, transed_tripoints = translation.translate(allTriPolygones)
'''
# 旋转变换
rotated_points = rotation.rotate(transed_points)
'''
# 缩放变换, 返回最终预处理好的模型
# final_points是一个二维的nx3数组，每一行代表三角形的一个顶点
final_tripoints = scaling.scale(mp, transed_tripoints)
return final_tripoints
def main():
inputs = [ [1, 1, 4],
[1, 2, 5],
[1, 3, 6],
]
output = [ 3,
2,
1,
]
print("## NON SCALED ##")
test(inputs, output)
print("## SCALED ##")
from scaling import scale
scaled = scale(inputs)
test(scaled, output)
def main():
inputs = [
[1, 1, 4],
[1, 2, 5],
[1, 3, 6],
]
output = [
3,
2,
1,
]
print("## NON SCALED ##")
test(inputs, output)
print("## SCALED ##")
from scaling import scale
scaled = scale(inputs)
test(scaled, output)
def normalize(motion, translate='', rotate='', scale='', clean=True):
"""!
Apply normalization to a motion.
The input motion is not modified.
@param motion numpy.array: The motion to normalize
@param translate: The normalization for translating the motions
@param rotate: The normalization for rotating the motions
@param scale: The normalization for scaling the motions
@param cleaning: Remove duplicate points and large jumps, default true
@return: The normalized motion and the normalization parameters
"""
out = motion
translationRef = translation.translate(out[:, 1:4], translate)
rotationRef = rotation.rotate(out[:, 1:8], rotate)
scalingRef = scaling.scale(out[:, 1:4], scale)
out, removedPoints = cleaning.clean(motion)
return out, translationRef, rotationRef, scalingRef
def test_scale(filename, dist):
im = Image.open(filename)
# different cases from given problems
expections = []
expections.append([(192, 128), (96, 64), (48, 32), (24, 16), (12, 8)]) # 1.
expections.append([(300, 200)]) # 2.
expections.append([(450, 300)]) # 3.
expections.append([(500, 200)]) # 4.
for case in expections:
print "Scaling case %d" % expections.index(case)
for size in case:
out = scale(im, size)
out_name = "scaling-%dx%d.png" % out.size
out_path = os.path.join(dist, out_name)
out.save(out_path)
print " Picture %s has been saved to the assets folder !" % out_name
def normalize(allPolygones, name):
# 平移变换, 将模型重心作为空间原点
mp, transed_points = translation.translate(allPolygones)
# 旋转变换
rotated_points = rotation.rotate(transed_points)
# 缩放变换, 返回最终预处理好的模型
# final_points是一个二维的nx3数组，每一行代表三角形的一个顶点
final_points = scaling.scale(mp, rotated_points)
# 原本final_points是list类型，转换为<type 'numpy.ndarray'>
final_points = np.array(final_points)
#extractEigvect(final_points, name)
#eigvectflat(final_points, name)
# 测试生成二维投影直方图
# histogram.getHist(final_points)
allTris = []
for i in range(len(final_points)/3):
allTris.append(final_points[i*3:i*3+3])
#allTris = np.array(allTris)
# print 'type(allTris): ', type(allTris)
histogram.getTriHist(allTris, final_points, name, 200, 200)
tf.compat.v1.keras.backend.set_session(session)
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--process', action='store_true')
options = parser.parse_args()
lighthouse_image_dir = "l"
other_image_dir = "o"
# Scale the data if flag exists
if options.process:
shutil.rmtree("./" + lighthouse_image_dir)
shutil.rmtree("./" + other_image_dir)
os.mkdir(lighthouse_image_dir)
os.mkdir(other_image_dir)
scaling.scale("lighthouses", lighthouse_image_dir, (64, 64))
scaling.scale("365", other_image_dir, (64, 64))
# Read images in as grayscale images and store as numpy arrays
# Label the classes as 1 for lighthouse, 0 for other
images = []
y = []
for file in os.listdir(lighthouse_image_dir):
image = cv.imread(lighthouse_image_dir + "/" + file, 0)
images.append(image)
y.append(1)
for file in os.listdir(other_image_dir):
images.append(cv.imread(other_image_dir + "/" + file, cv.IMREAD_GRAYSCALE))
y.append(0)
lis1.append(i.text.strip())
results['directions'] = lis1
# Fill out this list comprehension and get each element's text
#
pattern = re.compile(r'(window.lazyModal\(\')(.*)\'\);')
nutr_link = page_graph.find("script", text=pattern)
nutr_link_url = pattern.search(nutr_link.text).group(2)
page_html_nutr = requests.get(nutr_link_url)
page_graph_nutr = BeautifulSoup(page_html_nutr.content)
# print(page_graph_nutr)
for i in page_graph_nutr.find_all('span', {'class', "nutrient-name"}):
# print("Nutrients")
# print(i.text)
lis2.append(i.text)
results['nutrients'] = lis2
print("fin", type(results))
print(results)
return results
rf = RecipeFetcher()
# meat_lasagna = rf.search_recipes('meat lasagna')[0]
print("Please input URL")
url = input()
# print(type(meat_lasagna))
#
results = rf.scrape_recipe(url)
res = scaling.scale(results, 2, "down")
import prePlot import plot import readTRC import RRA import CMC import directories import JR allDir = list(directories.main(directories)) paramsDir = allDir[1] idResultsDir = allDir[7] soResultsDir = allDir[8] cmcResultsDir = allDir[10] jrResultsDir = allDir[11] scaling.scale() # IK.inverseKinematics() # ID.inverseDynamics() # Static Optimization """ input parameters time - t0 & t1 """ # t0 = 0 # t1 = 2 # SO.run(t0,t1) # # readTRC.plot() # RRA.reduceResidualActuators() """
