from time import time
# get planer array library, numpy
# pal = planer.core(numpy)
# get planer array library, cupy
pal = planer.core(cupy)
img = io.imread('test.jpg')
x = (img/255.0).transpose(2, 0, 1)
x = x[None, :, :, :].astype('float32')
x = pal.array(x)
x = resize(x, (224, 224))
net = read_net('mobile')
print('load done!')
net(x)
print('start timing!')
start = time()
for i in range(10):
y = net(x)
print('planer mobilenet-v1 time (x10):', time()-start)
y = pal.argmax(y, axis=-1)
rst = classes[int(y[0])]
print('result:', rst)
plt.imshow(img.astype('uint8'))
plt.title(rst)
def load(lang='ch'):
globals()['net'] = planer.read_net(root+'/openpose')
return resize(img, (h, w))
def normal(img):
img = img.astype('float32')
img.reshape((-1, 3))[:] -= pal.array([104, 117, 123])
return img
img = io.imread('test.jpg')
img_ = pal.array(img)
x = normal(img_).transpose(2, 0, 1)
x = makesize32(x[None, :, :, :])
# 2 files needed, hed.txt, hed.npy
net = read_net('hed')
y = net(x)
net.timer = {}
print('start timing!')
start = time()
for i in range(10):
y = net(x)
print('planer hed time (x10):', time() - start)
run_time = (time() - start) / 10
for k in net.timer:
print(k, net.timer[k])
y = pal.cpu(y)
plt.subplot(121)
# get planer array library, numpy
# get planer array library, cupy
pal = planer.core(cupy)
img = gravel()
img = numpy.concatenate(([img], [img]))
img = img.astype('float32') / 255
#img = numpy.zeros((2,1024,1024), 'float32')
img_ = pal.array(img)
x = img_[None, :, :, :]
net = read_net('cellpose')
print('load done!')
y = net(x)
net.timer = {}
print('start timing!')
start = time()
for i in range(1):
y = net(x)
print('unet detect time x1:', time() - start)
for k in net.timer:
print(k, net.timer[k])
y = pal.cpu(y[0])
plt.subplot(131)
def load():
globals()['net'] = planer.read_net(root + '/face68key.onnx')
51: 'w',
52: 'x',
53: 'y',
54: 'z'
}
def greedy_search(raw, blank=0):
max_id = raw.argmax(2).ravel()
msk = max_id[1:] != max_id[:-1]
max_id = max_id[1:][msk]
return max_id[max_id != blank]
# net = planer.onnx2planer('./crnn.onnx')
net = planer.read_net('./crnn-ocr')
x = page()[:40, :150].astype('float32')
w = 48 * x.shape[1] // x.shape[0]
x = planer.resize(x, (48, w))
x = (x - 0.5) / (90 / 255)
x = x[None, None, :, :]
net.timer = {}
start = time()
y = net(x)
print(time() - start)
for k in net.timer:
print(k, net.timer[k])
yy1 = pal.maximum(y1[i], y1[order[1:]])
xx2 = pal.minimum(x2[i], x2[order[1:]])
yy2 = pal.minimum(y2[i], y2[order[1:]])
w = pal.maximum(0.0, xx2 - xx1 + 1)
h = pal.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = pal.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
net = read_net('yolov3')
y = net(x)
net.timer = {}
start = time()
y = net(x)
print('yolo-v3 time:', time() - start)
for k in net.timer:
print(k, net.timer[k])
plt.imshow(img[:, :, ::-1])
# get one result from the batch results
bbox = post_process(y, anchors_all)[0]
start = time()
def load(name='resnet'):
globals()['net'] = planer.read_net(root+'/%s.onnx'%name)
with open(root+'/imagenet_labs.json') as f:
globals()['classes'] = json.load(f)
des *= np.array(img.shape[:2]).reshape(2, 1, 1) / 224
rst = planer.mapcoord(img.transpose(2, 0, 1), *des * 1)
return rst.transpose(1, 2, 0).astype(np.uint8)
# generate check board
def check_board(shp, d=20):
checkboard = np.zeros(shp, dtype=np.uint8)
msk = np.tile([False] * d + [True] * d, 1000)
checkboard[msk[:shp[0]]] += 128
checkboard[:, msk[:shp[1]]] += 128
return ((checkboard > 0) * 255).astype(np.uint8)
if __name__ == '__main__':
net = planer.read_net('./face_key')
face = imread('./face.jpg')
x = np.array(face.transpose(2, 0, 1)[None, :, :, :])
x = planer.resize(x, (224, 224)).astype(np.float32)
start = time()
y = net(x / 255)
print(time() - start)
rc = y.reshape(-1, 2)[:, ::-1] * 50 + 100
thin = flow_map(face, count_flow(rc, fac=-10))
fat = flow_map(face, count_flow(rc, fac=10))
grid = check_board(face.shape, 30)
thin_grid = flow_map(grid, count_flow(rc, fac=-10))
# size to be 32x
def makesize32(img):
h, w = img.shape[-2:]
w = w // 32 * 32
h = h // 32 * 32
return resize(img, (h, w))
img = io.imread('test.jpg')[:, :, ::-1]
img_ = pal.array(img)
x = normalize(img_).transpose(2, 0, 1)[None, :, :, :].copy()
x = makesize32(x)
# 2 files needed, craft.txt, craft.npy
net = read_net('craft')
print('load done!')
y = net(x)
net.timer = {}
start = time()
print('start timing!')
for i in range(10):
y = net(x)
print('planer craft time (x10):', time() - start)
runtime = (time() - start) / 10
for k in net.timer:
print(k, net.timer[k])
def load():
globals()['net'] = planer.read_net(root+'/fba.onnx')
from matplotlib import pyplot as plt
from time import time
# get planer array library, numpy
# pal = planer.core(numpy)
# get planer array library, cupy
pal = planer.core(numpy)
img = io.imread('test.jpg')[:,:,:3]
x = (img/255.0).transpose(2, 0, 1)
x = x[None, :, :, :].astype('float32')
x = pal.array(x)
x = resize(x, (224, 224))
net = read_net('resnet18')
print('load done!')
y = net(x)
'''
print('start timing!')
net.timer={}
start = time()
for i in range(10):
y = net(x)
print('planer resnet18 time (x10):', time()-start)
'''
y = pal.argmax(y, axis=-1)
rst = classes[int(y[0])]
for k in net.timer:
print(k, net.timer[k])
def load():
globals()['net'] = planer.read_net(root + '/rvm_mobilenetv3.onnx')
def load(name='u2netp_general'):
globals()['net'] = planer.read_net(root + '/%s.onnx' % name)
def load_model(names='cyto_0'):
if isinstance(names, str): return read_net(root + '/models/' + names)
return [read_net(root + '/models/' + i) for i in names]
import cupy
import planer
from planer import read_net, resize
# get planer array library, numpy
# pal = planer.core(numpy)
# get planer array library, cupy
pal = planer.core(cupy)
img = io.imread('comic.png')[:, :, :3]
x = (img / 255.0).transpose(2, 0, 1)
x = x[None, :, :, :].astype('float32')
x = pal.array(x)
net = read_net('ESRGAN')
print('load done!')
y = net(x)
print('start timing!')
start = time()
y = net(x)
print('planer esrgan time:', time() - start)
y = pal.cpu(y)
plt.subplot(121)
plt.imshow(img.astype('uint8'))
plt.subplot(122)
plt.imshow(y[0, :, :, :].transpose(1, 2, 0))
plt.show()
