def ocr(self, img, psm=7, digits=False):
tesseract_raw.set_page_seg_mode(self.handle, psm)
tesseract_raw.set_is_numeric(self.handle, digits)
img = PIL.fromarray(img.astype('uint8'))
tesseract_raw.set_image(self.handle, img)
text = tesseract_raw.get_utf8_text(self.handle)
tesseract_raw.cleanup(self.handle)
return text
gt = mat["image_info"][0, 0][0, 0][0]
# Read image
img = plt.imread(img_path)
# Create a zero matrix of image size
k = np.zeros((img.shape[0], img.shape[1]))
# Generate hot encoded matrix of sparse matrix
for i in range(0, len(gt)):
if int(gt[i][1]) < img.shape[0] and int(gt[i][0]) < img.shape[1]:
k[int(gt[i][1]), int(gt[i][0])] = 1
# generate density map
k = gaussian_filter_density(k)
image = Image.fromarray(255 * k)
# image.show()
# File path to save density map
file_path = img_path.replace('.jpg', '.h5').replace('images', 'ground')
with h5py.File(file_path, 'w') as hf:
hf['density'] = k
# %%
path_test = "G:/pycharm/CSRnet-master/data/part_A_final/train_data/ground/IMG_107.h5"
# file_path = img_paths[-5].replace('.jpg','.h5').replace('images','ground')
file_path = path_test
print(file_path)
import cyni
import numpy as np
import PIL as Image
cyni.initialize()
device = cyni.getAnyDevice()
device.open()
depthStream = device.createStream("depth", fps=30, width=640, height=480)
#colorStream = device.createStream("color", fps=30, width=1280, height=960)
#colorStream = device.createStream("color", fps=30, width=640, height=480)
#device.setImageRegistrationMode("depth_to_color")
device.setDepthColorSyncEnabled(on=True)
depthStream.start()
# colorStream.start()
# colorFrame = colorStream.readFrame()
# colorFrame = colorStream.readFrame()
# colorFrame = colorStream.readFrame()
# colorFrame = colorStream.readFrame()
depthFrame = depthStream.readFrame()
# registered = cyni.registerColorImage(depthFrame.data, colorFrame.data, depthStream, colorStream)
# Image.fromarray(colorFrame.data).save("color.png")
# Image.fromarray(registered).save("registered.png")
Image.fromarray(cyni.depthMapToImage(depthFrame.data)).save("depth.png")
plt.figure()
plt.scatter(lidarxx, lidaryy, color=colors, s=0.5)
plt.scatter(edgexx, edgeyy, color='k', s=0.5)
plt.xlim([0, w])
plt.ylim([h, 0])
edgeptsind = lidardepthmapIndrec[edges]
edgepts3d = lidar_camcoord[edgeptsind, :]
edgepts3dprojected = (intrinsic @ edgepts3d.T).T
edgepts3dprojected[:,
0] = edgepts3dprojected[:, 0] / edgepts3dprojected[:, 2]
edgepts3dprojected[:,
1] = edgepts3dprojected[:, 1] / edgepts3dprojected[:, 2]
edgepts3dprojected = edgepts3dprojected[:, 0:3]
plt.figure()
plt.imshow(pil.fromarray(rgbarr))
distances = edgepts3dprojected[:, 2]
colors = cm.jet(1 / distances * 10)
plt.gca().scatter(edgepts3dprojected[:, 0],
edgepts3dprojected[:, 1],
color=colors,
s=0.5)
plt.xlim([0, w])
plt.ylim([h, 0])
from utils import *
edges = (bsmvrec > 0) * (bsmvrec < 0.1)
testsets = [[1610, 558, 1609, 554], [1610, 558, 1606, 558],
[407, 618, 408, 613], [332, 587, 331, 583],
[119, 743, 116, 734], [879, 651, 880, 645],
