No output by runnig demo.py

onoderay commented 4 years ago

Hello, thanks for nice work and this would be bit naive question since I'm not goot at reading engish. As following your instrunction, I fnally manage to get output result of pose estimation and object detection by running demo.py. But how can I get keypoints coordinates data? I got following messages by running demo.py especially The output will be saved to /home/yuyonod/cv2/CenterNet/src/lib/../../exp/ctdet/default But I couldnt find the any result either keypoints and saved output images in exp folder. Do you have any advises?

Full messages shown below.

python demo.py ctdet --demo ../images/24274813513_0cfd2ce6d0_k.jpg --load_model ../models/ctdet_coco_dla_2x.pth
Fix size testing.
training chunk_sizes: [32]
The output will be saved to  /home/yuyonod/cv2/CenterNet/src/lib/../../exp/ctdet/default
heads {'hm': 80, 'wh': 2, 'reg': 2}
Creating model...
loaded ../models/ctdet_coco_dla_2x.pth, epoch 230

(wiredly it has stopeed here I cant stop by typing Ctr + C)

xingyizhou commented 4 years ago

The image will display on your screen. It won't work if you are using a server via ssh.

onoderay commented 4 years ago

No, no no. What's I meant "output" is not displayed image, it suggests key-points or heatmap skeletons information in multi_pose script and center coordinates and width and height in ctdet script.

David-19940718 commented 4 years ago

@onoderay 你好，请问你这个预测完后的坐标点信息和对应的类别，分数信息能保存出来吗？

xingyizhou commented 4 years ago

The outputs will not show in the screen. If you want to dump the outputs to file, you can do this as is shown in readme:

import sys
CENTERNET_PATH = /path/to/CenterNet/src/lib/
sys.path.insert(0, CENTERNET_PATH)

from detectors.detector_factory import detector_factory
from opts import opts

MODEL_PATH = /path/to/model
TASK = 'ctdet' # or 'multi_pose' for human pose estimation
opt = opts().init('{} --load_model {}'.format(TASK, MODEL_PATH).split(' '))
detector = detector_factory[opt.task](opt)

img = image/or/path/to/your/image/
ret = detector.run(img)['results']

ret will be a python dict: {category_id : [[x1, y1, x2, y2, score], ...], }

KhanhCon commented 4 years ago

The outputs will not show in the screen. If you want to dump the outputs to file, you can do this as is shown in readme:

ret will be a python dict: {category_id : [[x1, y1, x2, y2, score], ...], }

I got float results like this. Is this maybe because I'm using torch 1.0 and cuda 10?

{1: array([[1.88266647e+02, 1.87754349e+02, 2.71509979e+02, 4.44029938e+02, 8.31451654e-01], [3.67457275e+02, 2.16558838e+02, 4.81009155e+02, 4.93061462e+02, 7.73522258e-01], [7.52775818e+02, 1.89528366e+02, 8.61289795e+02, 3.85257935e+02, 7.01904893e-01], [9.20946411e+02, 1.79072983e+02, 1.00360297e+03, 3.96296722e+02, 6.05505347e-01], [4.58280457e+02, 2.42123642e+02, 5.65374817e+02, 3.28342926e+02, 5.52386582e-01], [8.33291443e+02, 1.80540421e+02, 8.87493225e+02, 2.86587189e+02, 4.07754987e-01], [5.11583588e+02, 2.43700302e+02, 5.58589050e+02, 2.81153168e+02, 2.94458210e-01], [9.23730103e+02, 2.09271942e+02, 9.52381470e+02, 2.43316254e+02, 2.45495334e-01], [1.06479016e+03, 2.10522186e+02, 1.10781958e+03, 2.75908295e+02, 2.28151783e-01], [5.91670410e+02, 2.01050720e+02, 6.07173401e+02, 2.27956894e+02, 2.08628222e-01], [9.20510986e+02, 2.29461288e+02, 9.52803772e+02, 2.91918365e+02, 1.73453346e-01], [9.24910522e+02, 1.76875061e+02, 1.00202307e+03, 2.91921082e+02, 1.72886133e-01], [4.52924957e+02, 2.44312332e+02, 5.67311523e+02, 4.16092560e+02, 1.71487212e-01], [9.52959473e+02, 1.78773880e+02, 9.78253662e+02, 2.22386246e+02, 1.69336230e-01], [4.38190582e+02, 2.14866272e+02, 4.95324432e+02, 3.04000519e+02, 1.58925056e-01], [2.01884140e+02, 1.83056427e+02, 2.23812698e+02, 2.15974365e+02, 1.49776936e-01], [7.02421753e+02, 1.99203094e+02, 7.14362366e+02, 2.33888977e+02, 1.47609934e-01], [1.20856171e+02, 1.60006302e+02, 1.49419968e+02, 2.02216385e+02, 1.42384261e-01], [9.11389771e+02, 2.48144455e+02, 9.80736145e+02, 3.96026978e+02, 1.28656298e-01], [9.17928467e+02, 2.77136261e+02, 9.73367615e+02, 4.02519409e+02, 1.25267625e-01], [7.21496033e+02, 1.97844009e+02, 7.33020813e+02, 2.35363068e+02, 1.20238610e-01], [1.06275977e+03, 1.63221146e+02, 1.10420215e+03, 1.83143524e+02, 1.20213754e-01], [8.07593445e+02, 1.89808380e+02, 8.40500305e+02, 2.27944656e+02, 1.19432658e-01], [4.43526459e+02, 2.98785492e+02, 4.72608887e+02, 3.63207092e+02, 1.03526674e-01]], dtype=float32), 2: array([[5.8128436e+02, 3.2015591e+02, 6.1036902e+02, 4.2451218e+02, 1.4503880e-01], [5.8730334e+02, 3.5827512e+02, 6.0924414e+02, 4.2477673e+02, 1.0464772e-01]], dtype=float32), 3: array([[9.40922485e+02, 1.74940689e+02, 1.10419202e+03, 5.01262695e+02, 7.35163271e-01], [1.05245764e+03, 1.62014038e+02, 1.10320886e+03, 1.83770401e+02, 4.01580423e-01], [3.81964111e+02, 1.93149048e+02, 3.98207001e+02, 2.09870743e+02, 3.71277153e-01], [5.17155334e+02, 1.99897552e+02, 5.52378479e+02, 2.23080872e+02, 3.53017151e-01], [4.52498505e+02, 1.87626419e+02, 4.82792023e+02, 2.10607803e+02, 3.05006742e-01], [5.55579895e+02, 1.98171814e+02, 5.87705933e+02, 2.24355942e+02, 2.82967806e-01], [4.67379333e+02, 1.93787033e+02, 5.52076599e+02, 2.21718597e+02, 2.82801300e-01], [2.58814636e+02, 1.83658615e+02, 3.37679352e+02, 2.87522186e+02, 2.31444865e-01], [3.48811737e+02, 1.88092117e+02, 3.74281311e+02, 1.99832031e+02, 1.72601044e-01], [8.45242065e+02, 1.89480438e+02, 9.59937500e+02, 3.65349396e+02, 1.22586876e-01], [8.83041687e+02, 2.21784256e+02, 9.55514343e+02, 2.81642120e+02, 1.21336140e-01], [1.92001877e+02, 1.81369705e+02, 3.38588562e+02, 2.87826569e+02, 1.17726177e-01], [4.28679077e+02, 1.87498642e+02, 4.51157440e+02, 2.14194687e+02, 1.03312410e-01], [4.34342880e+01, 1.94555893e+02, 3.32047607e+02, 2.91121674e+02, 9.89589319e-02], [9.22172363e+02, 2.15691727e+02, 9.53443115e+02, 2.50752991e+02, 9.62707698e-02], [8.55037781e+02, 2.60071014e+02, 9.33192566e+02, 3.67721710e+02, 9.46588069e-02]], dtype=float32), 4: array([[7.7129700e+02, 2.8584515e+02, 8.5742883e+02, 4.2914856e+02, 4.6862921e-01], [9.1792847e+02, 2.7713626e+02, 9.7336761e+02, 4.0251941e+02, 3.1618059e-01], [8.7064313e+02, 1.8002090e+02, 1.0053579e+03, 3.9324136e+02, 1.9811258e-01], [8.9407764e+02, 3.3471994e+02, 9.2669031e+02, 3.7490765e+02, 1.6305232e-01], [5.9167041e+02, 2.0105072e+02, 6.0717340e+02, 2.2795689e+02, 1.3489471e-01], [8.5686792e+02, 2.8478622e+02, 9.3121967e+02, 3.7431851e+02, 1.3482100e-01], [8.7638965e+02, 2.4456686e+02, 9.2809430e+02, 2.9746774e+02, 1.2985437e-01], [7.5572211e+02, 2.8512769e+02, 7.9052789e+02, 3.9141678e+02, 1.2889129e-01], [8.5924463e+02, 3.3044025e+02, 8.9157703e+02, 3.6739783e+02, 1.0158612e-01], [8.3854456e+02, 1.7627057e+02, 8.9314648e+02, 3.7854059e+02, 9.6070871e-02], [7.5196356e+02, 2.4022119e+02, 7.8954590e+02, 2.9571701e+02, 9.5139056e-02], [8.5355298e+02, 2.1726012e+02, 8.9536554e+02, 2.8793890e+02, 8.8781744e-02]], dtype=float32), 5: array([], shape=(0, 5), dtype=float32), 6: array([[7.8750839e+01, 1.1956803e+02, 1.9045317e+02, 1.9728659e+02, 1.4013235e-01], [8.2164288e+02, 1.5676567e+02, 9.8106909e+02, 1.8659402e+02, 1.1654177e-01], [4.6091650e+02, 1.9312920e+02, 5.4321729e+02, 2.2068866e+02, 9.5691130e-02], [1.0524576e+03, 1.6201404e+02, 1.1032089e+03, 1.8377040e+02, 9.4557509e-02], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 9.3206726e-02]], dtype=float32), 7: array([], shape=(0, 5), dtype=float32), 8: array([[2.5881464e+02, 1.8365862e+02, 3.3767935e+02, 2.8752219e+02, 4.2487338e-01], [1.9200188e+02, 1.8136971e+02, 3.3858856e+02, 2.8782657e+02, 2.8179702e-01], [4.6737933e+02, 1.9378703e+02, 5.5207660e+02, 2.2171860e+02, 2.3831420e-01], [8.2164288e+02, 1.5676567e+02, 9.8106909e+02, 1.8659402e+02, 2.1636972e-01], [7.9597824e+01, 1.1954791e+02, 1.9006575e+02, 2.0557053e+02, 2.1429372e-01], [8.3380695e+02, 1.6128096e+02, 9.9013434e+02, 3.9514688e+02, 1.7470759e-01], [5.1715533e+02, 1.9989755e+02, 5.5237848e+02, 2.2308087e+02, 1.7455389e-01], [1.0524576e+03, 1.6201404e+02, 1.1032089e+03, 1.8377040e+02, 1.6903879e-01], [5.5557990e+02, 1.9817181e+02, 5.8770593e+02, 2.2435594e+02, 1.6713984e-01], [4.5249850e+02, 1.8762642e+02, 4.8279202e+02, 2.1060780e+02, 1.6448274e-01], [9.3006848e+02, 1.6615004e+02, 1.1051918e+03, 4.9725168e+02, 1.4698893e-01], [9.5911171e+01, 1.2150968e+02, 2.2566763e+02, 2.0651863e+02, 1.3853368e-01], [8.3998547e+02, 1.7599452e+02, 9.8150793e+02, 2.7579956e+02, 1.3346782e-01], [3.8404456e+02, 1.9263722e+02, 4.0538232e+02, 2.0957277e+02, 1.3329120e-01], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 1.1510180e-01]], dtype=float32), 9: array([], shape=(0, 5), dtype=float32), 10: array([[6.2332794e+02, 1.7543706e+02, 6.3640210e+02, 1.8499434e+02, 1.9979078e-01], [2.3029057e+01, 1.1899847e+02, 4.1986435e+01, 1.6111662e+02, 1.4616837e-01]], dtype=float32), 11: array([], shape=(0, 5), dtype=float32), 12: array([[2.3029057e+01, 1.1899847e+02, 4.1986435e+01, 1.6111662e+02, 1.2493925e-01]], dtype=float32), 13: array([], shape=(0, 5), dtype=float32), 14: array([[4.2238777e+01, 1.8917522e+02, 1.5893756e+02, 2.9668314e+02, 2.2145331e-01], [4.1385326e+01, 1.9171677e+02, 2.1256020e+02, 2.9425726e+02, 2.1918175e-01], [4.4437128e+02, 3.1972482e+02, 6.1312500e+02, 4.9902060e+02, 1.7449246e-01], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 1.3006474e-01]], dtype=float32), 15: array([], shape=(0, 5), dtype=float32), 16: array([], shape=(0, 5), dtype=float32), 17: array([], shape=(0, 5), dtype=float32), 18: array([], shape=(0, 5), dtype=float32), 19: array([], shape=(0, 5), dtype=float32), 20: array([], shape=(0, 5), dtype=float32), 21: array([], shape=(0, 5), dtype=float32), 22: array([], shape=(0, 5), dtype=float32), 23: array([], shape=(0, 5), dtype=float32), 24: array([], shape=(0, 5), dtype=float32), 25: array([[9.3354675e+02, 2.4599426e+02, 9.7790936e+02, 2.9329950e+02, 1.6727217e-01]], dtype=float32), 26: array([[4.49576324e+02, 3.41546967e+02, 4.78700287e+02, 4.96103485e+02, 1.09754406e-01]], dtype=float32), 27: array([[9.3354675e+02, 2.4599426e+02, 9.7790936e+02, 2.9329950e+02, 1.8606143e-01], [1.8866473e+02, 2.9302808e+02, 2.0376270e+02, 3.3678482e+02, 1.1479716e-01], [1.8719382e+02, 2.5007120e+02, 2.0683739e+02, 3.2479196e+02, 9.8798744e-02]], dtype=float32), 28: array([[8.3329144e+02, 1.8054042e+02, 8.8749323e+02, 2.8658719e+02, 1.3669623e-01], [4.4361603e+02, 2.7395215e+02, 4.5814026e+02, 2.9931229e+02, 1.2056076e-01]], dtype=float32), 29: array([[4.6919995e+02, 3.1694785e+02, 6.0089233e+02, 4.1674185e+02, 1.3081582e-01]], dtype=float32), 30: array([], shape=(0, 5), dtype=float32), 31: array([], shape=(0, 5), dtype=float32), 32: array([], shape=(0, 5), dtype=float32), 33: array([], shape=(0, 5), dtype=float32), 34: array([], shape=(0, 5), dtype=float32), 35: array([], shape=(0, 5), dtype=float32), 36: array([], shape=(0, 5), dtype=float32), 37: array([], shape=(0, 5), dtype=float32), 38: array([], shape=(0, 5), dtype=float32), 39: array([], shape=(0, 5), dtype=float32), 40: array([[4.54680023e+02, 3.55583832e+02, 4.73403351e+02, 4.27880035e+02, 2.90563494e-01], [2.51120651e+02, 3.18786499e+02, 2.63868408e+02, 3.42476105e+02, 1.06872126e-01], [5.87303345e+02, 3.58275116e+02, 6.09244141e+02, 4.24776733e+02, 1.06069624e-01]], dtype=float32), 41: array([], shape=(0, 5), dtype=float32), 42: array([], shape=(0, 5), dtype=float32), 43: array([], shape=(0, 5), dtype=float32), 44: array([], shape=(0, 5), dtype=float32), 45: array([], shape=(0, 5), dtype=float32), 46: array([], shape=(0, 5), dtype=float32), 47: array([], shape=(0, 5), dtype=float32), 48: array([], shape=(0, 5), dtype=float32), 49: array([], shape=(0, 5), dtype=float32), 50: array([], shape=(0, 5), dtype=float32), 51: array([], shape=(0, 5), dtype=float32), 52: array([], shape=(0, 5), dtype=float32), 53: array([], shape=(0, 5), dtype=float32), 54: array([], shape=(0, 5), dtype=float32), 55: array([], shape=(0, 5), dtype=float32), 56: array([], shape=(0, 5), dtype=float32), 57: array([[4.4437128e+02, 3.1972482e+02, 6.1312500e+02, 4.9902060e+02, 5.4855621e-01], [4.4780740e+02, 4.0905780e+02, 6.0650165e+02, 4.9715942e+02, 1.2152367e-01], [5.0191052e+02, 4.1048935e+02, 6.0555505e+02, 4.8002225e+02, 1.0679674e-01]], dtype=float32), 58: array([[4.6612814e+02, 3.1615115e+02, 5.8993744e+02, 4.1722537e+02, 9.6560396e-02]], dtype=float32), 59: array([], shape=(0, 5), dtype=float32), 60: array([], shape=(0, 5), dtype=float32), 61: array([], shape=(0, 5), dtype=float32), 62: array([], shape=(0, 5), dtype=float32), 63: array([], shape=(0, 5), dtype=float32), 64: array([], shape=(0, 5), dtype=float32), 65: array([], shape=(0, 5), dtype=float32), 66: array([], shape=(0, 5), dtype=float32), 67: array([], shape=(0, 5), dtype=float32), 68: array([[4.8397363e+02, 2.5237894e+02, 5.0320410e+02, 2.8440067e+02, 1.8055500e-01], [4.4361603e+02, 2.7395215e+02, 4.5814026e+02, 2.9931229e+02, 1.0156604e-01], [4.5192215e+02, 2.5575865e+02, 4.8053168e+02, 3.0243512e+02, 9.4050229e-02]], dtype=float32), 69: array([], shape=(0, 5), dtype=float32), 70: array([], shape=(0, 5), dtype=float32), 71: array([], shape=(0, 5), dtype=float32), 72: array([], shape=(0, 5), dtype=float32), 73: array([], shape=(0, 5), dtype=float32), 74: array([[1.8886520e+02, 3.0652768e+02, 2.0260828e+02, 3.3425604e+02, 1.1647063e-01]], dtype=float32), 75: array([], shape=(0, 5), dtype=float32), 76: array([], shape=(0, 5), dtype=float32), 77: array([], shape=(0, 5), dtype=float32), 78: array([], shape=(0, 5), dtype=float32), 79: array([], shape=(0, 5), dtype=float32), 80: array([], shape=(0, 5), dtype=float32)}

David-19940718 commented 4 years ago

I found a solution to save the results(bounding box, predicted_id with the corresponding score ): https://blog.csdn.net/weixin_43509263/article/details/100799415, everyone can refer to it if you need.

Jumponthemoon commented 4 years ago

The outputs will not show in the screen. If you want to dump the outputs to file, you can do this as is shown in readme: ret will be a python dict: {category_id : [[x1, y1, x2, y2, score], ...], }

I got float results like this. Is this maybe because I'm using torch 1.0 and cuda 10?

{1: array([[1.88266647e+02, 1.87754349e+02, 2.71509979e+02, 4.44029938e+02, 8.31451654e-01], [3.67457275e+02, 2.16558838e+02, 4.81009155e+02, 4.93061462e+02, 7.73522258e-01], [7.52775818e+02, 1.89528366e+02, 8.61289795e+02, 3.85257935e+02, 7.01904893e-01], [9.20946411e+02, 1.79072983e+02, 1.00360297e+03, 3.96296722e+02, 6.05505347e-01], [4.58280457e+02, 2.42123642e+02, 5.65374817e+02, 3.28342926e+02, 5.52386582e-01], [8.33291443e+02, 1.80540421e+02, 8.87493225e+02, 2.86587189e+02, 4.07754987e-01], [5.11583588e+02, 2.43700302e+02, 5.58589050e+02, 2.81153168e+02, 2.94458210e-01], [9.23730103e+02, 2.09271942e+02, 9.52381470e+02, 2.43316254e+02, 2.45495334e-01], [1.06479016e+03, 2.10522186e+02, 1.10781958e+03, 2.75908295e+02, 2.28151783e-01], [5.91670410e+02, 2.01050720e+02, 6.07173401e+02, 2.27956894e+02, 2.08628222e-01], [9.20510986e+02, 2.29461288e+02, 9.52803772e+02, 2.91918365e+02, 1.73453346e-01], [9.24910522e+02, 1.76875061e+02, 1.00202307e+03, 2.91921082e+02, 1.72886133e-01], [4.52924957e+02, 2.44312332e+02, 5.67311523e+02, 4.16092560e+02, 1.71487212e-01], [9.52959473e+02, 1.78773880e+02, 9.78253662e+02, 2.22386246e+02, 1.69336230e-01], [4.38190582e+02, 2.14866272e+02, 4.95324432e+02, 3.04000519e+02, 1.58925056e-01], [2.01884140e+02, 1.83056427e+02, 2.23812698e+02, 2.15974365e+02, 1.49776936e-01], [7.02421753e+02, 1.99203094e+02, 7.14362366e+02, 2.33888977e+02, 1.47609934e-01], [1.20856171e+02, 1.60006302e+02, 1.49419968e+02, 2.02216385e+02, 1.42384261e-01], [9.11389771e+02, 2.48144455e+02, 9.80736145e+02, 3.96026978e+02, 1.28656298e-01], [9.17928467e+02, 2.77136261e+02, 9.73367615e+02, 4.02519409e+02, 1.25267625e-01], [7.21496033e+02, 1.97844009e+02, 7.33020813e+02, 2.35363068e+02, 1.20238610e-01], [1.06275977e+03, 1.63221146e+02, 1.10420215e+03, 1.83143524e+02, 1.20213754e-01], [8.07593445e+02, 1.89808380e+02, 8.40500305e+02, 2.27944656e+02, 1.19432658e-01], [4.43526459e+02, 2.98785492e+02, 4.72608887e+02, 3.63207092e+02, 1.03526674e-01]], dtype=float32), 2: array([[5.8128436e+02, 3.2015591e+02, 6.1036902e+02, 4.2451218e+02, 1.4503880e-01], [5.8730334e+02, 3.5827512e+02, 6.0924414e+02, 4.2477673e+02, 1.0464772e-01]], dtype=float32), 3: array([[9.40922485e+02, 1.74940689e+02, 1.10419202e+03, 5.01262695e+02, 7.35163271e-01], [1.05245764e+03, 1.62014038e+02, 1.10320886e+03, 1.83770401e+02, 4.01580423e-01], [3.81964111e+02, 1.93149048e+02, 3.98207001e+02, 2.09870743e+02, 3.71277153e-01], [5.17155334e+02, 1.99897552e+02, 5.52378479e+02, 2.23080872e+02, 3.53017151e-01], [4.52498505e+02, 1.87626419e+02, 4.82792023e+02, 2.10607803e+02, 3.05006742e-01], [5.55579895e+02, 1.98171814e+02, 5.87705933e+02, 2.24355942e+02, 2.82967806e-01], [4.67379333e+02, 1.93787033e+02, 5.52076599e+02, 2.21718597e+02, 2.82801300e-01], [2.58814636e+02, 1.83658615e+02, 3.37679352e+02, 2.87522186e+02, 2.31444865e-01], [3.48811737e+02, 1.88092117e+02, 3.74281311e+02, 1.99832031e+02, 1.72601044e-01], [8.45242065e+02, 1.89480438e+02, 9.59937500e+02, 3.65349396e+02, 1.22586876e-01], [8.83041687e+02, 2.21784256e+02, 9.55514343e+02, 2.81642120e+02, 1.21336140e-01], [1.92001877e+02, 1.81369705e+02, 3.38588562e+02, 2.87826569e+02, 1.17726177e-01], [4.28679077e+02, 1.87498642e+02, 4.51157440e+02, 2.14194687e+02, 1.03312410e-01], [4.34342880e+01, 1.94555893e+02, 3.32047607e+02, 2.91121674e+02, 9.89589319e-02], [9.22172363e+02, 2.15691727e+02, 9.53443115e+02, 2.50752991e+02, 9.62707698e-02], [8.55037781e+02, 2.60071014e+02, 9.33192566e+02, 3.67721710e+02, 9.46588069e-02]], dtype=float32), 4: array([[7.7129700e+02, 2.8584515e+02, 8.5742883e+02, 4.2914856e+02, 4.6862921e-01], [9.1792847e+02, 2.7713626e+02, 9.7336761e+02, 4.0251941e+02, 3.1618059e-01], [8.7064313e+02, 1.8002090e+02, 1.0053579e+03, 3.9324136e+02, 1.9811258e-01], [8.9407764e+02, 3.3471994e+02, 9.2669031e+02, 3.7490765e+02, 1.6305232e-01], [5.9167041e+02, 2.0105072e+02, 6.0717340e+02, 2.2795689e+02, 1.3489471e-01], [8.5686792e+02, 2.8478622e+02, 9.3121967e+02, 3.7431851e+02, 1.3482100e-01], [8.7638965e+02, 2.4456686e+02, 9.2809430e+02, 2.9746774e+02, 1.2985437e-01], [7.5572211e+02, 2.8512769e+02, 7.9052789e+02, 3.9141678e+02, 1.2889129e-01], [8.5924463e+02, 3.3044025e+02, 8.9157703e+02, 3.6739783e+02, 1.0158612e-01], [8.3854456e+02, 1.7627057e+02, 8.9314648e+02, 3.7854059e+02, 9.6070871e-02], [7.5196356e+02, 2.4022119e+02, 7.8954590e+02, 2.9571701e+02, 9.5139056e-02], [8.5355298e+02, 2.1726012e+02, 8.9536554e+02, 2.8793890e+02, 8.8781744e-02]], dtype=float32), 5: array([], shape=(0, 5), dtype=float32), 6: array([[7.8750839e+01, 1.1956803e+02, 1.9045317e+02, 1.9728659e+02, 1.4013235e-01], [8.2164288e+02, 1.5676567e+02, 9.8106909e+02, 1.8659402e+02, 1.1654177e-01], [4.6091650e+02, 1.9312920e+02, 5.4321729e+02, 2.2068866e+02, 9.5691130e-02], [1.0524576e+03, 1.6201404e+02, 1.1032089e+03, 1.8377040e+02, 9.4557509e-02], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 9.3206726e-02]], dtype=float32), 7: array([], shape=(0, 5), dtype=float32), 8: array([[2.5881464e+02, 1.8365862e+02, 3.3767935e+02, 2.8752219e+02, 4.2487338e-01], [1.9200188e+02, 1.8136971e+02, 3.3858856e+02, 2.8782657e+02, 2.8179702e-01], [4.6737933e+02, 1.9378703e+02, 5.5207660e+02, 2.2171860e+02, 2.3831420e-01], [8.2164288e+02, 1.5676567e+02, 9.8106909e+02, 1.8659402e+02, 2.1636972e-01], [7.9597824e+01, 1.1954791e+02, 1.9006575e+02, 2.0557053e+02, 2.1429372e-01], [8.3380695e+02, 1.6128096e+02, 9.9013434e+02, 3.9514688e+02, 1.7470759e-01], [5.1715533e+02, 1.9989755e+02, 5.5237848e+02, 2.2308087e+02, 1.7455389e-01], [1.0524576e+03, 1.6201404e+02, 1.1032089e+03, 1.8377040e+02, 1.6903879e-01], [5.5557990e+02, 1.9817181e+02, 5.8770593e+02, 2.2435594e+02, 1.6713984e-01], [4.5249850e+02, 1.8762642e+02, 4.8279202e+02, 2.1060780e+02, 1.6448274e-01], [9.3006848e+02, 1.6615004e+02, 1.1051918e+03, 4.9725168e+02, 1.4698893e-01], [9.5911171e+01, 1.2150968e+02, 2.2566763e+02, 2.0651863e+02, 1.3853368e-01], [8.3998547e+02, 1.7599452e+02, 9.8150793e+02, 2.7579956e+02, 1.3346782e-01], [3.8404456e+02, 1.9263722e+02, 4.0538232e+02, 2.0957277e+02, 1.3329120e-01], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 1.1510180e-01]], dtype=float32), 9: array([], shape=(0, 5), dtype=float32), 10: array([[6.2332794e+02, 1.7543706e+02, 6.3640210e+02, 1.8499434e+02, 1.9979078e-01], [2.3029057e+01, 1.1899847e+02, 4.1986435e+01, 1.6111662e+02, 1.4616837e-01]], dtype=float32), 11: array([], shape=(0, 5), dtype=float32), 12: array([[2.3029057e+01, 1.1899847e+02, 4.1986435e+01, 1.6111662e+02, 1.2493925e-01]], dtype=float32), 13: array([], shape=(0, 5), dtype=float32), 14: array([[4.2238777e+01, 1.8917522e+02, 1.5893756e+02, 2.9668314e+02, 2.2145331e-01], [4.1385326e+01, 1.9171677e+02, 2.1256020e+02, 2.9425726e+02, 2.1918175e-01], [4.4437128e+02, 3.1972482e+02, 6.1312500e+02, 4.9902060e+02, 1.7449246e-01], [4.3434288e+01, 1.9455589e+02, 3.3204761e+02, 2.9112167e+02, 1.3006474e-01]], dtype=float32), 15: array([], shape=(0, 5), dtype=float32), 16: array([], shape=(0, 5), dtype=float32), 17: array([], shape=(0, 5), dtype=float32), 18: array([], shape=(0, 5), dtype=float32), 19: array([], shape=(0, 5), dtype=float32), 20: array([], shape=(0, 5), dtype=float32), 21: array([], shape=(0, 5), dtype=float32), 22: array([], shape=(0, 5), dtype=float32), 23: array([], shape=(0, 5), dtype=float32), 24: array([], shape=(0, 5), dtype=float32), 25: array([[9.3354675e+02, 2.4599426e+02, 9.7790936e+02, 2.9329950e+02, 1.6727217e-01]], dtype=float32), 26: array([[4.49576324e+02, 3.41546967e+02, 4.78700287e+02, 4.96103485e+02, 1.09754406e-01]], dtype=float32), 27: array([[9.3354675e+02, 2.4599426e+02, 9.7790936e+02, 2.9329950e+02, 1.8606143e-01], [1.8866473e+02, 2.9302808e+02, 2.0376270e+02, 3.3678482e+02, 1.1479716e-01], [1.8719382e+02, 2.5007120e+02, 2.0683739e+02, 3.2479196e+02, 9.8798744e-02]], dtype=float32), 28: array([[8.3329144e+02, 1.8054042e+02, 8.8749323e+02, 2.8658719e+02, 1.3669623e-01], [4.4361603e+02, 2.7395215e+02, 4.5814026e+02, 2.9931229e+02, 1.2056076e-01]], dtype=float32), 29: array([[4.6919995e+02, 3.1694785e+02, 6.0089233e+02, 4.1674185e+02, 1.3081582e-01]], dtype=float32), 30: array([], shape=(0, 5), dtype=float32), 31: array([], shape=(0, 5), dtype=float32), 32: array([], shape=(0, 5), dtype=float32), 33: array([], shape=(0, 5), dtype=float32), 34: array([], shape=(0, 5), dtype=float32), 35: array([], shape=(0, 5), dtype=float32), 36: array([], shape=(0, 5), dtype=float32), 37: array([], shape=(0, 5), dtype=float32), 38: array([], shape=(0, 5), dtype=float32), 39: array([], shape=(0, 5), dtype=float32), 40: array([[4.54680023e+02, 3.55583832e+02, 4.73403351e+02, 4.27880035e+02, 2.90563494e-01], [2.51120651e+02, 3.18786499e+02, 2.63868408e+02, 3.42476105e+02, 1.06872126e-01], [5.87303345e+02, 3.58275116e+02, 6.09244141e+02, 4.24776733e+02, 1.06069624e-01]], dtype=float32), 41: array([], shape=(0, 5), dtype=float32), 42: array([], shape=(0, 5), dtype=float32), 43: array([], shape=(0, 5), dtype=float32), 44: array([], shape=(0, 5), dtype=float32), 45: array([], shape=(0, 5), dtype=float32), 46: array([], shape=(0, 5), dtype=float32), 47: array([], shape=(0, 5), dtype=float32), 48: array([], shape=(0, 5), dtype=float32), 49: array([], shape=(0, 5), dtype=float32), 50: array([], shape=(0, 5), dtype=float32), 51: array([], shape=(0, 5), dtype=float32), 52: array([], shape=(0, 5), dtype=float32), 53: array([], shape=(0, 5), dtype=float32), 54: array([], shape=(0, 5), dtype=float32), 55: array([], shape=(0, 5), dtype=float32), 56: array([], shape=(0, 5), dtype=float32), 57: array([[4.4437128e+02, 3.1972482e+02, 6.1312500e+02, 4.9902060e+02, 5.4855621e-01], [4.4780740e+02, 4.0905780e+02, 6.0650165e+02, 4.9715942e+02, 1.2152367e-01], [5.0191052e+02, 4.1048935e+02, 6.0555505e+02, 4.8002225e+02, 1.0679674e-01]], dtype=float32), 58: array([[4.6612814e+02, 3.1615115e+02, 5.8993744e+02, 4.1722537e+02, 9.6560396e-02]], dtype=float32), 59: array([], shape=(0, 5), dtype=float32), 60: array([], shape=(0, 5), dtype=float32), 61: array([], shape=(0, 5), dtype=float32), 62: array([], shape=(0, 5), dtype=float32), 63: array([], shape=(0, 5), dtype=float32), 64: array([], shape=(0, 5), dtype=float32), 65: array([], shape=(0, 5), dtype=float32), 66: array([], shape=(0, 5), dtype=float32), 67: array([], shape=(0, 5), dtype=float32), 68: array([[4.8397363e+02, 2.5237894e+02, 5.0320410e+02, 2.8440067e+02, 1.8055500e-01], [4.4361603e+02, 2.7395215e+02, 4.5814026e+02, 2.9931229e+02, 1.0156604e-01], [4.5192215e+02, 2.5575865e+02, 4.8053168e+02, 3.0243512e+02, 9.4050229e-02]], dtype=float32), 69: array([], shape=(0, 5), dtype=float32), 70: array([], shape=(0, 5), dtype=float32), 71: array([], shape=(0, 5), dtype=float32), 72: array([], shape=(0, 5), dtype=float32), 73: array([], shape=(0, 5), dtype=float32), 74: array([[1.8886520e+02, 3.0652768e+02, 2.0260828e+02, 3.3425604e+02, 1.1647063e-01]], dtype=float32), 75: array([], shape=(0, 5), dtype=float32), 76: array([], shape=(0, 5), dtype=float32), 77: array([], shape=(0, 5), dtype=float32), 78: array([], shape=(0, 5), dtype=float32), 79: array([], shape=(0, 5), dtype=float32), 80: array([], shape=(0, 5), dtype=float32)}

This already gives you the output organized by categoryid.You can see it range from 1 to 80 and every category id has an array containing [x1,y1,x2,y2,score].For example,in your result,the category 1 has the array `1: array([[1.88266647e+02, 1.87754349e+02, 2.71509979e+02, 4.44029938e+02, 8.31451654e-01].They're the output you want.

xingyizhou / CenterNet

No output by runnig demo.py #500