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######################### [{'id': 1, 'name': 'new'}] ######################### {1: {'id': 1, 'name': 'new'}}
Traceback (most recent call last):
File "ncs_detection.py", line 173, in
count_for_video_ncs(img_dir, start_index, end_index)
File "ncs_detection.py", line 137, in count_for_video_ncs
predictions = predict(image_np, detection_graph)
File "ncs_detection.py", line 30, in predict
for box_index in range(num_valid_boxes):
TypeError: 'numpy.float16' object cannot be interpreted as an integer
pi@JBLPI0001:~/workspace/ncs_detection $
The text was updated successfully, but these errors were encountered:
pi@JBLPI0001:/workspace/test/ncs_detection $ python3 ncs_detection.py
mkdir: cannot create directory ‘r10_tmp’: File exists
rm: cannot remove 'r10_tmp/*': No such file or directory
[INFO] finding NCS devices...
[INFO] found 1 devices. device0 will be used. opening device0...
[INFO] loading the graph file into RPi memory...
[INFO] allocating the graph on the NCS...
Traceback (most recent call last):
File "ncs_detection.py", line 169, in
count_for_video_ncs(img_dir, start_index, end_index)
File "ncs_detection.py", line 134, in count_for_video_ncs
predictions = predict(image_np, detection_graph)
File "ncs_detection.py", line 30, in predict
for box_index in range(num_valid_boxes):
TypeError: 'numpy.float16' object cannot be interpreted as an integer
pi@JBLPI0001:/workspace/test/ncs_detection $
运行识别时出错了:
pi@JBLPI0001:~/workspace/ncs_detection $ python3 ncs_detection.py
mkdir: cannot create directory ‘r10_tmp’: File exists
rm: cannot remove 'r10_tmp/*': No such file or directory
[INFO] finding NCS devices...
[INFO] found 1 devices. device0 will be used. opening device0...
[INFO] loading the graph file into RPi memory...
[INFO] allocating the graph on the NCS...
data/mscoco_label_map.pbtxt 1
######################### item {
name: "/m/01g317"
id: 1
display_name: "new"
}
item {
name: "/m/0199g"
id: 2
display_name: "bicycle"
}
item {
name: "/m/0k4j"
id: 3
display_name: "car"
}
item {
name: "/m/04_sv"
id: 4
display_name: "motorcycle"
}
item {
name: "/m/05czz6l"
id: 5
display_name: "airplane"
}
item {
name: "/m/01bjv"
id: 6
display_name: "bus"
}
item {
name: "/m/07jdr"
id: 7
display_name: "train"
}
item {
name: "/m/07r04"
id: 8
display_name: "truck"
}
item {
name: "/m/019jd"
id: 9
display_name: "boat"
}
item {
name: "/m/015qff"
id: 10
display_name: "traffic light"
}
item {
name: "/m/01pns0"
id: 11
display_name: "fire hydrant"
}
item {
name: "/m/02pv19"
id: 13
display_name: "stop sign"
}
item {
name: "/m/015qbp"
id: 14
display_name: "parking meter"
}
item {
name: "/m/0cvnqh"
id: 15
display_name: "bench"
}
item {
name: "/m/015p6"
id: 16
display_name: "bird"
}
item {
name: "/m/01yrx"
id: 17
display_name: "cat"
}
item {
name: "/m/0bt9lr"
id: 18
display_name: "dog"
}
item {
name: "/m/03k3r"
id: 19
display_name: "horse"
}
item {
name: "/m/07bgp"
id: 20
display_name: "sheep"
}
item {
name: "/m/01xq0k1"
id: 21
display_name: "cow"
}
item {
name: "/m/0bwd_0j"
id: 22
display_name: "elephant"
}
item {
name: "/m/01dws"
id: 23
display_name: "bear"
}
item {
name: "/m/0898b"
id: 24
display_name: "zebra"
}
item {
name: "/m/03bk1"
id: 25
display_name: "giraffe"
}
item {
name: "/m/01940j"
id: 27
display_name: "backpack"
}
item {
name: "/m/0hnnb"
id: 28
display_name: "umbrella"
}
item {
name: "/m/080hkjn"
id: 31
display_name: "handbag"
}
item {
name: "/m/01rkbr"
id: 32
display_name: "tie"
}
item {
name: "/m/01s55n"
id: 33
display_name: "suitcase"
}
item {
name: "/m/02wmf"
id: 34
display_name: "frisbee"
}
item {
name: "/m/071p9"
id: 35
display_name: "skis"
}
item {
name: "/m/06__v"
id: 36
display_name: "snowboard"
}
item {
name: "/m/018xm"
id: 37
display_name: "sports ball"
}
item {
name: "/m/02zt3"
id: 38
display_name: "kite"
}
item {
name: "/m/03g8mr"
id: 39
display_name: "baseball bat"
}
item {
name: "/m/03grzl"
id: 40
display_name: "baseball glove"
}
item {
name: "/m/06_fw"
id: 41
display_name: "skateboard"
}
item {
name: "/m/019w40"
id: 42
display_name: "surfboard"
}
item {
name: "/m/0dv9c"
id: 43
display_name: "tennis racket"
}
item {
name: "/m/04dr76w"
id: 44
display_name: "bottle"
}
item {
name: "/m/09tvcd"
id: 46
display_name: "wine glass"
}
item {
name: "/m/08gqpm"
id: 47
display_name: "cup"
}
item {
name: "/m/0dt3t"
id: 48
display_name: "fork"
}
item {
name: "/m/04ctx"
id: 49
display_name: "knife"
}
item {
name: "/m/0cmx8"
id: 50
display_name: "spoon"
}
item {
name: "/m/04kkgm"
id: 51
display_name: "bowl"
}
item {
name: "/m/09qck"
id: 52
display_name: "banana"
}
item {
name: "/m/014j1m"
id: 53
display_name: "apple"
}
item {
name: "/m/0l515"
id: 54
display_name: "sandwich"
}
item {
name: "/m/0cyhj_"
id: 55
display_name: "orange"
}
item {
name: "/m/0hkxq"
id: 56
display_name: "broccoli"
}
item {
name: "/m/0fj52s"
id: 57
display_name: "carrot"
}
item {
name: "/m/01b9xk"
id: 58
display_name: "hot dog"
}
item {
name: "/m/0663v"
id: 59
display_name: "pizza"
}
item {
name: "/m/0jy4k"
id: 60
display_name: "donut"
}
item {
name: "/m/0fszt"
id: 61
display_name: "cake"
}
item {
name: "/m/01mzpv"
id: 62
display_name: "chair"
}
item {
name: "/m/02crq1"
id: 63
display_name: "couch"
}
item {
name: "/m/03fp41"
id: 64
display_name: "potted plant"
}
item {
name: "/m/03ssj5"
id: 65
display_name: "bed"
}
item {
name: "/m/04bcr3"
id: 67
display_name: "dining table"
}
item {
name: "/m/09g1w"
id: 70
display_name: "toilet"
}
item {
name: "/m/07c52"
id: 72
display_name: "tv"
}
item {
name: "/m/01c648"
id: 73
display_name: "laptop"
}
item {
name: "/m/020lf"
id: 74
display_name: "mouse"
}
item {
name: "/m/0qjjc"
id: 75
display_name: "remote"
}
item {
name: "/m/01m2v"
id: 76
display_name: "keyboard"
}
item {
name: "/m/050k8"
id: 77
display_name: "cell phone"
}
item {
name: "/m/0fx9l"
id: 78
display_name: "microwave"
}
item {
name: "/m/029bxz"
id: 79
display_name: "oven"
}
item {
name: "/m/01k6s3"
id: 80
display_name: "toaster"
}
item {
name: "/m/0130jx"
id: 81
display_name: "sink"
}
item {
name: "/m/040b_t"
id: 82
display_name: "refrigerator"
}
item {
name: "/m/0bt_c3"
id: 84
display_name: "book"
}
item {
name: "/m/01x3z"
id: 85
display_name: "clock"
}
item {
name: "/m/02s195"
id: 86
display_name: "vase"
}
item {
name: "/m/01lsmm"
id: 87
display_name: "scissors"
}
item {
name: "/m/0kmg4"
id: 88
display_name: "teddy bear"
}
item {
name: "/m/03wvsk"
id: 89
display_name: "hair drier"
}
item {
name: "/m/012xff"
id: 90
display_name: "toothbrush"
}
######################### [{'id': 1, 'name': 'new'}] ######################### {1: {'id': 1, 'name': 'new'}}
Traceback (most recent call last):
File "ncs_detection.py", line 173, in
count_for_video_ncs(img_dir, start_index, end_index)
File "ncs_detection.py", line 137, in count_for_video_ncs
predictions = predict(image_np, detection_graph)
File "ncs_detection.py", line 30, in predict
for box_index in range(num_valid_boxes):
TypeError: 'numpy.float16' object cannot be interpreted as an integer
pi@JBLPI0001:~/workspace/ncs_detection $
The text was updated successfully, but these errors were encountered: