Error using tensorflow create_coco_tf_record script: "TypeError: string indices must be integers" - tensorflow
I want to use the create_coco_tf_record-script provided by tensorflow, to convert the following simple label definition in coco json format to TFRecord:
"info": {
"year": 2018,
"version": null,
"description": "TransferLearningTest",
"contributor": "ralph#r4robotics.com.au",
"url": "labelbox.io",
"date_created": "2018-03-25T08:30:27.427851+00:00"
},
"images": [{
"id": "cjf6gxqjw2fho01619gre5j0y",
"width": 615,
"height": 409,
"file_name": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles1.jpg?alt=media&token=b381c976-da30-49d7-8e95-eb4ae8588354",
"license": null,
"flickr_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles1.jpg?alt=media&token=b381c976-da30-49d7-8e95-eb4ae8588354",
"coco_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles1.jpg?alt=media&token=b381c976-da30-49d7-8e95-eb4ae8588354",
"date_captured": null
}, {
"id": "cjf6gyhtl55sv01385xtqjrqi",
"width": 259,
"height": 194,
"file_name": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles2.jpg?alt=media&token=9b274e2e-c541-4e80-8f3d-b198f3ba9b4d",
"license": null,
"flickr_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles2.jpg?alt=media&token=9b274e2e-c541-4e80-8f3d-b198f3ba9b4d",
"coco_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles2.jpg?alt=media&token=9b274e2e-c541-4e80-8f3d-b198f3ba9b4d",
"date_captured": null
}, {
"id": "cjf6gzj9v2g1h0161bwh18chv",
"width": 277,
"height": 182,
"file_name": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles3.jpg?alt=media&token=3cfc13ca-432d-4501-b574-00d3874a4682",
"license": null,
"flickr_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles3.jpg?alt=media&token=3cfc13ca-432d-4501-b574-00d3874a4682",
"coco_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles3.jpg?alt=media&token=3cfc13ca-432d-4501-b574-00d3874a4682",
"date_captured": null
}, {
"id": "cjf6h0p9n55wz0178pg79lc3c",
"width": 301,
"height": 167,
"file_name": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles4.jpg?alt=media&token=d2660bc4-d576-45f0-8de6-557270fc683d",
"license": null,
"flickr_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles4.jpg?alt=media&token=d2660bc4-d576-45f0-8de6-557270fc683d",
"coco_url": "https://firebasestorage.googleapis.com/v0/b/labelbox-193903.appspot.com/o/cjf6gtsr950sr0125idy65yiy%2Ff245e964-d756-4c01-98de-b6e5a9070588%2Fbottles4.jpg?alt=media&token=d2660bc4-d576-45f0-8de6-557270fc683d",
"date_captured": null
}],
"annotations": [{
"id": 1,
"image_id": "cjf6gxqjw2fho01619gre5j0y",
"category_id": 1,
"segmentation": [
[118.39765618513167, 313.457848898712, 179.7169976455091, 299.1734470204843, 294.6908226914901, 310.3222212573321, 337.1962867729657, 334.7101881586143, 366.4623832276035, 338.89097185223864, 372.03689297966736, 385.5765403887654, 332.31863061175864, 389.75732408238974, 282.84505592143563, 406.48051201843583, 215.9512047089942, 408.5708772844735, 192.2596180064203, 390.4541125044023, 151.8445552101198, 403.6933051688366, 105.1582353958287, 376.51813141795526, 118.39765618513167, 313.457848898712]
],
"area": 22106.876283900496,
"bbox": [105.1582353958287, 0.42912271552648545, 266.8786575838387, 109.39743026398922],
"iscrowd": 0
}, {
"id": 2,
"image_id": "cjf6gxqjw2fho01619gre5j0y",
"category_id": 1,
"segmentation": [
[160.20631983821562, 142.04523900617488, 195.04687288245222, 131.24488556110788, 308.62704390918475, 134.03209241070698, 356.01021731433246, 152.1488571907783, 381.7922053020817, 150.75522718520426, 384.57951334057844, 186.64038911511503, 349.7389071338769, 187.68559832890833, 317.6856089656722, 202.3183678373679, 159.50946624735892, 195.3503772941444, 160.20631983821562, 142.04523900617488]
],
"area": 13123.705213053147,
"bbox": [159.50946624735892, 206.6816321626321, 225.07004709321953, 71.07348227626002],
"iscrowd": 0
}, {
"id": 3,
"image_id": "cjf6gyhtl55sv01385xtqjrqi",
"category_id": 1,
"segmentation": [
[80.06035395893144, 68.18619344603749, 119.11342792196902, 74.69491256085784, 131.84812313721997, 72.14801308536389, 177.97602777539703, 78.09078572494903, 187.59778022105084, 91.67421361042045, 203.1624077063576, 93.37213939731716, 201.18146375358646, 112.04938782407424, 184.76784795099502, 111.200414135477, 169.20322046568833, 122.51994816851872, 128.16920254987957, 117.42614921753086, 114.86852951688535, 114.03029764373744, 93.07803808305403, 114.31328167650393, 70.43857992260781, 103.2767316762287, 80.06035395893144, 68.18619344603749]
],
"area": 4995.907009222967,
"bbox": [70.43857992260781, 71.48005183148128, 132.7238277837498, 54.33375472248123],
"iscrowd": 0
}, {
"id": 4,
"image_id": "cjf6gzj9v2g1h0161bwh18chv",
"category_id": 1,
"segmentation": [
[173.46162883883662, 160.28013107383993, 255.65715601241382, 148.2998138472238, 266.2728180897869, 177.11325728633884, 184.68389092165103, 182.8759506021435, 159.20627416758742, 180.1462513325615, 154.35340470313542, 170.74397441725296, 175.28142885516084, 167.7109803710303, 173.46162883883662, 160.28013107383993]
],
"area": 2509.1082874191734,
"bbox": [154.35340470313542, -0.8759506021434983, 111.91941338665146, 34.576136754919716],
"iscrowd": 0
}, {
"id": 5,
"image_id": "cjf6gzj9v2g1h0161bwh18chv",
"category_id": 1,
"segmentation": [
[37.58112185203197, 87.03332022958155, 45.16373762158412, 93.40262623857566, 94.90570169790779, 106.44448675338808, 106.73458692647054, 87.03332022958155, 46.680260775494574, 73.08155224493905, 40.31086815713212, 74.901344044691, 33.63817553604898, 74.901344044691, 27.875382923127926, 80.9673321371363, 37.58112185203197, 87.03332022958155]
],
"area": 1386.09176276128,
"bbox": [27.875382923127926, 75.55551324661192, 78.85920400334261, 33.36293450844903],
"iscrowd": 0
}, {
"id": 6,
"image_id": "cjf6gzj9v2g1h0161bwh18chv",
"category_id": 1,
"segmentation": [
[200.7590456092244, 136.92608617388885, 181.95412147624396, 120.09295996138994, 234.4258318576678, 85.2135284298296, 255.05055600697247, 103.71478748380605, 200.7590456092244, 136.92608617388885]
],
"area": 1614.301579806095,
"bbox": [181.95412147624396, 45.073913826111145, 73.09643453072852, 51.71255774405926],
"iscrowd": 0
}, {
"id": 7,
"image_id": "cjf6h0p9n55wz0178pg79lc3c",
"category_id": 1,
"segmentation": [
[17.847508506087518, 28.63952607163654, 66.60858665657888, 24.08859036914734, 77.98617155836023, 14.986669362689923, 145.27644948557162, 14.49906202292621, 147.5519565454611, 51.881911126804255, 75.0605019090974, 56.10780833710362, 64.0079859014193, 47.98110201017366, 24.3489855928197, 53.34473314609532, 17.847508506087518, 28.63952607163654]
],
"area": 4189.730491764894,
"bbox": [17.847508506087518, 110.89219166289638, 129.7044480393736, 41.60874631417741],
"iscrowd": 0
}, {
"id": 8,
"image_id": "cjf6h0p9n55wz0178pg79lc3c",
"category_id": 1,
"segmentation": [
[223.94433711573117, 23.27591973645434, 257.10186033759857, 27.82685543894354, 261.32783036444124, 48.306165303102944, 179.73427308501883, 104.86804629868364, 145.27644948557162, 113.3198159185429, 128.37261898053467, 122.42173692500033, 111.46876367433086, 108.76885541531423, 131.29826382863067, 96.09118858515549, 137.14960312715638, 77.56230808005091, 223.94433711573117, 23.27591973645434]
],
"area": 6031.236484118768,
"bbox": [111.46876367433086, 44.57826307499967, 149.85906669011038, 99.14581718854599],
"iscrowd": 0
}, {
"id": 9,
"image_id": "cjf6h0p9n55wz0178pg79lc3c",
"category_id": 1,
"segmentation": [
[26.299423758605975, 125.34733136210352, 40.60267830965016, 111.53193060632253, 117.97024076106304, 72.6862842838929, 133.57379568968702, 80.81299061082292, 132.59856420621048, 93.16559414805232, 111.46876367433086, 115.2702204768582, 64.33305479552251, 138.67514957886033, 46.128923912905776, 139.65033945764822, 23.37375410934314, 148.75226046410563, 8.095292875989244, 141.11316147693933, 26.299423758605975, 125.34733136210352]
],
"area": 3857.6591542480846,
"bbox": [8.095292875989244, 18.24773953589436, 125.47850281369777, 76.06597618021274],
"iscrowd": 0
}],
"licenses": [],
"categories": [{
"supercategory": "Bottle",
"id": 1,
"name": "Bottle"
}]
}
But when I run the script using
with open('coco_labels.json') as json_data:
label_info = json.load(json_data)
IMAGE_FOLDER = "coco_images"
with tf.python_io.TFRecordWriter("training.record") as writer:
for i,image in enumerate(label_info["images"]):
img_data = requests.get(image["file_name"]).content
image_name = "image"+str(i)+".jpg"
image_path = os.path.join(IMAGE_FOLDER,image_name)
with open(image_path, 'wb') as handler:
handler.write(img_data)
image["file_name"] = image_name
tf_example = create_coco_tf_record.create_tf_example(image,
label_info["annotations"][i],
IMAGE_FOLDER,
label_info["categories"]
)
writer.write(tf_example.SerializeToString())
I get the error
(image, annotations_list, image_dir, category_index, include_masks)
124 num_annotations_skipped = 0
125 for object_annotations in annotations_list:
--> 126 (x, y, width, height) = tuple(object_annotations['bbox'])
127 if width <= 0 or height <= 0:
128 num_annotations_skipped += 1
TypeError: string indices must be integers
What could be the problem?
Each image is supposed to receive a list of annotations, and you are providing a single one. Making it a single element list should solve your error.
Ideally, make each item of images in your json be a list itself. As a quick fix, embrace label_info["annotations"][i] in brackets:
[label_info["annotations"][i]]
Related
Plotly Animation with slider
I want to add two moving points represent the location of two trains according to the day. My day data is as shown in pic starting from 0 to 7. However, in the resulting animation, the slider does not slide into the integer day. It jumped from 1.75 to 2.25 or 2.75 to 3.25 automatically. Can anyone help me to solve that?
trainpath info
import plotly.graph_objects as go
import pandas as pd
dataset = pd.read_csv('trainpath.csv')
days = []
for k in range(len(dataset['day'])):
if dataset['day'][k] not in days:
days.append(dataset['day'][k])
t1 = [-1, 0, 1, 1, 1, 0, -1, -1, -1]
k1 = [-20, -20, -20, 0, 20, 20, 20, 0, -20]
# make list of trains
trains = []
for train in dataset["train"]:
if train not in trains:
trains.append(train)
# make figure
fig_dict = {
"data": [go.Scatter(x=t1, y=k1,
mode="lines",
line=dict(width=2, color="blue")),
go.Scatter(x=t1, y=k1,
mode="lines",
line=dict(width=2, color="blue"))],
"layout": {},
"frames": []
}
# fill in most of layout
fig_dict['layout']['title'] = {'text':'Train Animation'}
fig_dict["layout"]["xaxis"] = {"range": [-10, 10], "title": "xlocation", 'autorange':False, 'zeroline':False}
fig_dict["layout"]["yaxis"] = {"range": [-22, 22], "title": "ylocation", 'autorange':False, 'zeroline':False}
fig_dict["layout"]["hovermode"] = "closest"
fig_dict["layout"]["updatemenus"] = [
{
"buttons": [
{
"args": [None, {"frame": {"duration": 500, "redraw": False},
"fromcurrent": True, "transition": {"duration": 300,
"easing": "quadratic-in-out"}}],
"label": "Play",
"method": "animate"
},
{
"args": [[None], {"frame": {"duration": 0, "redraw": False},
"mode": "immediate",
"transition": {"duration": 0}}],
"label": "Pause",
"method": "animate"
}
],
"direction": "left",
"pad": {"r": 10, "t": 87},
"showactive": False,
"type": "buttons",
"x": 0.1,
"xanchor": "right",
"y": 0,
"yanchor": "top"
}
]
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {"size": 20},
"prefix": "Day:",
"visible": True,
"xanchor": "right"
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": []
}
# make data
day = 0
for train in trains:
dataset_by_date = dataset[dataset['day']==day]
dataset_by_date_and_train = dataset_by_date[dataset_by_date['train']==train]
data_dict = {
'x': list(dataset_by_date_and_train['x']),
'y': list(dataset_by_date_and_train['y']),
'mode': 'markers',
'text': train,
'marker': {
'sizemode': 'area',
'sizeref': 20,
'size': 20,
# 'size': list(dataset_by_date_and_train['quantity']) # this section can be used to increase or decrease the marker size to reflect the material quantity
},
'name': train
}
fig_dict['data'].append(data_dict)
# make frames
for day in days:
frame={'data': [go.Scatter(x=t1, y=k1,
mode="lines",
line=dict(width=2, color="blue")),
go.Scatter(x=t1, y=k1,
mode="lines",
line=dict(width=2, color="blue"))], 'name':str(day)}
for train in trains:
dataset_by_date = dataset[dataset['day'] == day]
dataset_by_date_and_train = dataset_by_date[dataset_by_date['train'] == train]
data_dict = {
'x': list(dataset_by_date_and_train['x']),
'y': list(dataset_by_date_and_train['y']),
'mode': 'markers',
'text': train,
'marker': {
'sizemode': 'area',
'sizeref': 20,
'size': 20,
# 'size': list(dataset_by_date_and_train['quantity']) # this section can be used to increase or decrease the marker size to reflect the material quantity
},
'name': train
}
frame['data'].append(data_dict)
fig_dict['frames'].append(frame)
slider_step = {'args': [
[day],
{'frame': {'duration':300, 'redraw':False},
'mode': 'immediate',
'transition': {'duration':3000}}
],
'label': day,
'method': 'animate'}
sliders_dict["steps"].append(slider_step)
if day == 7:
print('H')
fig_dict["layout"]["sliders"] = [sliders_dict]
fig = go.Figure(fig_dict)
fig.show()
prevent text from overlapping data points and other text
I'm trying to find an intelligent solution to how text / annotations are placed into a matplotlib plt so they don't over lap with the data point being annotated. Code snip below. Apologies for long dict at the top. So far I've found adjustText which looks very promising, but I can't seem to get it working in this instance. The code below uses adjust_text(), but at the moment all text is being placed together in one part of the ax and I don't understand why. If you run without adjust_text() it places text roughly where it should be, but text is overlapping the data point in places, which I want to avoid. Grateful for any help.
fig, ax = plt.subplots(figsize=(10, 8))
dl_data = {
"Center": {
"axis": (0, 0),
"tp": (0, 0),
"r": 21.37311395187889,
"colour": "#ffffff",
"text": "Center",
"fill": "solid",
"ec": "#808080",
"alignment": ("center", "center"),
},
"First": {
"r": 6.758772077825972,
"wlc": 45.681000000000004,
"text": "First",
"colour": "#FFFFFF",
"fill": "dashed",
"ec": "#808080",
"alignment": ("center", "center"),
"axis": (-68.82111180215705, -1.2642233142341064e-14),
"tp": (-68.82111180215705, -1.2642233142341064e-14),
},
"Second": {
"r": 18.979199140111263,
"wlc": 360.21000000000004,
"text": "Second",
"colour": "#FFFFFF",
"fill": "dashed",
"ec": "#808080",
"alignment": ("center", "center"),
"axis": (-34.41055590107855, 59.600831137357034),
"tp": (-34.41055590107855, 59.600831137357034),
},
"P1": {
"r": 4.779173568725037,
"wlc": 2.6,
"colour": "#92a700",
"text": "P1, £3",
"fill": "solid",
"ec": "#92a700",
"axis": (-80.83697480558055, -1.4849511367261418e-14),
"alignment": ("right", "top"),
"tp": (-87.6161483743056, -1.6094825349031936e-14),
},
"P2": {
"r": 4.779173568725037,
"wlc": 0,
"colour": "#ffba00",
"text": "P2 has a long\nName, £0\n\n",
"fill": "solid",
"ec": "#ffba00",
"axis": (-13.031791598544089, 30.17548646933409),
"alignment": ("left", "top"),
"tp": (-9.047093352116576, 24.691019844418072),
},
"P3": {
"r": 4.779173568725037,
"wlc": 0.21,
"colour": "#92a700",
"text": "P3 has a very,\nlong long long,\nname, £0 \n",
"fill": "solid",
"ec": "#92a700",
"axis": (-55.78932020361301, 30.175486469334082),
"alignment": ("right", "top"),
"tp": (-59.77401845004052, 24.691019844418065),
},
"P4": {
"r": 15.811388300841896,
"wlc": 250,
"colour": "#e77200",
"text": "P4 also\nhas a longish\nname, £250\n",
"fill": "solid",
"ec": "#e77200",
"axis": (-34.41055590107855, 95.97255740839438),
"alignment": ("center", "center"),
"tp": (-34.41055590107855, 113.78394570923628),
},
"P5": {
"r": 4.779173568725037,
"wlc": 6.6,
"colour": "#92a700",
"text": "P5 is medium,\n£7\n\n",
"fill": "solid",
"ec": "#92a700",
"axis": (-69.00212318005225, 70.8403126698613),
"alignment": ("right", "top"),
"tp": (-75.44950037768407, 72.9351925104148),
},
"P6": {
"r": 10.16857905510893,
"wlc": 103.4,
"colour": "#92a700",
"text": "P6 is a very long name\nlike P4 is also,\n£100\n",
"fill": "solid",
"ec": "#92a700",
"axis": (0.181011377895139, 70.8403126698613),
"alignment": ("left", "top"),
"tp": (11.754017782309209, 74.600610395285),
},
}
ts = []
x_list = []
y_list = []
for c in dl_data.keys():
circle = plt.Circle(
dl_data[c]["axis"], # x, y position
radius=dl_data[c]["r"],
fc=dl_data[c]["colour"], # face colour
ec=dl_data[c]["ec"], # edge colour
zorder=2,
)
ax.add_patch(circle)
x = dl_data[c]["axis"][0]
y = dl_data[c]["axis"][1]
text = dl_data[c]["text"]
if c in ["Center", "First", "Second"]:
pass
else:
ts.append(ax.text(x, y, dl_data[c]["text"]))
x_list.append(x)
y_list.append(y)
adjust_text(
ts,
x=x_list,
y=y_list,
force_points=0.1,
arrowprops=dict(arrowstyle="->", color="red"),
)
plt.axis("scaled")
plt.axis("off")
plt.show()
There are two issues:
adjust_text must called after all drawing is completed, i.e. plt.axis("scaled") must come before adjust_text, see docs:
Call adjust_text the very last, after all plotting (especially
anything that can change the axes limits) has been done.
You must pass your circles as additional objects to be avoided: add_objects=objects
ts = []
x_list = []
y_list = []
objects = []
for c in dl_data.keys():
circle = plt.Circle(
dl_data[c]["axis"], # x, y position
radius=dl_data[c]["r"],
fc=dl_data[c]["colour"], # face colour
ec=dl_data[c]["ec"], # edge colour
zorder=2,
)
objects.append(circle)
ax.add_patch(circle)
x = dl_data[c]["axis"][0]
y = dl_data[c]["axis"][1]
text = dl_data[c]["text"]
if c in ["Center", "First", "Second"]:
pass
else:
ts.append(ax.text(x, y, dl_data[c]["text"].strip()))
x_list.append(x)
y_list.append(y)
plt.axis("scaled")
plt.axis("off")
adjust_text(
ts,
add_objects=objects,
arrowprops=dict(arrowstyle="->", color="red"),
)
I couldn't manage to move the P6 text away from the green and orange circles, though.
Create a ui5-tree-table dynamically with a dynamic number of columns depending on the model
Is there a way to create a treetable dynamically in a way to have a dynamic number of columns and rows? Lets say the attributes in the model are called 'ColX' where X stands for a counter. For every such attribute I want to create a column.
An example for a model that I found is working with a treetable looks like and would produce 3 columns and 2 first level rows:
{
"Nodes": [
{
"NodeID": 1,
"HierarchyLevel": 0,
"Description": "Level1Node1",
"ParentNodeID": null,
"DrillState": "expanded",
"Col1": "col1-val1",
"Col2": "col1-val2",
"Col3": "col1-val3",
"data": [
{
"NodeID": 11,
"HierarchyLevel": 2,
"Description": "11",
"ParentNodeID": 1,
"DrillState": "leaf",
"Col1": "col11-val1",
"Col2": "col11-val2",
"Col3": "col11-val3"
},
{
"NodeID": 12,
"HierarchyLevel": 2,
"Description": "12",
"ParentNodeID": 1,
"DrillState": "leaf",
"Col1": "col12-val1",
"Col2": "col12-val2",
"Col3": "col12-val3"
},
{
"NodeID": 16,
"HierarchyLevel": 2,
"Description": "1x",
"ParentNodeID": 1,
"DrillState": "leaf",
"Col1": "col13-val1",
"Col2": "col13-val2",
"Col3": "col13-val3"
}
]
},
{
"NodeID": 2,
"HierarchyLevel": 0,
"Description": "Level1Node2",
"ParentNodeID": null,
"DrillState": "expanded",
"Col1": "col2-val1",
"Col2": "col2-val2",
"Col3": "col2-val3",
"data": [
{
"NodeID": 17,
"HierarchyLevel": 2,
"Description": "21",
"ParentNodeID": 2,
"DrillState": "leaf",
"Col1": "col21-val1",
"Col2": "col21-val2",
"Col3": "col21-val3"
}
]
}
]
}
Kusto Query JSON Array Values
can anyone offer a clue on how to do query values within arrays -- such as below, I want to find all records where
DiscoveredInformationTypes_s Confidence > 80
Can anyone help? How do I query inside this array?
MachineName_s
Version_s
ProcessName_s
ApplicationName_s
Operation_s
ObjectId_s
DiscoveredInformationTypes_s
[ { "Confidence": 55, "Count": 1, "SensitiveType": "3356946c-6bb7-449b-b253-6ffa419c0ce7", "UniqueCount": 1, "SensitiveInformationDetections": null, "Name": "International Classification of Diseases (ICD-10-CM)" } ]
you can use mv-apply: https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/mv-applyoperator
for example:
datatable(DiscoveredInformationTypes_s:dynamic)
[
dynamic([ { "Confidence": 55, "Count": 1, "SensitiveType": "3356946c-6bb7-449b-b253-6ffa419c0ce7", "UniqueCount": 1, "SensitiveInformationDetections": null, "Name": "International Classification of Diseases (ICD-10-CM)" } ]),
dynamic([ { "Confidence": 81, "Count": 1, "SensitiveType": "3356946c-6bb7-449b-b253-6ffa419c0ce7", "UniqueCount": 1, "SensitiveInformationDetections": null, "Name": "International Classification of Diseases (ICD-10-CM)" } ])
]
| mv-apply DiscoveredInformationTypes_s on (
where DiscoveredInformationTypes_s.Confidence > 80
)
or, if your column is string-typed and not dynamic-typed, you'll need to invoke parse_json() on it first:
datatable(s:string)
[
'[ { "Confidence": 55, "Count": 1, "SensitiveType": "3356946c-6bb7-449b-b253-6ffa419c0ce7", "UniqueCount": 1, "SensitiveInformationDetections": null, "Name": "International Classification of Diseases (ICD-10-CM)" } ]',
'[ { "Confidence": 81, "Count": 1, "SensitiveType": "3356946c-6bb7-449b-b253-6ffa419c0ce7", "UniqueCount": 1, "SensitiveInformationDetections": null, "Name": "International Classification of Diseases (ICD-10-CM)" } ]'
]
| project DiscoveredInformationTypes_s = parse_json(s)
| mv-apply DiscoveredInformationTypes_s on (
where DiscoveredInformationTypes_s.Confidence > 80
)
tf.dataSync() does not return tensor from BlazeFaceModel in a readable form
I am using BlazeFaceModel to detect faces before sending the faces to another model using Tensorflow.js
When I am using a custom model and trying to get the tensor output I used the code below and it worked at returning the tensors.
const returnTensors = true;
const faces = await blazeModel.estimateFaces(tensor, returnTensors);
if (faces !== null) {
// Download the tensors to view the shape
const face = faces.dataSync();
face.forEach((pred, i) => {
console.log(`x: ${i}, pred: ${pred}`);
});
}
But it throws the following error when applying on the tensor output from BlazeFaceModel:
faces.dataSync is not a function. (In 'faces.dataSync()', 'faces.dataSync' is undefined)
Output from console.log(faces)
Array [
Object {
"bottomRight": Tensor {
"dataId": Object {},
"dtype": "float32",
"id": 60793,
"isDisposedInternal": false,
"kept": false,
"rankType": "1",
"scopeId": 116528,
"shape": Array [
2,
],
"size": 2,
"strides": Array [],
},
"landmarks": Tensor {
"dataId": Object {},
"dtype": "float32",
"id": 60795,
"isDisposedInternal": false,
"kept": false,
"rankType": "2",
"scopeId": 116532,
"shape": Array [
6,
2,
],
"size": 12,
"strides": Array [
2,
],
},
"probability": Tensor {
"dataId": Object {},
"dtype": "float32",
"id": 60785,
"isDisposedInternal": false,
"kept": false,
"rankType": "1",
"scopeId": 116495,
"shape": Array [
1,
],
"size": 1,
"strides": Array [],
},
"topLeft": Tensor {
"dataId": Object {},
"dtype": "float32",
"id": 60792,
"isDisposedInternal": false,
"kept": false,
"rankType": "1",
"scopeId": 116526,
"shape": Array [
2,
],
"size": 2,
"strides": Array [],
},
},
]
faces is not a tensor. It is an array of json with key values where the values are tensor. If you would like to get all the tensors at once in an array, Object.values(faces[0]) can be used tensors = Object.values(faces[0]) // array of tensor tensors.map(t => t.dataSync()) // download the value of the tensor to a js array // alternatively they can all be converted to a big tensor before using only once dataSync()