prevent text from overlapping data points and other text - matplotlib

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.

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()

Combining separate temporal measurement series

I have a data set that combines two temporal measurement series with one row per measurement
time: 1, measurement: a, value: 5
time: 2, measurement: b, value: false
time: 10, measurement: a, value: 2
time: 13, measurement: b, value: true
time: 20, measurement: a, value: 4
time: 24, measurement: b, value: true
time: 30, measurement: a, value: 6
time: 32, measurement: b, value: false
in a visualization using Vega lite, I'd like to combine the measurement series and encode measurement a and b in a single visualization without simply layering their representation on a temporal axis but representing their value in a single encoding spec.
either measurement a values need to be interpolated and added as a new value to rows of measurement b
eg:
time: 2, measurement: b, value: false, interpolatedMeasurementA: 4.6667
or the other way around, which leaves the question of how to interpolate a boolean. maybe closest value by time, or simpler: last value
eg:
time: 30, measurement: a, value: 6, lastValueMeasurementB: true
I suppose this could be done either query side in which case this question would be regarding indexDB Flux query language
or this could be done on the visualization side in which case this would be regarding vega-lite
There's not any true linear interpolation schemes built-in to Vega-Lite (though the loess transform comes close), but you can achieve roughly what you wish with a window transform.
Here is an example (view in editor):
{
"data": {
"values": [
{"time": 1, "measurement": "a", "value": 5},
{"time": 2, "measurement": "b", "value": false},
{"time": 10, "measurement": "a", "value": 2},
{"time": 13, "measurement": "b", "value": true},
{"time": 20, "measurement": "a", "value": 4},
{"time": 24, "measurement": "b", "value": true},
{"time": 30, "measurement": "a", "value": 6},
{"time": 32, "measurement": "b", "value": false}
]
},
"transform": [
{
"calculate": "datum.measurement == 'a' ? datum.value : null",
"as": "measurement_a"
},
{
"window": [
{"op": "mean", "field": "measurement_a", "as": "interpolated"}
],
"sort": [{"field": "time"}],
"frame": [1, 1]
},
{"filter": "datum.measurement == 'b'"}
],
"mark": "line",
"encoding": {
"x": {"field": "time"},
"y": {"field": "interpolated"},
"color": {"field": "value"}
}
}
This first uses a calculate transform to isolate the values to be interpolated, then a window transform that computes the mean over adjacent values (frame: [1, 1]), then a filter transform to isolate interpolated rows.
If you wanted to go the other route, you could do a similar sequence of transforms targeting the boolean value instead.

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()

Error using tensorflow create_coco_tf_record script: "TypeError: string indices must be integers"

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]]

C3JS Acces value shown on X axis

I have simple bar chart like this:
Here is my C3JS
var chart = c3.generate({
data: {
json:[{"A": 67, "B": 10, "site": "Google", "C": 12}, {"A": 10, "B": 20, "site": "Amazon", "C": 12}, {"A": 25, "B": 10, "site": "Stackoverflow", "C": 8}, {"A": 20, "B": 22, "site": "Yahoo", "C": 12}, {"A": 76, "B": 30, "site": "eBay", "C": 9}],
mimeType: 'json',
keys: {
x: 'site',
value: ['A','B','C']
},
type: 'bar',
selection: {
enabled: true
},
onselected: function(d,element)
{
alert('selected x: '+chart.selected()[0].x+' value: '+chart.selected()[0].value+' name: '+chart.selected()[0].name);
},
groups: [
['A','B','C']
]
},
axis: {
x: {
type: 'category'
}
}
});
After some chart elemnt is selected (clicked), alert shows X and Value and Name attributes of first selected element. For example "selected x: 0 value: 67 name: A" after I click on left-top chart element. How can I get value shown on X axis? In this case it is "Google".
Property categories is populated when the x-axis is declared to be of type category as it is in this case. So to get the data from the x-axis you needs to call the .categories() function.
onselected: function(d,element){alert(chart.categories()[d.index]);}
https://jsfiddle.net/4bos2qzx/1/