如何使用plotly在google colab中返回交互式绘制矩形的坐标

Question

我是新来的。我想在图像上以交互方式绘制一些边界框并将它们的坐标放到一个列表中（左上角、右下角）。 这应该在 google colab 中完成，所以 CV2 不起作用。 link 给出了一个交互式选择（拖动和绘制）图像区域的示例，而 link 可用于使用 plotly 提取绘图上的坐标。我仍然无法弄清楚如何将这两个示例组合在一起并返回边界框坐标。我在下面添加了这些代码sn-ps。

在图像上绘制边界框：-

!wget https://gamingnewsanalyst.com/wp-content/uploads/2020/03/Crysis-3-Free-Download-800x450.jpg
import plotly.express as px
import cv2

img = cv2.cvtColor(cv2.imread('/content/Crysis-3-Free-Download-800x450.jpg'),cv2.COLOR_BGR2RGB)
fig = px.imshow(img)

fig.update_layout(
    dragmode='drawrect',
    newshape=dict(line_color='cyan'))
fig.show()

example image

获取鼠标点击点的坐标：-

import plotly.graph_objects as go
from google.colab import output
output.enable_custom_widget_manager()

import numpy as np
np.random.seed(1)

x = np.random.rand(100)
y = np.random.rand(100)



f = go.FigureWidget([go.Scatter(x=x, y=y, mode='markers')])

scatter = f.data[0]
colors = ['#a3a7e4'] * 100
scatter.marker.color = colors
scatter.marker.size = [10] * 100
f.layout.hovermode = 'closest'


# create our callback function
def update_point(trace, points, selector):
    c = list(scatter.marker.color)
    s = list(scatter.marker.size)
    for i in points.point_inds:
        c[i] = '#bae2be'
        s[i] = 20
        print(points)
        with f.batch_update():
            scatter.marker.color = c
            scatter.marker.size = s


scatter.on_click(update_point)

f

当我点击一个点时，此代码将在输出下方给出

Points(point_inds=[6],
       xs=[0.1862602113776709],
       ys=[0.015821242846556283],
       trace_name='trace 0',
       trace_index=0)

我的预期输出应该是绘制的边界框的坐标。

[[100,100],[500,400]]  ## [[x0,y0],[x1,y1]]
[[200,130],[400,300]]

非常感谢任何帮助。 提前致谢。

Answer 1

经过一番搜索，找到了这个example，它告诉我们如何做到这一点。我对其进行了修改并将其部分插入到一个类中，以满足我的全部要求。

import plotly.express as px
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from jupyter_dash import JupyterDash
import cv2
import numpy as np

coords = []
depths = []

app = JupyterDash(__name__)

@app.callback(
    Output("bbox-data", "children"),
    Input("middle-sai", "relayoutData"),
    prevent_initial_call=True,
)
def on_new_annotation(relayout_data):

    global coords,depths
    if "shapes" in relayout_data:
        x0 = int(relayout_data["shapes"][-1]['x0'])
        y0 = int(relayout_data["shapes"][-1]['y0'])
        x1 = int(relayout_data["shapes"][-1]['x1'])
        y1 = int(relayout_data["shapes"][-1]['y1'])
        coords_dict = {'x0':x0,'y0':y0,'x1':x1,'y1':y1}
        coords.append([[y0,x0],[y1,x1]])
        depths.append(None)
        return html.Div([html.Div(str(coords_dict)),html.Div(dcc.RadioItems(['shallow', 'deep'],inline=True,id = "depth-sel"))],style={"color": "white"})

    else:
        return dash.no_update

@app.callback(
    Output('all-bboxes','children'),
    Input("depth-sel","value"),
)
def display_final_list(depth_val):
        global coords,depths

        if (depth_val == 'deep'):
            depths[-1] = 1
        elif (depth_val == 'shallow'):
            depths[-1] = 0
        flist = ''
        for i in range(len(coords)):
            flist += str(coords[i])
            if (depths[i]==1):
                flist += " deep "
            elif (depths[i]== 0):
                flist += " shallow "
            else:
                flist += " " + str(depths[i]) + " "
        return flist

class test_show():

  def __init__(self):
    global coords,depths
    coords = []
    depths = []
    self.img = cv2.cvtColor(cv2.imread('/content/Crysis-3-Free-Download-800x450.jpg'),cv2.COLOR_BGR2RGB)
    fig = px.imshow(self.img)

    fig.update_layout(
        autosize=False,
        # width=500,
        height=400,
        dragmode='drawrect',
        newshape=dict(line_color='cyan'))

    app.layout = html.Div(
        [
            dcc.Graph(id="middle-sai", figure=fig),
            html.Pre(id="bbox-data",style={"color": "white"}),
            html.Div(id='all-bboxes',style={"color": "white"}),
        ]
    )


  def show_image(self):
      app.run_server(debug=False,mode='inline')

t = test_show()
t.show_image()