首页 fig = px.bar( get_data_forecast_is_stk_bs_rpt_zq_shuju, # 数据集 x='数据范围', # x轴 y='归母利润上一年比本年倍数') fig.show() 改成橙色

fig = px.bar( get_data_forecast_is_stk_bs_rpt_zq_shuju, # 数据集 x='数据范围', # x轴 y='归母利润上一年比本年倍数') fig.show() 改成橙色

时间: 2024-02-17 17:02:03 浏览: 106

colourhistogram.rar_CBIR.fig_DWT.fig_cbir_cbir dwt_feature band

Colourhistogram II. TEXTURE FEATURE EXTRACTION IN CBIR An overview of the proposed CBIR system is illustrated in Fig. 1. The proposed algorithm, Label Wavelet Transform (LWT), is based on color image segmentation [1], and it is an extension of DWT-based texture feature extraction method. The 2-D DWT is computed by applying separable filter banks to the gray level images. The detail images Dn,1, Dn,2, and Dn,3 are obtained by band-pass filtering in a specific direction, and they can be categorized into three frequency bands: HL, LH, HH band, respectively. Each band contains different directional information at scale n. The texture feature is extracted from the variance (ó2 n,i) of the coefficients cn,i of the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To represent the texture feature of an image q, the texture feature vector of DWT is defined as [2]: TDWT (q) = [ó2 1,1, ó2 1,2, ó2 1,3, ..., ó2N max,3], (1) where Nmax denotes the largest scale. In this work, Nmax

你可以使用`color_discrete_sequence`参数将所有的柱子都设置成橙色。下面是一个例子： ```python import plotly.express as px fig = px.bar(get_data_forecast_is_stk_bs_rpt_zq_shuju, x='数据范围', y='归母利润上一年比本年倍数', color_discrete_sequence=['orange']) fig.show() ``` 其中，`color_discrete_sequence=['orange']`将所有柱子的颜色都设置成橙色。你可以根据需要修改颜色值或使用其他颜色映射方案。

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