Python 如何使用FILLCONVERXPOLY从所有轮廓制作遮罩

我想用轮廓做个面具。

这里是前7排的一瞥

>>> df
      contour.ID        xrT        yrT        xlT        ylT
1057          20  6259.2300  4620.7845  5670.1260  4651.4670
1058          20  6253.0935  4620.7845  5682.3990  4651.4670
1059          20  6253.0935  4633.0575  5694.6720  4657.6035
1060          20  6240.8205  4633.0575  5694.6720  4657.6035
1061          20  6228.5475  4645.3305  5700.8085  4669.8765
1062          20  6228.5475  4645.3305  5700.8085  4669.8765
1063          20  6216.2745  4645.3305  5713.0815  4669.8765

我可以用一个函数画出我关心的所有轮廓

def display_all_contours(img, df, grouping_var):
    # display with matplotlib

    # Create figure and axes
    fig, ax = plt.subplots(1)

    # Display the image
    ax.imshow(img)

    # split by contour
    grouped_frame = df.groupby(grouping_var)
    li = [grouped_frame.get_group(x) for x in grouped_frame.groups]

    # for every contour
    for i in range(len(li)):
        poly = patches.Polygon(np.array([li[i].xrT, li[i].yrT]).T,
                               fill=False)
        ax.add_patch(poly)

    for i in range(len(li)):
        poly = patches.Polygon(np.array([li[i].xlT, li[i].ylT]).T,
                               fill=False, color="white")
        ax.add_patch(poly)

    return("Displaying " + str(len(np.unique(df[grouping_var]))) + " contours.")

这是在具有我的图像形状的东西上绘制contorus的结果

mask = np.zeros((9373, 12273), dtype=np.uint8)

display_all_contours(mask, df, "contour.ID")

问题 现在，我想创建所有多边形的遮罩（在本例中为左侧）。因此，我创建了一个遮罩，并使用

cv2.fillConvexPoly

mask = np.zeros((9373, 12273), dtype=np.uint8)

display_all_contours(mask, df, "contour.ID")

for poly in np.unique(df["contour.ID"]):
    # subset
    sub_df = df[df["contour.ID"] == poly]
    # burn into the mask
    # explicitly burn into the mask
    mask = cv2.fillConvexPoly(mask, np.array(sub_df[["xlT", "ylT"]], 'int32'), 1)

出于某种原因，我不明白，这并没有产生我想要的结果

plt.imshow（掩码）

---

解决了这个问题，我实际上在寻找的函数是

fillPoly

mask = np.zeros((9373, 12273), dtype=np.uint8)

display_all_contours(mask, df, "contour.ID")

for poly in np.unique(df["contour.ID"]):
    # subset
    sub_df = df[df["contour.ID"] == poly]
    # burn into the mask
    # explicitly burn into the mask
    mask = cv2.fillConvexPoly(mask, np.array(sub_df[["xlT", "ylT"]], 'int32'), 1)

更换此线路可解决此问题

# mind the np.array(..., "int32") is wrapped in [] because that's how fillPoly likes it
mask = cv2.fillPoly(mask, [np.array(sub_df[["xlT", "ylT"]], 'int32')], 1)