Vectorizing all pixels as separate polygons using rasterio

Question

I use rasterio.features.shapes and use the geometry of pixels in raster data to create polygons for each pixel. Pixels with the same value merge these pixels if they are adjacent.

What I want is for each pixel to be in the form of separate polygons. How can I do it?

import rasterio
from rasterio.features import shapes
mask = None
with rasterio.Env():
    with rasterio.open(r"C:\Users\Lenovo\Desktop\clipped.tiff") as src:
        image = src.read(1) # first band
        results = (
        {'properties': {'raster_val': v}, 'geometry': s}
        for i, (s, v) 
        in enumerate(
            shapes(image, transform=src.transform)))
            
geoms = list(results)

The result (the red area is the combination of two same value pixels):

Expected result (separate polygons for all pixels):

Answer 1

When two adjacent pixels have the same value they are dissolved into one.

You can:

1. add a random float value between 0-0.9999 to each pixel value to make all pixel values unique,

2. vectorize

3. then floor them back to the original values:

   import rasterio
   from rasterio.features import shapes
   import numpy as np
   import geopandas as gpd
   
   file = r"C:\GIS\data\testdata\random_uint16.tif"
   
   mask = None
   with rasterio.Env():
   with rasterio.open(file) as src:
       image = src.read(1) # first band
       #image[:2,:2]
       #array([[7, 4],
       #       [2, 7]], dtype=int16)
   
       newimage = np.random.uniform(size=image.shape).astype(np.float32) #Create an array of random floats between 0-1 with the same shape as the raster
       newimage = image+newimage #Sum the original and random array
       #array([[7.9048324, 4.694755 ],
       #       [2.716989 , 7.423101 ]], dtype=float32)
   
       results = (
       {'properties': {'raster_val': v}, 'geometry': s}
       for i, (s, v) 
       in enumerate(
           shapes(newimage, mask=mask, transform=src.transform)))
   
   geoms = list(results)
   
   df  = gpd.GeoDataFrame.from_features(geoms)
   df = df.set_crs(3006)
   df["raster_val"] = np.floor(df["raster_val"]).astype(int) 
   #Restore the original raster values
   df.to_file(r"C:\GIS\data\testdata\random_unint16.shp")