231 lines
7.8 KiB
Python
231 lines
7.8 KiB
Python
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"""
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Color Features Pyramid
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Part of [imgdb] - Tools for analysis, indexing, search, and creative use of image data
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Overview
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========
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Pyramid down-sampling + color-feature analysis.
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Saves downsampled data to NAME.COLORSPACE.PYR{DOWNSAMPLING} data files mem-mapped to np arrays.
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Also retrieves and displays down-sampled image data.
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ds res(w*h) gridpx(w,h) grid()
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1 1920 * 1600 30, 20 64, 80
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2 32, 40
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3 480 * 400 16, 20
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4 240 * 200 15, 10 16, 20
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5 8, 10
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6 4, 5
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Cols, Rows (Many are in portrait, same but with vertical window rects)
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Resolution 1280, 960
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No. Tiles 40, 40
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Resolution 960, 1280
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No. Tiles 40, 40
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1280 960 0 1228800
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640 480 1 307200
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320 240 2 76800
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160 120 3 19200 + HISTO
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80 60 4 4800 + HISTO
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40 30 5 1200 + HISTO
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20 15 6 300
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10 8 7 80
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5 4 8 20
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"""
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import sys
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import os
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import math
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import cv2 as cv
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import numpy as np
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# import matplotlib.pyplot as plt
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from config import (
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check_params,
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NUM_LEVELS,
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NUM_HISTO_BINS,
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LEVELS_CONFIG
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)
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import color_features_tile as cft
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class ColorFeaturesPyramid(object):
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"""
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"""
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def __init__(self, **params) -> None:
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self.uuid = check_params(params, 'uuid', 'IMG_1001')
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# print(f"UUID: {self.uuid} -- {params}")
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self.full_tile = cft.ColorFeaturesTile(**{"uuid": self.uuid})
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self.down_sampling_levels = check_params(params, 'down_sampling', NUM_LEVELS)
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self.colorspace = check_params(params, 'colorspace', 'lab')
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self.write_flag = True
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self.debug = True
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def _determine_num_bins(self, sz, lvl):
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return int(sz / math.pow(2, (lvl + 1)))
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def _zeros_for_shape_level(self, sz, lvl):
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"""
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lists of np arrays of the correct size to hold our gradually shrinking arrays
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"""
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imgw, imgh = sz
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return np.zeros(
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(
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self._determine_num_bins(imgh, lvl),
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self._determine_num_bins(imgw, lvl),
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3,
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),
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dtype=np.uint16,
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)
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def _down_sample_in_place(self, in_mtrx, pwr, out_shape): # pwr: 0 1 2 3 4 ...
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print(f"in_mtrx.shape {in_mtrx.shape}")
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print(f"out_shape {out_shape}")
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(a, b, _) = in_mtrx.shape
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out_mtrx = np.zeros(out_shape, dtype=in_mtrx.dtype)
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mult = 2 ** pwr # 1 2 4 8 16 ...
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# same shape
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# for i in range(a):
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# for j in range(b):
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# print(i, j, a, b, in_mtrx[i,j,:])
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for i in range(a):
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for j in range(b):
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# print(
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# (i * mult),
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# ((i + 1) * mult),
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# (j * mult),
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# ((j + 1) * mult),
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# i,
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# j,
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# a,
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# b,
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# in_mtrx[i, j, :],
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# )
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out_mtrx[
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(i * mult) : ((i + 1) * mult), (j * mult) : ((j + 1) * mult)
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] = in_mtrx[i,j,:]
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return out_mtrx
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def _down_sample_and_analyze(self):
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"""
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Reads and image, performs a series of down-samplings, writes each to disk
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"""
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# Validate colorspace
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valid_colorspaces = ["rgb", "lab", "lab"]
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if self.colorspace not in valid_colorspaces:
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raise ValueError(f"Invalid colorspace: {self.colorspace}. Valid options are: {valid_colorspaces}")
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# dataroot = f'{self.full_tile.root_path}/img/'
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datapath = f'{self.full_tile.root_path}/img/{self.full_tile.uuid}/{self.full_tile.uuid}.jpeg'
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print(f'Down-sample + analyze: {datapath} - ({str(self.down_sampling_levels)})')
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img = cv.imread(datapath)
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print(img.shape)
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# print(img[500:520,600:620,:])
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imgw = img.shape[1]
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imgh = img.shape[0]
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ds_levels = int(self.down_sampling_levels)
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# convert color space once
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if self.colorspace == "rgb":
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img_ = cv.cvtColor(img, cv.COLOR_BGR2RGB)
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elif self.colorspace == "hsv":
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img_ = cv.cvtColor(img, cv.COLOR_BGR2HSV)
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else:
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img_ = cv.cvtColor(img, cv.COLOR_BGR2Lab)
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# add 1 to # of levels so we can write to the Nth + 1 level, properly sized
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# 0th level is image itself in imgs_ds, concat shifts zeros arrays to start
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# at index 1
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imgs_ds = [img_] + [
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self._zeros_for_shape_level((imgw, imgh), level)
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for level in range(ds_levels)
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]
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print(f"imgs_ds len: {len(imgs_ds)}\n")
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for i,img in enumerate(imgs_ds):
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print(f"{i}: {img.shape}\n")
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base_shape = imgs_ds[0].shape
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try:
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for level in range(ds_levels - 1):
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imgs_ds[(level + 1)] = cv.pyrDown(imgs_ds[level], imgs_ds[(level + 1)])
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# display copy
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img_a = self._down_sample_in_place(
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imgs_ds[level],
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level,
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base_shape
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)
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print(f"img_a shape: {img_a.shape} - imgs_ds shape: {imgs_ds[(level + 1)].shape} - level+1:{level+1}\n")
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print(f"writing {datapath[:-5]}.pyr_{str(level + 1)}_display.jpeg\n")
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dsimg_filename = f"{datapath[:-5]}.{self.colorspace}.pyr_{str(level)}_display.jpeg"
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if True or not os.path.exists(dsimg_filename):
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cv.imwrite(dsimg_filename, img_a)
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# real copy
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img_b = self._down_sample_in_place(
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imgs_ds[level],
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0,
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imgs_ds[level].shape
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)
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print(f"img_b shape: {img_b.shape} - imgs_ds shape: {imgs_ds[(level + 1)].shape} - level+1:{level+1}\n")
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print(f"writing {datapath[:-5]}.pyr_{str(level + 1)}.jpeg\n")
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dsimg_filename = f"{datapath[:-5]}.{self.colorspace}.pyr_{str(level)}.jpeg"
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if True or not os.path.exists(dsimg_filename):
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cv.imwrite(dsimg_filename, img_b)
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except Exception as e:
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print(str(e))
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pass
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# TODO: split out and refactor to allow different data outputs
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# for level in range(ds_levels):
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# fp = np.memmap((datapath + str(level+1)), dtype='uint8', mode='w+',
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# shape=(imgs_ds[level+1].shape[0], imgs_ds[level+1].shape[1], 3))
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# fp[:] = np.copy(imgs_ds[level+1])
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# fp.flush()
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# for now we need the 0th, original img AND
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# all the ds_levels recursively down-sampled image files
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# + 1 to include the 0th level
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for level in range(ds_levels - 1):
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# sub_img = imgs_ds[level]
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feat_tiles = cft.ColorFeaturesTile(
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**{
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"uuid": self.uuid,
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"level": int(level),
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"root_path": self.full_tile.root_path
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}
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)
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if LEVELS_CONFIG[level]["gridsplit"] is not None:
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gridsplit = LEVELS_CONFIG[level]["gridsplit"]
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yield (
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str(level),
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feat_tiles.analyze_img_histograms_postnorm(level)
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)
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if LEVELS_CONFIG[level]["tiles"]:
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yield (
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str(level),
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feat_tiles.analyze_img_tiles_postnorm(level)
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)
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if LEVELS_CONFIG[level]["target"]:
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targetblocks = LEVELS_CONFIG[level]["targetblocks"]
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yield (
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str(level),
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feat_tiles.analyze_img_blocks_postnorm(level)
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)
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