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# color_features_tiles.py - color features tiled data from images
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# ImageDB2 - Tools for analysis, indexing, search, and creative use of image data
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"""
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ColorFeaturesTiles
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Part of ImageDB2 - 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.TILE{SIZE}.OLAP{SIZE} data files mem-mapped to np arrays.
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Also retrieves and displays tiled image data.
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"""
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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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from dataclasses import dataclass
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from avdb.config import (
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check_params,
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NUM_HISTO_BINS,
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LEVELS_CONFIG
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)
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class ColorFeaturesTile(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', 'TESTIMG')
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self.root_path = check_params(params, 'root_path', '/home/kfl/dev/git/avdb-py-v1')
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self.level = check_params(params, 'level', 0)
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self.colorspace = check_params(params, 'color_space', 'lab')
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self.write_flag = True
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self.debug = True
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print(f"CREATING TILE -- LEVEL: {self.level} - UUID: {self.uuid} color space: {self.colorspace} - write_flag: {self.write_flag}")
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def analyze_img_tiles_postnorm(self, lvl=None):
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"""
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"""
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if lvl is None:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.jpeg"
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else:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.{self.colorspace}.pyr_{lvl}.jpeg"
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print(imgpath)
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img = cv.imread(imgpath, cv.IMREAD_UNCHANGED)
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imgw = img.shape[1]
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imgh = img.shape[0]
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# iterate over the tiles and unroll into linear chunks
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def _aux():
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for top_crnr in range(imgh):
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for lft_crnr in range(imgw):
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# print(img[top_crnr, lft_crnr])
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yield np.r_[
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img[top_crnr,lft_crnr],
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]
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total_tiles = np.c_[list(_aux())]
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if self.write_flag is True:
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print(imgpath)
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mmappath = f"{imgpath}.tiles.mmap"
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fp = np.memmap(
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mmappath,
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dtype=np.uint16,
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mode="w+",
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shape=total_tiles.shape
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)
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print(f"all tiles : {total_tiles.shape}")
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fp[:] = total_tiles
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fp.flush()
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return total_tiles, (imgw, imgh, 1, 1)
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def analyze_img_blocks_postnorm(self, lvl=None):
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"""
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"""
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if lvl is None:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.jpeg"
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else:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.{self.colorspace}.pyr_{lvl}.jpeg"
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print(imgpath)
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img = cv.imread(imgpath, cv.IMREAD_UNCHANGED)
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imgw = img.shape[1]
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imgh = img.shape[0]
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target_blocks = LEVELS_CONFIG[lvl]["targetblocks"]
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if target_blocks is None:
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raise ValueError(f"target_blocks is None for level {self.level}")
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# calculate gutters
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imgw -= 2
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imgh -= 2
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# iterate over the blocks and unroll into linear chunks
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def _aux():
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for top_crnr in range(imgh):
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for lft_crnr in range(imgw):
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yield np.r_[
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img[top_crnr,lft_crnr:lft_crnr+3],
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img[top_crnr+1,lft_crnr:lft_crnr+3],
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img[top_crnr+2,lft_crnr:lft_crnr+3]
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].flatten()
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total_blocks = np.c_[list(_aux())]
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if self.write_flag is True:
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print(imgpath)
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mmappath = f"{imgpath}.blocks.mmap"
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fp = np.memmap(
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mmappath,
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dtype=np.uint16,
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mode="w+",
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shape=total_blocks.shape
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)
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print(f"all histograms : {total_blocks.shape}")
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fp[:] = total_blocks
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fp.flush()
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return total_blocks, (imgw, imgh, 1, 1)
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def analyze_img_histograms_postnorm(self, lvl=None):
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"""
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overlapping tiles analysis
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+ width in tiles must be a multiple of 2
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"""
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if lvl is None:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.jpeg"
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else:
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imgpath = f"{self.root_path}/img/{self.uuid}/{self.uuid}.{self.colorspace}.pyr_{lvl}.jpeg"
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print(imgpath)
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img = cv.imread(imgpath, cv.IMREAD_UNCHANGED)
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imgw = img.shape[1]
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imgh = img.shape[0]
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# / by 1 = whole dimension
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gridsplit = LEVELS_CONFIG[lvl]["gridsplit"]
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tilesw = (gridsplit + 1) if gridsplit is not None else 1
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tilesh = (gridsplit + 1) if gridsplit is not None else 1
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tilew = int(imgw / tilesw)
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tileh = int(imgh / tilesh)
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# img = self.convert_colorspace(img)
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mask = np.zeros(img.shape[:2], np.uint8)
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all_histograms = np.array([], dtype=np.float32)
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all_histograms.shape = (0, 1)
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lastx, lasty = 0, 0
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print(f"dims: {(tilesw,tilesh)}")
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for y in range(tilesh):
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for x in range(tilesw):
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if (lastx, lasty) != (x, y):
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mask[
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int(tilew/2 * lastx) : int((tilew/2 * lastx) + tilew),
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int(tileh/2 * lasty) : int((tileh/2 * lasty) + tileh),
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] = 0
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lastx = x
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lasty = y
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mask[
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int(tilew/2 * x) : int((tilew/2 * x) + tilew),
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int(tileh/2 * y) : int((tileh/2 * y) + tileh),
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] = 255
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# calculate 3 channels' histograms
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hist0_masked = cv.calcHist(
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[img], [0], mask, [NUM_HISTO_BINS], [0, 65536]
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)
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hist0_masked.shape = (NUM_HISTO_BINS, 1)
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row0 = hist0_masked
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hist1_masked = cv.calcHist(
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[img], [1], mask, [NUM_HISTO_BINS], [0, 256]
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)
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hist1_masked.shape = (NUM_HISTO_BINS, 1)
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row1 = hist1_masked
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hist2_masked = cv.calcHist(
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[img], [2], mask, [NUM_HISTO_BINS], [0, 256]
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)
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hist2_masked.shape = (NUM_HISTO_BINS, 1)
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row2 = hist2_masked
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# print(f"row0: {row0} - {row0.shape}")
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# print(f"row1: {row1} - {row1.shape}")
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# print(f"row2: {row2} - {row2.shape}")
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# L [bin0,bin1,..bin15], r=0, c=0
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# A [bin0,bin1,..bin15]
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# B [bin0,bin1,..bin15]
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# L [bin0,bin1,..bin15], r=0, c=1
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# A [bin0,bin1,..bin15]
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# B [bin0,bin1,..bin15]
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# etc.
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# top left to bottom right
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all_histograms: np.array = np.r_[
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all_histograms, row0, row1, row2
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]
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# print(f"ALL HISTOGRAMS: {all_histograms.shape}")
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# print(all_histograms[-48:,:])
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total_hist = np.reshape(all_histograms[:], (-1,48))
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sz = tilesh * tilesw
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cv.normalize(
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total_hist, total_hist, norm_type=cv.NORM_MINMAX
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)
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# total_hist *= 255.
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print(f"total HISTOGRAM: {total_hist.shape} {total_hist.sum()}")
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print(total_hist[:2,:])
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print(np.max(total_hist, 0)[:24])
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print(np.max(total_hist, 1)[:24])
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print(np.min(total_hist, 0)[:24])
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print(np.min(total_hist, 1)[:24])
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print(np.mean(total_hist, 0)[:24])
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print(np.mean(total_hist, 1)[:24])
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if self.write_flag is True:
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print(f"writing to: {imgpath}.histos.mmap")
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mmappath = f"{imgpath}.histos.mmap"
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fp = np.memmap(
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mmappath,
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dtype="float32",
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mode="w+",
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shape=total_hist.shape
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)
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print(f"all histograms : {total_hist.shape}")
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fp[:] = total_hist
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# print(f"CHECK: {fp[:2,:]} - {np.max(fp)} - {np.min(fp)}")
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fp.flush()
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return total_hist, (imgw, imgh, tilesw, tilesh)
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