update,
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louiscklaw
213
vinniesniper-54816/task1/_lab/001-test-integration/retrieval.py
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213
vinniesniper-54816/task1/_lab/001-test-integration/retrieval.py
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from compareImgs import compareImgs
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import cv2 as cv
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from glob import glob
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import numpy as np
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from write_csv import write_csv
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# from compareImgs_hist import compareImgs_hist
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from compareImgs_hist_rgb import compareImgs_hist_rgb
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from SIFT import SIFT
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from pprint import pprint
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import os, sys
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from countour_util import getContourDiff, checkCountourImg1
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from color_layout_descriptor import getFullCLDDiff
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from grayscale import grayscale
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# the directory of the image database
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database_dir = "image.orig"
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img_src_path = ""
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img_cache = {}
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def retrieval(choice="1", para_test=0):
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if choice == "1":
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img_src_path = "beach.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - beach\n" % choice)
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if choice == "2":
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img_src_path = "building.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - building\n" % choice)
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if choice == "3":
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img_src_path = "bus.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - bus\n" % choice)
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if choice == "4":
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img_src_path = "dinosaur.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - dinosaur\n" % choice)
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if choice == "5":
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img_src_path = "flower.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - flower\n" % choice)
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if choice == "6":
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img_src_path = "horse.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - horse\n" % choice)
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if choice == "7":
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img_src_path = "man.jpg"
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img_src_rgb = cv.imread(img_src_path)
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# print("You choose: %s - man\n" % choice)
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min_diff = 1e50
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# src_input = cv.imread("man.jpg")
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# cv.imshow("Input", src_input)
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# read image database
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database = sorted(glob(database_dir + "/*.jpg"))
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weight_diff_gray = 0.3
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weight_diff_rgb = 0.3
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weight_diff_sift = 0.1
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weight_diff_contour = 0
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weight_diff_cld = 0.3
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weight_diff_u = 0
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weight_diff_d = 0
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weight_diff_l = 0
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weight_diff_r = 0
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# check of contour applicable
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check_contour = True
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len_contour = len(checkCountourImg1(img_src_rgb)[0])
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# len_contour = len(checkCountourImg1(img_src_rgb, 150)[0])
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# len_contour_check_building = len(checkCountourImg1(img_src_rgb, 100)[0])
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print(len_contour)
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# sys.exit()
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if len_contour == 0:
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# no contour found
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check_contour = False
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else:
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weight_diff_gray = 0.0
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weight_diff_rgb = 0.1
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weight_diff_sift = 0.1
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weight_diff_contour = 0.75
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weight_diff_cld = 0.05
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weight_diff_u = 0
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weight_diff_d = 0
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weight_diff_l = 0
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weight_diff_r = 0
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# else:
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# weight_diff_gray = 0.01
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# weight_diff_rgb = 0.1
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# weight_diff_sift = 0.0
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# weight_diff_contour = 0.6
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# weight_diff_cld = 0.2
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# weight_diff_u = 0
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# weight_diff_d = 0
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# weight_diff_l = 0
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# weight_diff_r = 0
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print("contour mode applied")
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raw_results = []
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i = 0
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# for img_test_path in database:
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# for img_test_path in database[200:299] + database[600:699] + database[400:499] + database[700:799] + database[100:199]:
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# for img_test_path in database[100:199] + database[500:599]:
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for img_test_path in database[0:999]:
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# for img_test_path in database[135:140]:
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if img_test_path[-5:] == "0.jpg":
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print("processing: " + img_test_path, end="\r")
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img_test_rgb = None
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# # read image
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if img_test_path in img_cache:
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img_test_rgb = img_cache[img_test_path]
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else:
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img_test_rgb = cv.imread(img_test_path)
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img_cache[img_test_path] = img_test_rgb
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# # convert to gray scale
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# img_test_gray = cv.cvtColor(img_test_rgb, cv.COLOR_BGR2GRAY)
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# change the image to gray scale
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img_src_gray = grayscale(img_src_path)
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img_test_gray = grayscale(img_test_path)
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diff_gray = compareImgs(img_src_gray, img_test_gray)
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# compare the two images by histogram, uncomment the following line to use histogram
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diff_rgb = compareImgs_hist_rgb(img_src_path, img_test_path, 11)
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diff_contour = getContourDiff(img_src_path, img_test_path) if check_contour else 0
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d_cld_full, d_cld_up, d_cld_down, d_cld_left, d_cld_right = getFullCLDDiff(img_src_path, img_test_path, 8, 256)
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sift_match = SIFT(img_src_path, img_test_path)
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# sift_match = 1
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raw_results.append(
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[
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img_test_path,
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diff_gray,
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diff_rgb,
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sift_match,
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diff_contour,
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#
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d_cld_full,
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d_cld_up,
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d_cld_down,
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d_cld_left,
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d_cld_right,
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]
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)
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# if i > 3:
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# break
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i += 1
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print("\nprocess done")
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np_raw_results = np.array(raw_results)
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np.savetxt("np_raw_results.csv", np_raw_results, delimiter=",", fmt="%s")
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min_values = np.min(np_raw_results[:, 1:].astype(np.float64), axis=0)
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max_values = np.max(np_raw_results[:, 1:].astype(np.float64), axis=0)
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with np.errstate(divide="ignore", invalid="ignore"):
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normalized_array = (np_raw_results[:, 1:].astype(np.float64) - min_values) / (max_values - min_values)
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normalized_array = np.nan_to_num(normalized_array, nan=0.0)
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np_raw_results = np.concatenate((np_raw_results, normalized_array), axis=1)
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np.savetxt("np_raw_results_n.csv", np_raw_results, delimiter=",", fmt="%s")
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start_col = 9
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np_raw_results[:, start_col + 1] = np_raw_results[:, start_col + 1].astype(np.float64) * weight_diff_gray
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np_raw_results[:, start_col + 2] = np_raw_results[:, start_col + 2].astype(np.float64) * weight_diff_rgb
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np_raw_results[:, start_col + 3] = (1 - np_raw_results[:, start_col + 3].astype(np.float64)) * weight_diff_sift
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if check_contour:
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np_raw_results[:, start_col + 4] = np_raw_results[:, start_col + 4].astype(np.float64) * weight_diff_contour
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else:
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np_raw_results[:, start_col + 4] = 0
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#
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np_raw_results[:, start_col + 5] = np_raw_results[:, start_col + 5].astype(np.float64) * weight_diff_cld
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np_raw_results[:, start_col + 6] = np_raw_results[:, start_col + 6].astype(np.float64) * weight_diff_u
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np_raw_results[:, start_col + 7] = np_raw_results[:, start_col + 7].astype(np.float64) * weight_diff_d
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np_raw_results[:, start_col + 8] = np_raw_results[:, start_col + 8].astype(np.float64) * weight_diff_l
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np_raw_results[:, start_col + 9] = np_raw_results[:, start_col + 9].astype(np.float64) * weight_diff_r
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diff_sums = np.sum(np_raw_results[:, 10:-1].astype(np.float64), axis=1)
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np_raw_results = np.concatenate((np_raw_results, diff_sums[:, np.newaxis]), axis=1)
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np_raw_results_sorted = np_raw_results[np.argsort(np_raw_results[:, -1].astype(np.float64))]
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raw_results = np_raw_results_sorted.tolist()
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min_raw_result = raw_results[0]
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result = min_raw_result[0]
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write_csv(raw_results, "raw_results.csv")
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result_category = result.replace("image.orig/", "")[0]
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print("r:" + result + " rc:" + result_category)
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return result_category
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