update,

vinniesniper-54816/task1/_lab/001-test-integration/retrieval copy.py

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+from compareImgs import compareImgs
+import cv2 as cv
+from glob import glob
+from compareImgs_hist import compareImgs_hist
+from compareImgs_hist_rgb import compareImgs_hist_rgb
+from SIFT import SIFT
+from pprint import pprint
+import os, sys
+
+# the directory of the image database
+database_dir = "image.orig"
+
+
+def retrieval(choice="1"):
+    if choice == "1":
+        src_rgb = cv.imread("beach.jpg")
+        # print("You choose: %s - beach\n" % choice)
+    if choice == "2":
+        src_rgb = cv.imread("building.jpg")
+        # print("You choose: %s - building\n" % choice)
+    if choice == "3":
+        src_rgb = cv.imread("bus.jpg")
+        # print("You choose: %s - bus\n" % choice)
+    if choice == "4":
+        src_rgb = cv.imread("dinosaur.jpg")
+        # print("You choose: %s - dinosaur\n" % choice)
+    if choice == "5":
+        src_rgb = cv.imread("flower.jpg")
+        # print("You choose: %s - flower\n" % choice)
+    if choice == "6":
+        src_rgb = cv.imread("horse.jpg")
+        # print("You choose: %s - horse\n" % choice)
+    if choice == "7":
+        src_rgb = cv.imread("man.jpg")
+        # print("You choose: %s - man\n" % choice)
+
+    min_diff = 1e50
+
+    # src_input = cv.imread("man.jpg")
+    # cv.imshow("Input", src_input)
+
+    # change the image to gray scale
+    src_gray = cv.cvtColor(src_rgb, cv.COLOR_BGR2GRAY)
+
+    # read image database
+    database = sorted(glob(database_dir + "/*.jpg"))
+
+    raw_results = []
+    i = 0
+    for img in database[0:150]:
+        # read image
+        img_rgb = cv.imread(img)
+        # convert to gray scale
+        img_gray = cv.cvtColor(img_rgb, cv.COLOR_BGR2GRAY)
+        # compare the two images
+        diff_gray = compareImgs(src_gray, img_gray)
+
+        # compare the two images by histogram, uncomment the following line to use histogram
+        diff_rgb = compareImgs_hist_rgb(src_rgb, img_rgb)
+
+        sift_match = SIFT(src_rgb, img_rgb)
+        raw_results.append([img, diff_gray, diff_rgb, sift_match])
+
+        # if i > 3:
+        #     break
+        i += 1
+
+    # normalize
+    min_diff_gray = min([raw_result[1] for raw_result in raw_results])
+    max_diff_gray = max([raw_result[1] for raw_result in raw_results])
+    min_diff_rgb = min([raw_result[2] for raw_result in raw_results])
+    max_diff_rgb = max([raw_result[2] for raw_result in raw_results])
+    min_diff_sift_match = min([raw_result[3] for raw_result in raw_results])
+    max_diff_sift_match = max([raw_result[3] for raw_result in raw_results])
+
+    for i in range(len(raw_results)):
+        raw_results[i] = [
+            raw_results[i][0],
+            (raw_results[i][1] - min_diff_gray) / (max_diff_gray - min_diff_gray),
+            (raw_results[i][2] - min_diff_rgb) / (max_diff_rgb - min_diff_rgb),
+            (raw_results[i][3] - min_diff_sift_match) / (max_diff_sift_match - min_diff_sift_match),
+        ]
+
+    weight_diff_gray = 0.7
+    weight_diff_rgb = 0.3
+    weight_sift_match = 0
+
+    for raw_result in raw_results:
+        img, diff_gray, diff_rgb, sift_match = raw_result
+        test = sum(
+            [
+                #
+                weight_diff_gray * diff_gray,
+                weight_diff_rgb * diff_rgb,
+                weight_sift_match * (1 - sift_match),
+            ]
+        )
+        if test <= min_diff:
+            min_diff = test
+            result = img
+
+    result_category = result.replace("image.orig/", "")[0]
+
+    print("r:" + result + " rc:" + result_category)
+    return result_category