update,

vinniesniper-54816/task1/_lab/001-test-integration/demo.py

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+import cv2 as cv
+import numpy as np
+from glob import glob
+
+# the directory of the image database
+database_dir = "image.orig"
+
+# Compute pixel-by-pixel difference and return the sum
+def compareImgs(img1, img2):
+    # resize img2 to img1
+    img2 = cv.resize(img2, (img1.shape[1], img1.shape[0]))
+    diff = cv.absdiff(img1, img2)
+    return diff.sum()
+
+def compareImgs_hist(img1, img2):
+    width, height = img1.shape[1], img1.shape[0]
+    img2 = cv.resize(img2, (width, height))
+    num_bins = 10
+    hist1 = [0] * num_bins
+    hist2 = [0] * num_bins
+    bin_width = 255.0 / num_bins + 1e-4
+    # compute histogram from scratch
+
+    # for w in range(width):
+    #     for h in range(height):
+    #         hist1[int(img1[h, w] / bin_width)] += 1
+    #         hist2[int(img2[h, w] / bin_width)] += 1
+
+    # compute histogram by using opencv function
+    # https://docs.opencv.org/4.x/d6/dc7/group__imgproc__hist.html#ga4b2b5fd75503ff9e6844cc4dcdaed35d
+
+    hist1 = cv.calcHist([img1], [0], None, [num_bins], [0, 255])
+    hist2 = cv.calcHist([img2], [0], None, [num_bins], [0, 255])
+    sum = 0
+    for i in range(num_bins):
+        sum += abs(hist1[i] - hist2[i])
+    return sum / float(width * height)
+
+def retrieval():
+    print("1: beach")
+    print("2: building")
+    print("3: bus")
+    print("4: dinosaur")
+    print("5: flower")
+    print("6: horse")
+    print("7: man")
+    choice = input("Type in the number to choose a category and type enter to confirm\n")
+    if choice == '1':
+        src_input = cv.imread("beach.jpg")
+        print("You choose: %s - beach\n" % choice)
+    if choice == '2':
+        src_input = cv.imread("building.jpg")
+        print("You choose: %s - building\n" % choice)
+    if choice == '3':
+        src_input = cv.imread("bus.jpg")
+        print("You choose: %s - bus\n" % choice)
+    if choice == '4':
+        src_input = cv.imread("dinosaur.jpg")
+        print("You choose: %s - dinosaur\n" % choice)
+    if choice == '5':
+        src_input = cv.imread("flower.jpg")
+        print("You choose: %s - flower\n" % choice)
+    if choice == '6':
+        src_input = cv.imread("horse.jpg")
+        print("You choose: %s - horse\n" % choice)
+    if choice == '7':
+        src_input = 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_input, cv.COLOR_BGR2GRAY)
+
+    # read image database
+    database = sorted(glob(database_dir + "/*.jpg"))
+
+
+    for img in database:
+        # 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 = compareImgs(src_gray, img_gray)
+        # compare the two images by histogram, uncomment the following line to use histogram
+        # diff = compareImgs_hist(src_gray, img_gray)
+        print(img, diff)
+        # find the minimum difference
+        if diff <= min_diff:
+            # update the minimum difference
+            min_diff = diff
+            # update the most similar image
+            closest_img = img_rgb
+            result = img
+    
+    print("the most similar image is %s, the pixel-by-pixel difference is %f " % (result, min_diff))
+    print("\n")
+
+    cv.imshow("Result", closest_img)
+    cv.waitKey(0)
+    cv.destroyAllWindows()
+
+def SIFT():
+    img1 = cv.imread("flower.jpg")
+    img2 = cv.imread("image.orig/685.jpg")
+    if img1 is None or img2 is None:
+        print('Error loading images!')
+        exit(0)
+    #-- Step 1: Detect the keypoints using SIFT Detector, compute the descriptors
+    minHessian = 400
+    detector = cv.SIFT_create()
+    keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
+    keypoints2, descriptors2 = detector.detectAndCompute(img2, None)
+    #-- Step 2: Matching descriptor vectors with a brute force matcher
+    matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE)
+    matches = matcher.match(descriptors1, descriptors2)
+    #-- Draw matches
+    img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
+    cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches)
+    #-- Show detected matches
+    cv.imshow('Matches: SIFT (Python)', img_matches)
+    cv.waitKey()
+
+    # draw good matches
+    matches = sorted(matches, key = lambda x:x.distance)
+    min_dist = matches[0].distance
+    good_matches = tuple(filter(lambda x:x.distance <= 2 * min_dist, matches))
+
+    img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
+    cv.drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
+    
+    #-- Show detected matches
+    cv.imshow('Good Matches: SIFT (Python)', img_matches)
+    cv.waitKey()
+
+
+def main():
+    # img = cv.imread("beach.jpg")
+    # cv.imshow("Image", img)
+    # from matplotlib import pyplot as plt
+    # plt.hist(img.ravel(),10,[0,256]); plt.show()
+    # gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    # cv.imshow("Gray Image", gray_img)
+    # cv.waitKey()
+
+
+    print("1: Image retrieval demo")
+    print("2: SIFT demo")
+    number = int(input("Type in the number to choose a demo and type enter to confirm\n"))
+    if number == 1:
+        retrieval()
+    elif number == 2:
+        SIFT()
+        # pass
+    else:
+        print("Invalid input")
+        exit()
+
+main()