update,

vinniesniper-54816/task1/from_client/2022-Codes-Python/demo.py

@@ -0,0 +1,164 @@
+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()