diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/beach.jpg b/vinniesniper-54816/task1/_ref/2022-Codes-Python/beach.jpg new file mode 100644 index 00000000..c7f3e98a --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/beach.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37694a3d30fc33348fc7dbdcf640280a44c59e54f42ef304edc53dc96acc0bae +size 277757 diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/building.jpg b/vinniesniper-54816/task1/_ref/2022-Codes-Python/building.jpg new file mode 100644 index 00000000..b6dc724f --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/building.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:049b1f61209c48760f4f0a1cb00ead76771b9d933a7a7e1c0071d8b1ad72f602 +size 81982 diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/bus.jpg b/vinniesniper-54816/task1/_ref/2022-Codes-Python/bus.jpg new file mode 100644 index 00000000..29149ca9 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cebc71c510740b68a3c0fd1da1ae25103f4e07effa0afbfe643c11064d34ae8b +size 558953 diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo.py new file mode 100644 index 00000000..988480a7 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo.py @@ -0,0 +1,162 @@ +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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft1.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft1.py new file mode 100644 index 00000000..8b724515 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft1.py @@ -0,0 +1,260 @@ +import cv2 as cv +import numpy as np +from glob import glob +import os,sys +from pprint import pprint + +# 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") + + choice = '2' + if choice == '1': + img_input = cv.imread("beach.jpg") + print("You choose: %s - beach\n" % choice) + if choice == '2': + img_input = cv.imread("building.jpg") + print("You choose: %s - building\n" % choice) + if choice == '3': + img_input = cv.imread("bus.jpg") + print("You choose: %s - bus\n" % choice) + if choice == '4': + img_input = cv.imread("dinosaur.jpg") + print("You choose: %s - dinosaur\n" % choice) + if choice == '5': + img_input = cv.imread("flower.jpg") + print("You choose: %s - flower\n" % choice) + if choice == '6': + img_input = cv.imread("horse.jpg") + print("You choose: %s - horse\n" % choice) + if choice == '7': + img_input = cv.imread("man.jpg") + print("You choose: %s - man\n" % choice) + + min_diff = 1e50 + + # src_input = cv.imread("man.jpg") + cv.imshow("Input", img_input) + + # change the image to gray scale + src_gray = cv.cvtColor(img_input, cv.COLOR_BGR2GRAY) + + # read image database + database = sorted(glob(database_dir + "/*.jpg")) + + print('working') + diff_array = [] + for img in database: + # read image + img_rgb = cv.imread(img) + + # compare the two images + diff = compareImgs(img_input, img_rgb) + + # compare the two images by histogram, uncomment the following line to use histogram + # diff = compareImgs_hist(src_gray, img_gray) + # print(img, diff) + diff_array.append([img,diff, img_rgb]) + + diff_array.sort(key=lambda x: x[1]) + result = diff_array[0] + min_diff = result[1] + closest_img = result[2] + + i = 0 + for diff in diff_array: + i += 1 + # SIFT_debug(img_input, diff[2]) + filename = diff[0].replace('image.orig/','') + category = filename[0] + + if (category=="2"): + pprint('found at ' + str(i)) + matches = SIFT_compare(img_input, diff[2]) + print(diff[0],matches) + result = diff + min_diff = result[1] + closest_img = result[2] + # SIFT_debug(img_input, closest_img) + break + + # 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 SIFT_debug(img1, img2): + # 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 SIFT_compare(img1, img2): + # 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() + return len(good_matches) + +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() + retrieval() + + print("done") + + import os,sys + sys.exit() + + 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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2.py new file mode 100644 index 00000000..8bcd1425 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2.py @@ -0,0 +1,388 @@ +import cv2 as cv +import numpy as np +from glob import glob +import os, sys +from pprint import pprint + +# 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 color_layout_descriptor(image, num_blocks=8, resize_to_px=256): + resized_image = cv.resize(image, (resize_to_px, resize_to_px)) + ycrcb_image = cv.cvtColor(resized_image, cv.COLOR_BGR2YCrCb) + + # Step 5: Divide the image into sub-blocks + block_size = int(256 / num_blocks) + blocks = [] + for i in range(num_blocks): + for j in range(num_blocks): + block = ycrcb_image[ + i * block_size : (i + 1) * block_size, + j * block_size : (j + 1) * block_size, + ] + blocks.append(block) + + # Step 6: Extract features from each sub-block + i = 0 + features = [] + center_features = [] + for block in blocks: + # Compute the mean and standard deviation of each color channel + mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1)) + std_y, std_cr, std_cb = np.std(block, axis=(0, 1)) + features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + + if (i == 28) or (i == 29) or (i == 36) or (i == 37): + center_features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + i += 1 + + # Step 7: Concatenate features into a single vector + cld_full_picture = np.array(features) + cld_center = np.array(center_features) + + return (cld_full_picture, cld_center) + + +def colorlayoutCompare(cld1, cld2): + distance = np.linalg.norm(cld1 - cld2) + + similarity = 1.0 / (1.0 + distance) + + return (distance, similarity) + + +def retrieval(choice="3"): + print("testing 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": + img_input = cv.imread("beach.jpg") + print("test: %s - beach" % choice) + if choice == "2": + img_input = cv.imread("building.jpg") + print("test: %s - building" % choice) + if choice == "3": + img_input = cv.imread("bus.jpg") + print("test: %s - bus" % choice) + if choice == "4": + img_input = cv.imread("dinosaur.jpg") + print("test: %s - dinosaur" % choice) + if choice == "5": + img_input = cv.imread("flower.jpg") + print("test: %s - flower" % choice) + if choice == "6": + img_input = cv.imread("horse.jpg") + print("test: %s - horse" % choice) + if choice == "7": + img_input = cv.imread("man.jpg") + print("test: %s - man" % choice) + + min_diff = 1e50 + # src_input = cv.imread("man.jpg") + # cv.imshow("Input", img_input) + # change the image to gray scale + # src_gray = cv.cvtColor(img_input, cv.COLOR_BGR2GRAY) + + # read image database + database = sorted(glob(database_dir + "/*.jpg")) + + descriptors = [] + + (cld1, cld1_center) = color_layout_descriptor(img_input) + for img in database: + print(f"processing {img}", end="\r") + + # read image + img_rgb = cv.imread(img) + (cld2, cld2_center) = color_layout_descriptor(img_rgb) + + # compare the two images + # diff = compareImgs(img_input, img_rgb) + (diff_full, s_full) = colorlayoutCompare(cld1, cld2) + (diff_center, s_center) = colorlayoutCompare(cld1_center, cld2_center) + + # compare the two images by histogram, uncomment the following line to use histogram + # diff = compareImgs_hist(src_gray, img_gray) + # print(img, diff) + + len_good_matches = SIFT_compare(img_input, img_rgb) + + descriptors.append([img, img_rgb, s_full, s_center, len_good_matches]) + print("\nprocess done") + + normalized_descriptors = [] + max_s_full = max(descriptors, key=lambda x: x[2])[2] + min_s_full = min(descriptors, key=lambda x: x[2])[2] + + max_s_center = max(descriptors, key=lambda x: x[3])[3] + min_s_center = min(descriptors, key=lambda x: x[3])[3] + + max_good_matches = max(descriptors, key=lambda x: x[4])[4] + min_good_matches = min(descriptors, key=lambda x: x[4])[4] + + for descriptor in descriptors: + normalized_s_full = (descriptor[2] - min_s_full) / (max_s_full - min_s_full) + normalized_s_center = (descriptor[3] - min_s_center) / (max_s_center - min_s_center) + normalized_good_matches = (descriptor[4] - min_good_matches) / (max_good_matches - min_good_matches) + + normalized_descriptors.append([descriptor[0], descriptor[1], normalized_s_full, normalized_s_center, normalized_good_matches]) + + print("\nnormalized descriptors done") + + for descriptor in normalized_descriptors: + print(descriptor[0], descriptor[2], descriptor[3], descriptor[4]) + + import csv + + with open("descriptor.csv", "w", newline="") as csvfile: + spamwriter = csv.writer(csvfile) + spamwriter.writerow(["image", "similarity_full", "similarity_center", "good_matches"]) + for descriptor in normalized_descriptors: + spamwriter.writerow([descriptor[0], descriptor[2], descriptor[3], descriptor[4]]) + + import xlsxwriter + + workbook = xlsxwriter.Workbook("descriptor.xlsx") + worksheet = workbook.add_worksheet() + row = 0 + worksheet.write(row, 0, "image") + worksheet.write(row, 1, "similarity_full") + worksheet.write(row, 2, "similarity_center") + worksheet.write(row, 3, "good_matches") + row += 1 + for descriptor in normalized_descriptors: + worksheet.write(row, 0, descriptor[0]) + worksheet.write(row, 1, descriptor[2]) + worksheet.write(row, 2, descriptor[3]) + worksheet.write(row, 3, descriptor[4]) + row += 1 + workbook.close() + + sys.exit() + + # sort by s_full, largest first + descriptors.sort(key=lambda x: x[2]) + result = descriptors[0] + closest_img = result[1] + + # get feature match + diff_with_matches = [] + for descriptor in descriptors[0:5]: + # SIFT_debug(img_input, diff[2]) + full_file_name = descriptor[0] + img = descriptor[1] + diff_full = descriptor[2] + diff_center = descriptor[3] + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + len_good_matches = SIFT_compare(img_input, img) + + diff_with_matches.append([full_file_name, img, len_good_matches, 0, s_full, s_center]) + + matches = sorted(diff_with_matches, key=lambda x: x[4], reverse=True) + for match in matches: + pprint((match[0], match[2], match[4], match[5])) + + [full_file_name, closest_img, len_good_matches, _, s_full, s_center] = matches[0] + + # 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() + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + + print("f:" + filename_only + ": c:" + category) + + return category + + +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 SIFT_debug(img1, img2): + # 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 SIFT_compare(img1, img2): + # 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() + return len(good_matches) + + +def test(): + test_result = True + # 1, 2, 4, 7 + for i in [6]: + if str(i) == retrieval(str(i)): + print("test ok") + else: + test_result = False + + if test_result: + print("all test ok") + pass + else: + print("some test failed") + pass + + +def main(): + + test() + sys.exit() + + 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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_error.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_error.py new file mode 100644 index 00000000..6f98b179 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_error.py @@ -0,0 +1,316 @@ +import cv2 as cv +import numpy as np +from glob import glob +import os, sys +from pprint import pprint + +# 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 color_layout_descriptor(image, num_blocks=8, resize_to_px=256): + resized_image = cv.resize(image, (resize_to_px, resize_to_px)) + ycrcb_image = cv.cvtColor(resized_image, cv.COLOR_BGR2YCrCb) + + # Step 5: Divide the image into sub-blocks + block_size = int(resize_to_px / num_blocks) + blocks = [] + for i in range(num_blocks): + for j in range(num_blocks): + block = ycrcb_image[ + i * block_size : (i + 1) * block_size, + j * block_size : (j + 1) * block_size, + ] + blocks.append(block) + + # Step 6: Extract features from each sub-block + features = [] + for block in blocks: + # Compute the mean and standard deviation of each color channel + mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1)) + std_y, std_cr, std_cb = np.std(block, axis=(0, 1)) + features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + + # Step 7: Concatenate features into a single vector + cld_vector = np.array(features) + + return cld_vector + + +def colorlayoutCompare(img1, img2): + cld1 = color_layout_descriptor(img1) + cld2 = color_layout_descriptor(img2) + distance = np.linalg.norm(cld1 - cld2) + + similarity = 1.0 / (1.0 + distance) + + return (distance, similarity) + + +def retrieval(choice="3"): + print("testing 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": + img_input = cv.imread("beach.jpg") + print("You choose: %s - beach\n" % choice) + if choice == "2": + img_input = cv.imread("building.jpg") + print("You choose: %s - building\n" % choice) + if choice == "3": + img_input = cv.imread("bus.jpg") + print("You choose: %s - bus\n" % choice) + if choice == "4": + img_input = cv.imread("dinosaur.jpg") + print("You choose: %s - dinosaur\n" % choice) + if choice == "5": + img_input = cv.imread("flower.jpg") + print("You choose: %s - flower\n" % choice) + if choice == "6": + img_input = cv.imread("horse.jpg") + print("You choose: %s - horse\n" % choice) + if choice == "7": + img_input = cv.imread("man.jpg") + print("You choose: %s - man\n" % choice) + + min_diff = 1e50 + + # src_input = cv.imread("man.jpg") + # cv.imshow("Input", img_input) + + # change the image to gray scale + src_gray = cv.cvtColor(img_input, cv.COLOR_BGR2GRAY) + + # read image database + database = sorted(glob(database_dir + "/*.jpg")) + + diff_array = [] + for img in database: + print(f"processing {img}", end="\r") + + # read image + img_rgb = cv.imread(img) + + # compare the two images + # diff = compareImgs(img_input, img_rgb) + (d, s) = colorlayoutCompare(img_input, img_rgb) + diff = d + + # compare the two images by histogram, uncomment the following line to use histogram + # diff = compareImgs_hist(src_gray, img_gray) + # print(img, diff) + diff_array.append([img, diff, img_rgb]) + + diff_array.sort(key=lambda x: x[1]) + result = diff_array[0] + min_diff = result[1] + closest_img = result[2] + + # get feature match + diff_with_matches = [] + i = 0 + for diff in diff_array[0:20]: + i += 1 + # SIFT_debug(img_input, diff[2]) + full_file_name = diff[0] + img = diff[2] + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + len_good_matches = SIFT_compare(img_input, img) + + diff_with_matches.append([full_file_name, len_good_matches, d, img]) + + matches = sorted(diff_with_matches, key=lambda x: x[1], reverse=True) + + [full_file_name, len_good_matches, d, closest_img] = matches[0] + + pprint(full_file_name) + + # 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() + + print("cat:" + category + ": found") + + return category + + +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 SIFT_debug(img1, img2): + # 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 SIFT_compare(img1, img2): + # 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() + return len(good_matches) + + +def main(): + print("cat 1 test passed" if "1" == retrieval("1") else "cat 1 test failed") # OK + # retrieval("2") # OK + # retrieval("4") # OK + # retrieval("6") # OK + # retrieval("7") # OK + print(retrieval("3")) # failed + # retrieval("5") # failed + + print("done") + + sys.exit() + + 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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_regression.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_regression.py new file mode 100644 index 00000000..abae3b04 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_regression.py @@ -0,0 +1,355 @@ +import cv2 as cv +import numpy as np +from glob import glob +import os, sys +from pprint import pprint + +# 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 color_layout_descriptor(image, num_blocks=8, resize_to_px=256): + resized_image = cv.resize(image, (resize_to_px, resize_to_px)) + ycrcb_image = cv.cvtColor(resized_image, cv.COLOR_BGR2YCrCb) + + # Step 5: Divide the image into sub-blocks + block_size = int(256 / num_blocks) + blocks = [] + for i in range(num_blocks): + for j in range(num_blocks): + block = ycrcb_image[ + i * block_size : (i + 1) * block_size, + j * block_size : (j + 1) * block_size, + ] + blocks.append(block) + + # Step 6: Extract features from each sub-block + i = 0 + features = [] + center_features = [] + for block in blocks: + # Compute the mean and standard deviation of each color channel + mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1)) + std_y, std_cr, std_cb = np.std(block, axis=(0, 1)) + features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + + if (i == 28) or (i == 29) or (i == 36) or (i == 37): + center_features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + i += 1 + + # Step 7: Concatenate features into a single vector + cld_full_picture = np.array(features) + cld_center = np.array(center_features) + + return (cld_full_picture, cld_center) + + +def colorlayoutCompare(cld1, cld2): + distance = np.linalg.norm(cld1 - cld2) + + similarity = 1.0 / (1.0 + distance) + + return (distance, similarity) + + +def retrieval(choice="3"): + print("testing 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": + img_input = cv.imread("beach.jpg") + print("test: %s - beach" % choice) + if choice == "2": + img_input = cv.imread("building.jpg") + print("test: %s - building" % choice) + if choice == "3": + img_input = cv.imread("bus.jpg") + print("test: %s - bus" % choice) + if choice == "4": + img_input = cv.imread("dinosaur.jpg") + print("test: %s - dinosaur" % choice) + if choice == "5": + img_input = cv.imread("flower.jpg") + print("test: %s - flower" % choice) + if choice == "6": + img_input = cv.imread("horse.jpg") + print("test: %s - horse" % choice) + if choice == "7": + img_input = cv.imread("man.jpg") + print("test: %s - man" % choice) + + min_diff = 1e50 + # src_input = cv.imread("man.jpg") + # cv.imshow("Input", img_input) + # change the image to gray scale + # src_gray = cv.cvtColor(img_input, cv.COLOR_BGR2GRAY) + + # read image database + database = sorted(glob(database_dir + "/*.jpg")) + + descriptors = [] + + (cld1, cld1_center) = color_layout_descriptor(img_input) + for img in database: + print(f"processing {img}", end="\r") + + # read image + img_rgb = cv.imread(img) + (cld2, cld2_center) = color_layout_descriptor(img_rgb) + + # compare the two images + # diff = compareImgs(img_input, img_rgb) + (diff_full, s_full) = colorlayoutCompare(cld1, cld2) + (diff_center, s_center) = colorlayoutCompare(cld1_center, cld2_center) + + # compare the two images by histogram, uncomment the following line to use histogram + # diff = compareImgs_hist(src_gray, img_gray) + # print(img, diff) + + len_good_matches = SIFT_compare(img_input, img_rgb) + + descriptors.append([img, img_rgb, s_full, s_center, len_good_matches]) + print("\nprocess done") + + normalized_descriptors = [] + max_s_full = max(descriptors, key=lambda x: x[2])[2] + min_s_full = min(descriptors, key=lambda x: x[2])[2] + + max_s_center = max(descriptors, key=lambda x: x[3])[3] + min_s_center = min(descriptors, key=lambda x: x[3])[3] + + max_good_matches = max(descriptors, key=lambda x: x[4])[4] + min_good_matches = min(descriptors, key=lambda x: x[4])[4] + + for descriptor in descriptors: + normalized_s_full = (descriptor[2] - min_s_full) / (max_s_full - min_s_full) + normalized_s_center = (descriptor[3] - min_s_center) / (max_s_center - min_s_center) + normalized_good_matches = (descriptor[4] - min_good_matches) / (max_good_matches - min_good_matches) + + normalized_descriptors.append([descriptor[0], descriptor[1], normalized_s_full, normalized_s_center, normalized_good_matches]) + + print("\nnormalized descriptors done") + + # sort by s_full, largest first + normalized_descriptors.sort(key=lambda x: x[2]) + result = normalized_descriptors[0] + closest_img = result[1] + + # get feature match + diff_with_matches = [] + for descriptor in normalized_descriptors[0:5]: + # SIFT_debug(img_input, diff[2]) + full_file_name = descriptor[0] + img = descriptor[1] + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + len_good_matches = SIFT_compare(img_input, img) + + diff_with_matches.append([full_file_name, img, len_good_matches, 0, s_full, s_center]) + + matches = sorted(diff_with_matches, key=lambda x: x[4], reverse=True) + for match in matches: + pprint((match[0], match[2], match[4], match[5])) + + [full_file_name, closest_img, len_good_matches, _, s_full, s_center] = matches[0] + + # 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() + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + + print("f:" + filename_only + ": c:" + category) + + return category + + +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 SIFT_debug(img1, img2): + # 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 SIFT_compare(img1, img2): + # 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() + return len(good_matches) + + +def test(): + test_result = True + # 1, 2, 4, 7 + for i in [1]: + if str(i) == retrieval(str(i)): + print("test ok") + else: + test_result = False + + if test_result: + print("all test ok") + pass + else: + print("some test failed") + pass + + +def main(): + + test() + sys.exit() + + 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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_working.py b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_working.py new file mode 100644 index 00000000..572c945d --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/demo_draft2_working.py @@ -0,0 +1,328 @@ +import cv2 as cv +import numpy as np +from glob import glob +import os, sys +from pprint import pprint + +# 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 color_layout_descriptor(image, num_blocks=8, resize_to_px=256): + resized_image = cv.resize(image, (resize_to_px, resize_to_px)) + ycrcb_image = cv.cvtColor(resized_image, cv.COLOR_BGR2YCrCb) + + # Step 5: Divide the image into sub-blocks + block_size = int(256 / num_blocks) + blocks = [] + for i in range(num_blocks): + for j in range(num_blocks): + block = ycrcb_image[ + i * block_size : (i + 1) * block_size, + j * block_size : (j + 1) * block_size, + ] + blocks.append(block) + + # Step 6: Extract features from each sub-block + features = [] + for block in blocks: + # Compute the mean and standard deviation of each color channel + mean_y, mean_cr, mean_cb = np.mean(block, axis=(0, 1)) + std_y, std_cr, std_cb = np.std(block, axis=(0, 1)) + features.extend([mean_y, mean_cr, mean_cb, std_y, std_cr, std_cb]) + + # Step 7: Concatenate features into a single vector + cld_vector = np.array(features) + + return cld_vector + + +def colorlayoutCompare(cld1, cld2): + distance = np.linalg.norm(cld1 - cld2) + + similarity = 1.0 / (1.0 + distance) + + return (distance, similarity) + + +def retrieval(choice="3"): + print("testing 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": + img_input = cv.imread("beach.jpg") + print("test: %s - beach" % choice) + if choice == "2": + img_input = cv.imread("building.jpg") + print("test: %s - building" % choice) + if choice == "3": + img_input = cv.imread("bus.jpg") + print("test: %s - bus" % choice) + if choice == "4": + img_input = cv.imread("dinosaur.jpg") + print("test: %s - dinosaur" % choice) + if choice == "5": + img_input = cv.imread("flower.jpg") + print("test: %s - flower" % choice) + if choice == "6": + img_input = cv.imread("horse.jpg") + print("test: %s - horse" % choice) + if choice == "7": + img_input = cv.imread("man.jpg") + print("test: %s - man" % choice) + + min_diff = 1e50 + # src_input = cv.imread("man.jpg") + # cv.imshow("Input", img_input) + # change the image to gray scale + # src_gray = cv.cvtColor(img_input, cv.COLOR_BGR2GRAY) + + # read image database + database = sorted(glob(database_dir + "/*.jpg")) + + diff_array = [] + + cld1 = color_layout_descriptor(img_input) + for img in database: + print(f"processing {img}", end="\r") + + # read image + img_rgb = cv.imread(img) + cld2 = color_layout_descriptor(img_rgb) + + # compare the two images + # diff = compareImgs(img_input, img_rgb) + (d, s) = colorlayoutCompare(cld1, cld2) + diff = d + + # compare the two images by histogram, uncomment the following line to use histogram + # diff = compareImgs_hist(src_gray, img_gray) + # print(img, diff) + diff_array.append([img, diff, img_rgb]) + + print("") + diff_array.sort(key=lambda x: x[1]) + result = diff_array[0] + min_diff = result[1] + closest_img = result[2] + + # get feature match + diff_with_matches = [] + i = 0 + for diff in diff_array[0:20]: + i += 1 + # SIFT_debug(img_input, diff[2]) + full_file_name = diff[0] + img = diff[2] + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + len_good_matches = SIFT_compare(img_input, img) + + diff_with_matches.append([full_file_name, len_good_matches, d, img]) + + matches = sorted(diff_with_matches, key=lambda x: x[1], reverse=True) + for match in matches: + pprint((match[0], match[1], match[2])) + + [full_file_name, len_good_matches, d, closest_img] = matches[0] + + # 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() + + filename_only = full_file_name.replace("image.orig/", "") + category = filename_only[0] + + print("f:" + filename_only + ": c:" + category) + + return category + + +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 SIFT_debug(img1, img2): + # 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 SIFT_compare(img1, img2): + # 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() + return len(good_matches) + + +def test(): + test_result = True + # 1, 2, 4, 7 + for i in [5]: + if str(i) == retrieval(str(i)): + print("test ok") + else: + test_result = False + + if test_result: + print("all test ok") + pass + else: + print("some test failed") + pass + + +def main(): + + test() + sys.exit() + + 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() diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/descriptor.csv b/vinniesniper-54816/task1/_ref/2022-Codes-Python/descriptor.csv new file mode 100644 index 00000000..70df3afc --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/descriptor.csv @@ -0,0 +1,1001 @@ +image,similarity_full,similarity_center,good_matches 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b/vinniesniper-54816/task1/_ref/2022-Codes-Python/image.orig/tidy_filename.py @@ -0,0 +1,7 @@ +import os + +for filename in os.listdir(): + if filename.endswith(".jpg"): + num = int(os.path.splitext(filename)[0]) + new_filename = "{:03d}.jpg".format(num) + os.rename(filename, new_filename) diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/man.jpg b/vinniesniper-54816/task1/_ref/2022-Codes-Python/man.jpg new file mode 100644 index 00000000..f301cc50 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/man.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0da170ff216ff8747715d995248d6ecb85103daac7ccc7e7e80449dba9ebfa3 +size 74574 diff --git a/vinniesniper-54816/task1/_ref/2022-Codes-Python/test.sh b/vinniesniper-54816/task1/_ref/2022-Codes-Python/test.sh new file mode 100755 index 00000000..e8831b50 --- /dev/null +++ b/vinniesniper-54816/task1/_ref/2022-Codes-Python/test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -x + +rm -rf **/__pycache__ __pycache__ + +python ./demo_draft2.py + +rm -rf **/__pycache__ __pycache__ + diff --git a/vinniesniper-54816/task1/_tools/.gitignore b/vinniesniper-54816/task1/_tools/.gitignore new file mode 100644 index 00000000..1ff99840 --- /dev/null +++ b/vinniesniper-54816/task1/_tools/.gitignore @@ -0,0 +1 @@ +./data/*.xml \ No newline at end of file