From de8a39669e1b6cbb2706e3025699f6fcd30e9faa Mon Sep 17 00:00:00 2001 From: yoyoyo-yo Date: Wed, 28 Aug 2019 07:14:28 +0900 Subject: [PATCH] add --- Question_51_60/README.md | 4 +- Question_51_60/answers/answer_51.jpg | Bin 14645 -> 14462 bytes Question_51_60/answers/answer_51.py | 158 ++++++++++++++++---------- Question_51_60/answers/answer_52.jpg | Bin 10812 -> 10963 bytes Question_51_60/answers/answer_52.py | 158 ++++++++++++++++---------- Question_51_60/answers/answer_53.jpg | Bin 10963 -> 14352 bytes Question_51_60/answers/answer_53.py | 161 +++++++++++++++++---------- Question_51_60/answers/answer_54.py | 55 ++++++--- Question_51_60/answers/answer_55.py | 54 ++++++--- Question_51_60/answers/answer_56.py | 57 ++++++---- Question_51_60/answers/answer_57.jpg | Bin 11067 -> 11039 bytes Question_51_60/answers/answer_57.py | 70 ++++++++---- Question_51_60/answers/answer_58.py | 117 +++++++++++-------- Question_51_60/answers/answer_59.py | 122 +++++++++++--------- Question_51_60/answers/answer_60.py | 14 ++- README.md | 3 +- 16 files changed, 622 insertions(+), 351 deletions(-) diff --git a/Question_51_60/README.md b/Question_51_60/README.md index 88ef330d..1ef42d80 100644 --- a/Question_51_60/README.md +++ b/Question_51_60/README.md @@ -120,7 +120,7 @@ NCCは照明変化に強いと言われる。 ZNCC(Zero means Normalized Cross Correlation)とは零平均正規化相互相関を類似度にする手法であり、Sが**最大**の位置がマッチング位置となる。 -画像Iの平均値をmi、画像Tの平均値をmtとすると、Sは次式で計算される。 +画像Iの平均値をmi、画像Tの平均値をmtとすると、Sは次式で計算される。（ただし、平均値はRGB成分ごとに減算する） ```bash Sum_{x=0:w, y=0:h} (I(i+x, j+y)-mi) * (T(x, y)-mt) @@ -129,7 +129,7 @@ S = ---------------------------------------------------------------------------- ``` このSは、-1<=S<=1をとる。 -ZNCCは平均値を引くことでNCCよりも照明変化に強いと言われる。（だが今回は検出が失敗する。） +ZNCCは平均値を引くことでNCCよりも照明変化に強いと言われる。 |入力 (imori.jpg) |テンプレート画像(imori_part.jpg)|出力(answers/answer_57.jpg)| |:---:|:---:|:---:| diff --git a/Question_51_60/answers/answer_51.jpg b/Question_51_60/answers/answer_51.jpg index 49869381e7ca9bc94fe10263f0172ca70f89d7fe..306e78bde8e435ebb6808b5ace882eb402cb7f1f 100644 GIT binary patch literal 14462 zcmbVyi$9b9`~Nx$X*r)_g{U-pR}PiK-lam4j*hV;IjmXUB`dQPIiEr(-mG#;LXOLE zIj)lSc8E;upvYm|qvp=G+xPDC`wxD%hq1?YzwX!Jx}MkbdS2J+msKue>&X)iClG7a zAP{TdAH?b?!WOZ1&7bSf>)Jop`gMO^H>_X3ZvDm$8#n&%S4MWzMj2U|jT<+~Z<3Y! zbHV>@mY3W7=gsO{g#0GNJ;c2=YZMWyod^^>_=f+D2|wZgU2E2^TfbqW41BLV{Dr!$ z@H5w~gP*hkehmEW2>5fvdc_S&JN6yfsC?E(MlVEV|NXRQoAi%XwW*%#0}Kva`6p6V zPEB1yQ){Q8(XQQlOwG(K4jwved+hiLJ9~$d=Pz7zadmU|@V)AH%|9S8=;p1^+hL?T zccUIe$Hd0PC!}XQ%FN1s{3PdjL19sG$&1pL)it$s_4GFljqM$sUER!g??3bp41OLO z{xZVm@h850pPc$JJtJIP`uR(=4E|nOWg|BI*)}Xff&}twN=P@mJpAKAQ)Q=Dq(w2$ z6uO3@%N%ZGH6{h{a@g1EvNVTwBX)J~_I+rqlad^RDqTfrQn}3>;52rWK0!~OlG3r< zhxw>}D`lZw2ak-KU~Qa9n^{FD{%pBDJ|vGv+c|y4D6usN1u@+pP;AMZ8K3=u)YF5ex8Ry|mDuLDrT_wu?qRjwKAYbAQV$T4? 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img.shape + out = img.copy() + + max_sigma = 0 + max_t = 0 + + # determine threshold + for _t in range(1, 255): + v0 = out[np.where(out < _t)] + m0 = np.mean(v0) if len(v0) > 0 else 0. + w0 = len(v0) / (H * W) + v1 = out[np.where(out >= _t)] + m1 = np.mean(v1) if len(v1) > 0 else 0. + w1 = len(v1) / (H * W) + sigma = w0 * w1 * ((m0 - m1) ** 2) + if sigma > max_sigma: + max_sigma = sigma + max_t = _t + + # Binarization + print("threshold >>", max_t) + th = max_t + out[out < th] = 0 + out[out >= th] = 255 + + return out + + +# Erosion +def Erode(img, Erode_time=1): + H, W = img.shape + out = img.copy() + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each erode + for i in range(Erode_time): + tmp = np.pad(out, (1, 1), 'edge') + # erode + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: + out[y-1, x-1] = 0 + + return out + + +# Dilation +def Dilate(img, Dil_time=1): + H, W = img.shape + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each dilate time + out = img.copy() + for i in range(Dil_time): + tmp = np.pad(out, (1, 1), 'edge') + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: + out[y-1, x-1] = 255 + + return out + + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape - -# Otsu binary -## Grayscale -out = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0] -out = out.astype(np.uint8) - -## Determine threshold of Otsu's binarization -max_sigma = 0 -max_t = 0 - -for _t in range(1, 255): - v0 = out[np.where(out < _t)[0]] - m0 = np.mean(v0) if len(v0) > 0 else 0. - w0 = len(v0) / (H * W) - v1 = out[np.where(out >= _t)[0]] - m1 = np.mean(v1) if len(v1) > 0 else 0. - w1 = len(v1) / (H * W) - sigma = w0 * w1 * ((m0 - m1) ** 2) - if sigma > max_sigma: - max_sigma = sigma - max_t = _t - -## Binarization -#print("threshold >>", max_t) -th = max_t -out[out < th] = 0 -out[out >= th] = 255 - -# Morphology filter -MF = np.array(((0, 1, 0), - (1, 0, 1), - (0, 1, 0)), dtype=np.int) - -# Morphology - erode -Erode_time = 1 -erode = out.copy() - -for i in range(Erode_time): - tmp = np.pad(out, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: - erode[y-1, x-1] = 0 - -# Morphology - dilate -Dil_time = 1 -dilate = out.copy() - -for i in range(Dil_time): - tmp = np.pad(out, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: - dilate[y-1, x-1] = 255 - -out = np.abs(erode - dilate) * 255 + +# Grayscale +gray = BGR2GRAY(img) + +# Otsu's binarization +otsu = otsu_binarization(gray) + +# Erode image +eroded = Erode(otsu) + +# Delate image +dilated = Dilate(otsu) + +# Morphology +out = np.abs(eroded - dilated) * 255 # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_52.jpg 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zHG!A8mD|aolMnU{80DWYm)*&3t3{eWSj0WjS|HZ$;XbX0R<+XLyGoEQe<*bs5e(m8 yv>&kST?_qpH>i>vTHy6Z#-SiL%TapPhqpvhVcd&Lo3j^2j%cL40^7QlhyM>>U=5=H diff --git a/Question_51_60/answers/answer_52.py b/Question_51_60/answers/answer_52.py index af52f43f..2d7fb2fa 100644 --- a/Question_51_60/answers/answer_52.py +++ b/Question_51_60/answers/answer_52.py @@ -2,64 +2,110 @@ import numpy as np import matplotlib.pyplot as plt +# Gray scale +def BGR2GRAY(img): + b = img[:, :, 0].copy() + g = img[:, :, 1].copy() + r = img[:, :, 2].copy() + + # Gray scale + out = 0.2126 * r + 0.7152 * g + 0.0722 * b + out = out.astype(np.uint8) + + return out + +# Otsu Binalization +def otsu_binarization(img, th=128): + H, W = img.shape + out = img.copy() + + max_sigma = 0 + max_t = 0 + + # determine threshold + for _t in range(1, 255): + v0 = out[np.where(out < _t)] + m0 = np.mean(v0) if len(v0) > 0 else 0. + w0 = len(v0) / (H * W) + v1 = out[np.where(out >= _t)] + m1 = np.mean(v1) if len(v1) > 0 else 0. + w1 = len(v1) / (H * W) + sigma = w0 * w1 * ((m0 - m1) ** 2) + if sigma > max_sigma: + max_sigma = sigma + max_t = _t + + # Binarization + print("threshold >>", max_t) + th = max_t + out[out < th] = 0 + out[out >= th] = 255 + + return out + + +# Erosion +def Erode(img, Erode_time=1): + H, W = img.shape + out = img.copy() + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each erode + for i in range(Erode_time): + tmp = np.pad(out, (1, 1), 'edge') + # erode + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: + out[y-1, x-1] = 0 + + return out + + +# Dilation +def Dilate(img, Dil_time=1): + H, W = img.shape + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each dilate time + out = img.copy() + for i in range(Dil_time): + tmp = np.pad(out, (1, 1), 'edge') + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: + out[y-1, x-1] = 255 + + return out + +# Opening morphology +def Morphology_Opening(img, time=1): + dil = Dilate(img, Dil_time=time) + erode = Erode(dil, Erode_time=time) + return erode + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape - -# Otsu binary -## Grayscale -out = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0] -out = out.astype(np.uint8) - -## Determine threshold of Otsu's binarization -max_sigma = 0 -max_t = 0 - -for _t in range(1, 255): - v0 = out[np.where(out < _t)] - m0 = np.mean(v0) if len(v0) > 0 else 0. - w0 = len(v0) / (H * W) - v1 = out[np.where(out >= _t)] - m1 = np.mean(v1) if len(v1) > 0 else 0. - w1 = len(v1) / (H * W) - sigma = w0 * w1 * ((m0 - m1) ** 2) - if sigma > max_sigma: - max_sigma = sigma - max_t = _t - -## Binarization -#print("threshold >>", max_t) -th = max_t -out[out < th] = 0 -out[out >= th] = 255 - -# Morphology filter -MF = np.array(((0, 1, 0), - (1, 0, 1), - (0, 1, 0)), dtype=np.int) - -# Morphology - erode -Erode_time = 3 -mor = out.copy() - -for i in range(Erode_time): - tmp = np.pad(mor, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: - mor[y-1, x-1] = 0 - -# Morphology - dilate -Dil_time = 3 - -for i in range(Dil_time): - tmp = np.pad(mor, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: - mor[y-1, x-1] = 255 - -out = out - mor + +# Grayscale +gray = BGR2GRAY(img) + +# Otsu's binarization +otsu = otsu_binarization(gray) + +# Opening process +opened = Morphology_Opening(otsu, time=3) + +# Tophat +out = np.abs(otsu - opened) * 255 # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_53.jpg b/Question_51_60/answers/answer_53.jpg index 073dc7341cb1861f617834aa0057fc82ed4920d6..4129ba5d6a4374e25015928b98074f68f9007002 100644 GIT binary patch literal 14352 zcmb7ri9b~P|NnF=LK?dwrmPjitt^#gDiKLT-N-tXWF2$e$jF#e_FZu!+)VZ&yCF*^ 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ze&n|_ir@cV&#PKZ^Bm~+!#5kFc$t%&{CJ43aoPTmkM$Q^86zvIZ1dd?o!!>*b)M>{ z20UgBylr7#E7txz{rW6EL?iYi-t7MP1~uU zR`%r0z_J3YO0V601PgMtI6bbX;l0l2x5EzrRY z>gk_0nAc0@$banB%NkS+8(6jt-2tj~ajXw3E7>^^YAFs#_mJN~r%e#^wOk2BNE#I* ziWz^2iA4wNKUBGxe8dDwYT^rtR{IV-5_;b@^67ur?6L0?x>?H)-mS|&%bgtS>A*C^ zW;xr6c8HG(6f?S_sG2vuKnmnl z_HP^`>MV6~Za14fdU|ehcFiuoW~7nfzgs?5SH1H@XWkh)-7{w0BYg2DudHa<*#84o CCZmP` diff --git a/Question_51_60/answers/answer_53.py b/Question_51_60/answers/answer_53.py index 9b4d98c1..21d525fb 100644 --- a/Question_51_60/answers/answer_53.py +++ b/Question_51_60/answers/answer_53.py @@ -2,64 +2,113 @@ import numpy as np import matplotlib.pyplot as plt + +# Gray scale +def BGR2GRAY(img): + b = img[:, :, 0].copy() + g = img[:, :, 1].copy() + r = img[:, :, 2].copy() + + # Gray scale + out = 0.2126 * r + 0.7152 * g + 0.0722 * b + out = out.astype(np.uint8) + + return out + +# Otsu Binalization +def otsu_binarization(img, th=128): + H, W = img.shape + out = img.copy() + + max_sigma = 0 + max_t = 0 + + # determine threshold + for _t in range(1, 255): + v0 = out[np.where(out < _t)] + m0 = np.mean(v0) if len(v0) > 0 else 0. + w0 = len(v0) / (H * W) + v1 = out[np.where(out >= _t)] + m1 = np.mean(v1) if len(v1) > 0 else 0. + w1 = len(v1) / (H * W) + sigma = w0 * w1 * ((m0 - m1) ** 2) + if sigma > max_sigma: + max_sigma = sigma + max_t = _t + + # Binarization + print("threshold >>", max_t) + th = max_t + out[out < th] = 0 + out[out >= th] = 255 + + return out + + +# Erosion +def Erode(img, Erode_time=1): + H, W = img.shape + out = img.copy() + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each erode + for i in range(Erode_time): + tmp = np.pad(out, (1, 1), 'edge') + # erode + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: + out[y-1, x-1] = 0 + + return out + + +# Dilation +def Dilate(img, Dil_time=1): + H, W = img.shape + + # kernel + MF = np.array(((0, 1, 0), + (1, 0, 1), + (0, 1, 0)), dtype=np.int) + + # each dilate time + out = img.copy() + for i in range(Dil_time): + tmp = np.pad(out, (1, 1), 'edge') + for y in range(1, H+1): + for x in range(1, W+1): + if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: + out[y-1, x-1] = 255 + + return out + +# Closing morphology +def Morphology_Closing(img, time=1): + erode = Erode(img, Erode_time=time) + dil = Dilate(erode, Dil_time=time) + return erode + + + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape - -# Otsu binary -## Grayscale -out = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0] -out = out.astype(np.uint8) - -## Determine threshold of Otsu's binarization -max_sigma = 0 -max_t = 0 - -for _t in range(1, 255): - v0 = out[np.where(out < _t)] - m0 = np.mean(v0) if len(v0) > 0 else 0. - w0 = len(v0) / (H * W) - v1 = out[np.where(out >= _t)] - m1 = np.mean(v1) if len(v1) > 0 else 0. - w1 = len(v1) / (H * W) - sigma = w0 * w1 * ((m0 - m1) ** 2) - if sigma > max_sigma: - max_sigma = sigma - max_t = _t - -## Binarization -#print("threshold >>", max_t) -th = max_t -out[out < th] = 0 -out[out >= th] = 255 - -# Morphology filter -MF = np.array(((0, 1, 0), - (1, 0, 1), - (0, 1, 0)), dtype=np.int) - -# Morphology - dilate -Dil_time = 3 -mor = out.copy() - -for i in range(Dil_time): - tmp = np.pad(mor, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) >= 255: - mor[y-1, x-1] = 255 - -# Morphology - erode -Erode_time = 3 - -for i in range(Erode_time): - tmp = np.pad(mor, (1, 1), 'edge') - for y in range(1, H+1): - for x in range(1, W+1): - if np.sum(MF * tmp[y-1:y+2, x-1:x+2]) < 255*4: - mor[y-1, x-1] = 0 - -out = mor - out + +# Grayscale +gray = BGR2GRAY(img) + +# Otsu's binarization +otsu = otsu_binarization(gray) + +# Opening process +opened = Morphology_Closing(otsu, time=3) + +# Tophat +out = np.abs(opened - otsu) * 255 # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_54.py b/Question_51_60/answers/answer_54.py index 73c06b32..37703725 100644 --- a/Question_51_60/answers/answer_54.py +++ b/Question_51_60/answers/answer_54.py @@ -2,28 +2,47 @@ import numpy as np import matplotlib.pyplot as plt + +# template matching +def Template_matching(img, template): + # get original image shape + H, W, C = img.shape + + # get template image shape + Ht, Wt, Ct = template.shape + + # Templete matching + # prepare x, y index + i, j = -1, -1 + # prepare evaluate value + v = 255 * H * W * C + + for y in range(H - Ht): + for x in range(W - Wt): + # get SSD value + _v = np.sum((img[y : y + Ht, x : x + Wt] - template) ** 2) + + # if SSD is min + if _v < v: + v = _v + i, j = x, y + + out = img.copy() + # draw rectangle + cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) + out = out.astype(np.uint8) + + return out + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape # Read templete image -temp = cv2.imread("imori_part.jpg").astype(np.float32) -Ht, Wt, Ct = temp.shape - - -# Templete matching -i, j = -1, -1 -v = 255 * H * W * C -for y in range(H-Ht): - for x in range(W-Wt): - _v = np.sum((img[y:y+Ht, x:x+Wt] - temp) ** 2) - if _v < v: - v = _v - i, j = x, y - -out = img.copy() -cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) -out = out.astype(np.uint8) +template = cv2.imread("imori_part.jpg").astype(np.float32) + +# Template matching +out = Template_matching(img, template) + # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_55.py b/Question_51_60/answers/answer_55.py index 5b5443de..63435818 100644 --- a/Question_51_60/answers/answer_55.py +++ b/Question_51_60/answers/answer_55.py @@ -2,28 +2,46 @@ import numpy as np import matplotlib.pyplot as plt +# template matching +def Template_matching(img, template): + # get original image shape + H, W, C = img.shape + + # get template image shape + Ht, Wt, Ct = template.shape + + # Templete matching + # prepare x, y index + i, j = -1, -1 + # prepare evaluate value + v = 255 * H * W * C + + for y in range(H - Ht): + for x in range(W - Wt): + # get SAD value + _v = np.sum(np.abs(img[y : y + Ht, x : x + Wt] - template)) + + # if SAD is min + if _v < v: + v = _v + i, j = x, y + + out = img.copy() + # draw rectangle + cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) + out = out.astype(np.uint8) + + return out + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape # Read templete image -temp = cv2.imread("imori_part.jpg").astype(np.float32) -Ht, Wt, Ct = temp.shape - - -# Templete matching -i, j = -1, -1 -v = 255 * H * W * C -for y in range(H-Ht): - for x in range(W-Wt): - _v = np.sum(np.abs(img[y:y+Ht, x:x+Wt] - temp)) - if _v < v: - v = _v - i, j = x, y - -out = img.copy() -cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) -out = out.astype(np.uint8) +template = cv2.imread("imori_part.jpg").astype(np.float32) + +# Template matching +out = Template_matching(img, template) + # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_56.py b/Question_51_60/answers/answer_56.py index dae7c407..fb3a0cbb 100644 --- a/Question_51_60/answers/answer_56.py +++ b/Question_51_60/answers/answer_56.py @@ -2,29 +2,48 @@ import numpy as np import matplotlib.pyplot as plt +# template matching +def Template_matching(img, template): + # get original image shape + H, W, C = img.shape + + # get template image shape + Ht, Wt, Ct = template.shape + + # Templete matching + # prepare x, y index + i, j = -1, -1 + # prepare evaluate value + v = -1 + + for y in range(H - Ht): + for x in range(W - Wt): + # get NCC value + # get numerator of NCC + _v = np.sum(img[y : y + Ht, x : x + Wt] * template) + # devided numerator + _v /= (np.sqrt(np.sum(img[y : y + Ht, x : x + Wt] ** 2)) * np.sqrt(np.sum(template ** 2))) + + # if NCC is max + if _v > v: + v = _v + i, j = x, y + + out = img.copy() + # draw rectangle + cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) + out = out.astype(np.uint8) + + return out + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape # Read templete image -temp = cv2.imread("imori_part.jpg").astype(np.float32) -Ht, Wt, Ct = temp.shape - - -# Templete matching -i, j = -1, -1 -v = -1 -for y in range(H-Ht): - for x in range(W-Wt): - _v = np.sum(img[y:y+Ht, x:x+Wt] * temp) - _v /= (np.sqrt(np.sum(img[y:y+Ht, x:x+Wt]**2)) * np.sqrt(np.sum(temp**2))) - if _v > v: - v = _v - i, j = x, y - -out = img.copy() -cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) -out = out.astype(np.uint8) +template = cv2.imread("imori_part.jpg").astype(np.float32) + +# Template matching +out = Template_matching(img, template) # Save result cv2.imwrite("out.jpg", out) diff --git a/Question_51_60/answers/answer_57.jpg 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zb4=jxERfHn$8kY-rjlfZ%wWV1G;}RJ6@68~VZ_e^yT}x&>Cdgr^tRU9huu55hCrK? z0`yczyhBQA5p6-LA+i$QCg=n7wz6ZQ&uAZt-U6-F1$JddX1#eP(bz4rxJR6f;faA>Nul!oY11*6WTt{*&CHzn1hmM_(1 z<&YkKj-5O~&jWj}6GKqUf1138@&~N=Q3w$RkdeH#2|BRCxOXJOVA}Msd%sWO7a!GG z^K}V9Wr2wuUN^4fE)) zA6B(6m+K=`$<&q^cM#;(^;|V{<*HKE&HJ*K|HG&oI62!_Xo;s zgDy#$H$^$5ktkR+8I`F}6fLmAdA5m~YTE7P*8b)NcINqO0#2&K0%X!*y;$v8n;bu| z0c03XPi{q9_8Z^CyJQ5C4=wweT%AHF=8x{!G-5N`w)9`rIbs`v)G>#3Ui#6 zo*K}Yyd{HL&G-l8H-ekKGbK~Le(EQHe>wW1lDVK}mQ(){nn&b(fHZqH35 zKArJQZI#R}zEFA;7uG?8nQj_Ik?VTd-Qzh$j(BWW-MYXOB^{$)6 v: + v = _v + i, j = x, y + + out = img.copy() + # draw rectangle + cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) + out = out.astype(np.uint8) + + return out + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape - -mi = np.mean(img) # Read templete image -temp = cv2.imread("imori_part.jpg").astype(np.float32) -Ht, Wt, Ct = temp.shape - -mt = np.mean(temp) - -# Templete matching -i, j = -1, -1 -v = -1 -for y in range(H-Ht): - for x in range(W-Wt): - _v = np.sum((img[y:y+Ht, x:x+Wt]-mi) * (temp-mt)) - _v /= (np.sqrt(np.sum((img[y:y+Ht, x:x+Wt]-mi)**2)) * np.sqrt(np.sum((temp-mt)**2))) - if _v > v: - v = _v - i, j = x, y - -out = img.copy() -cv2.rectangle(out, pt1=(i, j), pt2=(i+Wt, j+Ht), color=(0,0,255), thickness=1) -out = out.astype(np.uint8) - +template = cv2.imread("imori_part.jpg").astype(np.float32) + +# Template matching +out = Template_matching(img, template) + + # Save result cv2.imwrite("out.jpg", out) cv2.imshow("result", out) diff --git a/Question_51_60/answers/answer_58.py b/Question_51_60/answers/answer_58.py index 160c3995..3ca9890d 100644 --- a/Question_51_60/answers/answer_58.py +++ b/Question_51_60/answers/answer_58.py @@ -2,56 +2,79 @@ import numpy as np import matplotlib.pyplot as plt -# Read image -img = cv2.imread("seg.png").astype(np.float32) -H, W, C = img.shape - -label = np.zeros((H, W), dtype=np.int) -label[img[..., 0]>0] = 1 - -LUT = [0 for _ in range(H*W)] - -n = 1 - -for y in range(H): - for x in range(W): - if label[y, x] == 0: - continue - c3 = label[max(y-1,0), x] - c5 = label[y, max(x-1,0)] - if c3 < 2 and c5 < 2: - n += 1 - label[y, x] = n - else: - _vs = [c3, c5] - vs = [a for a in _vs if a > 1] - v = min(vs) - label[y, x] = v +# labeling 4 nearest neighbor +def labeling_4nn(img): + # get image shape + H, W, C = img.shape + + # prepare label tempolary image + label = np.zeros((H, W), dtype=np.int) + label[img[..., 0]>0] = 1 + + # look up table + LUT = [0 for _ in range(H*W)] + + n = 1 + + for y in range(H): + for x in range(W): + # skip black pixel + if label[y, x] == 0: + continue - minv = v - for _v in vs: - if LUT[_v] != 0: - minv = min(minv, LUT[_v]) - for _v in vs: - LUT[_v] = minv + # get above pixel + c3 = label[max(y-1,0), x] + + # get left pixel + c5 = label[y, max(x-1,0)] + + # if not labeled + if c3 < 2 and c5 < 2: + # labeling + n += 1 + label[y, x] = n + else: + # replace min label index + _vs = [c3, c5] + vs = [a for a in _vs if a > 1] + v = min(vs) + label[y, x] = v -count = 1 - -for l in range(2, n+1): - flag = True - for i in range(n+1): - if LUT[i] == l: - if flag: - count += 1 - flag = False - LUT[i] = count - -COLORS = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] -out = np.zeros((H, W, C), dtype=np.uint8) - -for i, lut in enumerate(LUT[2:]): - out[label == (i+2)] = COLORS[lut-2] + minv = v + for _v in vs: + if LUT[_v] != 0: + minv = min(minv, LUT[_v]) + for _v in vs: + LUT[_v] = minv + + count = 1 + + # integrate index of look up table + for l in range(2, n+1): + flag = True + for i in range(n+1): + if LUT[i] == l: + if flag: + count += 1 + flag = False + LUT[i] = count + + # draw color + COLORS = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] + out = np.zeros((H, W, C), dtype=np.uint8) + + for i, lut in enumerate(LUT[2:]): + out[label == (i+2)] = COLORS[lut-2] + + return out + +# Read image +img = cv2.imread("seg.png").astype(np.float32) + +# labeling 4 nearest neighbor +out = labeling_4nn(img) + # Save result cv2.imwrite("out.png", out) cv2.imshow("result", out) diff --git a/Question_51_60/answers/answer_59.py b/Question_51_60/answers/answer_59.py index a5431167..752e09b3 100644 --- a/Question_51_60/answers/answer_59.py +++ b/Question_51_60/answers/answer_59.py @@ -2,58 +2,80 @@ import numpy as np import matplotlib.pyplot as plt + +# labeling 8 nearest neighbor +def labeling_8nn(img): + # get image shape + H, W, C = img.shape + + # prepare labeling image + label = np.zeros((H, W), dtype=np.int) + label[img[..., 0]>0] = 1 + + # look up table + LUT = [0 for _ in range(H*W)] + + n = 1 + + for y in range(H): + for x in range(W): + if label[y, x] == 0: + continue + # get right top pixel + c2 = label[max(y-1,0), min(x+1, W-1)] + # get top pixel + c3 = label[max(y-1,0), x] + # get left top pixel + c4 = label[max(y-1,0), max(x-1,0)] + # get left pixel + c5 = label[y, max(x-1,0)] + + # if all pixel is non labeled + if c3 < 2 and c5 < 2 and c2 < 2 and c4 < 2: + n += 1 + label[y, x] = n + else: + # get labeled index + _vs = [c3, c5, c2, c4] + vs = [a for a in _vs if a > 1] + v = min(vs) + label[y, x] = v + + minv = v + for _v in vs: + if LUT[_v] != 0: + minv = min(minv, LUT[_v]) + for _v in vs: + LUT[_v] = minv + + count = 1 + + # integrate labeled index of look up table + for l in range(2, n+1): + flag = True + for i in range(n+1): + if LUT[i] == l: + if flag: + count += 1 + flag = False + LUT[i] = count + + # draw color + COLORS = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] + out = np.zeros((H, W, C), dtype=np.uint8) + + for i, lut in enumerate(LUT[2:]): + out[label == (i+2)] = COLORS[lut-2] + + return out + + # Read image img = cv2.imread("seg.png").astype(np.float32) -H, W, C = img.shape - -label = np.zeros((H, W), dtype=np.int) -label[img[..., 0]>0] = 1 - -LUT = [0 for _ in range(H*W)] - -n = 1 - -for y in range(H): - for x in range(W): - if label[y, x] == 0: - continue - c2 = label[max(y-1,0), min(x+1, W-1)] - c3 = label[max(y-1,0), x] - c4 = label[max(y-1,0), max(x-1,0)] - c5 = label[y, max(x-1,0)] - if c3 < 2 and c5 < 2 and c2 < 2 and c4 < 2: - n += 1 - label[y, x] = n - else: - _vs = [c3, c5, c2, c4] - vs = [a for a in _vs if a > 1] - v = min(vs) - label[y, x] = v - - minv = v - for _v in vs: - if LUT[_v] != 0: - minv = min(minv, LUT[_v]) - for _v in vs: - LUT[_v] = minv - -count = 1 - -for l in range(2, n+1): - flag = True - for i in range(n+1): - if LUT[i] == l: - if flag: - count += 1 - flag = False - LUT[i] = count - -COLORS = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] -out = np.zeros((H, W, C), dtype=np.uint8) - -for i, lut in enumerate(LUT[2:]): - out[label == (i+2)] = COLORS[lut-2] - + +# labeling 8 nearest neighbor +out = labeling_8nn(img) + # Save result cv2.imwrite("out.png", out) cv2.imshow("result", out) diff --git a/Question_51_60/answers/answer_60.py b/Question_51_60/answers/answer_60.py index 7003d590..bbac60d1 100644 --- a/Question_51_60/answers/answer_60.py +++ b/Question_51_60/answers/answer_60.py @@ -2,15 +2,21 @@ import numpy as np import matplotlib.pyplot as plt + +# alpha blend +def alpha_blend(img1, img2, alpha): + # blend + out = img * alpha + img2 * (1 - alpha) + out = out.astype(np.uint8) + return out + # Read image img = cv2.imread("imori.jpg").astype(np.float32) -H, W, C = img.shape +# Read blend target image img2 = cv2.imread("thorino.jpg").astype(np.float32) -a = 0.6 -out = img * a + img2 * (1 - a) -out = out.astype(np.uint8) +out = alpha_blend(img, img2, alpha=0.6) # Save result cv2.imwrite("out.jpg", out) diff --git a/README.md b/README.md index 7665825f..d87c3852 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,8 @@ PythonとC++の好きな方でやってみてね♡（最近Javascriptも追加 ## Recent -- 2019.8.18 [Python] Q.50までコードの処理をメソッド化 +- 2019.8.28 [Python] Q.51~60のAnswerコードをメソッド化 +- 2019.8.18 [Python] Q.50までのAnswerコードをメソッド化 - 2019.8.12 [C++]Q.36-40の解答追加 - 2019.7.32 [C++]Q.32-35の解答追加 - 2019.7.23 [C++]Q.30-31の解答追加