├── .gitignore
├── A-0004-01_P.pdf
├── AffineInvariantFeatures.py
├── LICENSE
├── README.md
├── RegionOfInterest.py
├── TemplateMatcher.py
├── avery_8254.pdf
├── classifier.pdf
├── classifier.pkl
├── classifier_alt_1.pkl
├── classifier_alt_2.pkl
├── classify.py
├── dns_badge.png
├── dns_logo.png
├── sample.jpg
├── setup.py
├── template.png
├── train.py
└── util.py
/.gitignore:
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/A-0004-01_P.pdf:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/A-0004-01_P.pdf
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/AffineInvariantFeatures.py:
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1 | import cv2
2 | import numpy as np
3 | import itertools as it
4 | from multiprocessing.pool import ThreadPool
5 | from sklearn.base import BaseEstimator, TransformerMixin
6 |
7 |
8 | class AffineInvariant(TransformerMixin, BaseEstimator):
9 | def __init__(self, detector, extractor):
10 | self.detector = detector
11 | self.extractor = extractor
12 | self.pool = ThreadPool(processes=cv2.getNumberOfCPUs())
13 |
14 | def affine_skew(self, tilt, phi, img, mask=None):
15 | h, w = img.shape[:2]
16 | if mask is None:
17 | mask = np.zeros((h, w), np.uint8)
18 | mask[:] = 255
19 | A = np.float32([[1, 0, 0], [0, 1, 0]])
20 | if phi != 0.0:
21 | phi = np.deg2rad(phi)
22 | s, c = np.sin(phi), np.cos(phi)
23 | A = np.float32([[c, -s], [s, c]])
24 | corners = [[0, 0], [w, 0], [w, h], [0, h]]
25 | tcorners = np.int32(np.dot(corners, A.T))
26 | x, y, w, h = cv2.boundingRect(tcorners.reshape(1, -1, 2))
27 | A = np.hstack([A, [[-x], [-y]]])
28 | img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
29 | if tilt != 1.0:
30 | s = 0.8*np.sqrt(tilt * tilt - 1)
31 | img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
32 | img = cv2.resize(img, (0, 0), fx=1.0 / tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
33 | A[0] /= tilt
34 | if phi != 0.0 or tilt != 1.0:
35 | h, w = img.shape[:2]
36 | mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
37 | Ai = cv2.invertAffineTransform(A)
38 | return img, mask, Ai
39 |
40 | def affine_detect(self, img, mask=None):
41 | params = [(1.0, 0.0)]
42 | for t in 2 ** (0.5 * np.arange(1, 6)):
43 | for phi in np.arange(0, 180, 72.0 / t):
44 | params.append((t, phi))
45 |
46 | def f(p):
47 | t, phi = p
48 | timg, tmask, Ai = self.affine_skew(t, phi, img)
49 | keypoints = self.detector.detect(timg, tmask)
50 | keypoints, descrs = self.extractor.compute(timg, keypoints)
51 | for kp in keypoints:
52 | x, y = kp.pt
53 | kp.pt = tuple(np.dot(Ai, (x, y, 1)))
54 | if descrs is None:
55 | descrs = np.zeros((0, 64), 'float32')
56 | return keypoints, descrs
57 |
58 | keypoints, descrs = [], []
59 | ires = it.imap(f, params)
60 |
61 | for i, (k, d) in enumerate(ires):
62 | keypoints.extend(k)
63 | descrs.extend(d)
64 |
65 | return keypoints, np.array(descrs)
66 |
67 | def extract_features(self, image):
68 | keypoints, descriptors = self.affine_detect(image)
69 | if descriptors is None or not len(descriptors):
70 | # treat failure to classify as negative sample
71 | return np.zeros((0, 64), 'float32')
72 |
73 | return descriptors
74 |
75 | def fit(self, X, y=None, **fit_params):
76 | return self
77 |
78 | def transform(self, X, **transform_params):
79 | l = dict(n=0)
80 |
81 | def f(image):
82 | l['n'] += 1
83 | if (100 * l['n'] / len(X)) % 5 == 0:
84 | print '\r%d%% - %d/%d' % (100 * l['n'] / len(X), l['n'], len(X)),
85 | return self.extract_features(image)
86 |
87 | ires = self.pool.imap(f, X)
88 | return np.array(list(ires))
89 |
--------------------------------------------------------------------------------
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674 | .
675 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # DoNotSnap
2 |
3 |
4 | An experiment in detecting DoNotSnap badges in photos, to protect privacy.
5 |
6 | 
7 |
8 | This program allows you to detect and identify DoNotSnap badges via a sliding-window decision tree classifier (custom heuristics are used to reduce search space).
9 | The classifier is trained by matching samples against image templates using Affine-transform invariant SURF features.
10 |
11 | You can find examples of using the classifier in `classify.py` and training a new classifier in `train.py`
12 |
13 | A pre-trained classifier can be found in `classifier.pkl`
14 | Alternative versions of the same classifier are in `classifier_alt_1.pkl` and `classifier_alt_2.pkl`
15 | ## Running classification
16 | Run `python classify.py `
17 | This will deserialize the classifier from `classifier.pkl` and run it on the image you supplied. A sample image could be found in `sample.jpg`
18 | ## Training your own classifier
19 | Run `python train.py `
20 | This will read the sample filenames from `positive.txt` and `negative.txt` files.
21 | Templates filenames are specified in `templates.txt`. A sample template could be found in `template.png`
22 | The output is a `.pkl` with serialized classifier.
23 | ## Dependencies
24 | * opencv
25 | * numpy
26 | * sklearn
27 | * matplotlib
28 | * PIL
29 |
30 | ## DoNotSnap sticker templates
31 | In the repository you can find templates for printing DoNoSnap stickers.
32 |
33 | `avery_8254.pdf` contains design compatible with US format paper. Compatible Avery templates:
34 | * 15664
35 | * 18664
36 | * 45464
37 | * 48264
38 | * 48464
39 | * 48864
40 | * 5164
41 | * 5264
42 | * 55164
43 | * 5524
44 | * 55264
45 | * 55364
46 | * 55464
47 | * 5664
48 | * 58164
49 | * 58264
50 | * 8164
51 | * 8254
52 | * 8464
53 | * 8564
54 | * 15264
55 | * 95940
56 | * 95905
57 |
58 | `A-0004-01_P.pdf` contains design compatible with A4-format paper. Compatible Avery templates:
59 | * J8165
60 | * J8165-10
61 | * J8165-25
62 | * J8165-40
63 | * J8165-100
64 | * J8165-250
65 | * J8165-500
66 | * J8365
67 | * J8565
68 | * J8565-25
69 | * L7165
70 | * L7165-40
71 | * L7165-100
72 | * L7165-250
73 | * L7165-500
74 | * LR7165-100
75 | * L7165X
76 | * L7165X-100
77 | * L7165X-250
78 | * L7565
79 | * L7565-25
80 | * L7965
81 | * L7965-100
82 | * L7993
83 | * L7993-25
84 | * LR7165
85 | * LR7165-100
86 |
--------------------------------------------------------------------------------
/RegionOfInterest.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import math
3 | import numpy as np
4 | from util import pyramid, sliding_window
5 |
6 |
7 | def boundingRects(scale, contours):
8 | for contour in contours:
9 | epsilon = 0.1 * cv2.arcLength(contour, True)
10 | approx = cv2.approxPolyDP(contour, epsilon, True)
11 | x, y, w, h = cv2.boundingRect(approx)
12 |
13 | yield [x * scale, y * scale, w * scale, h * scale]
14 |
15 |
16 | def extractEdges(hue, intensity):
17 | kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
18 |
19 | edges = cv2.Canny(intensity, 120, 140)
20 | hue_edges = cv2.Canny(cv2.GaussianBlur(hue, (5, 5), 0), 0, 255)
21 | combined_edges = cv2.bitwise_or(hue_edges, edges)
22 | _, mask = cv2.threshold(combined_edges, 40, 255, cv2.THRESH_BINARY)
23 | return cv2.erode(cv2.GaussianBlur(mask, (3, 3), 0), kernel, iterations=1)
24 |
25 |
26 | def roiFromEdges(edges):
27 | kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
28 | small_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
29 | # close gaps in edges to create continous regions
30 | roi = cv2.dilate(edges, small_kernel, iterations=14)
31 | return cv2.erode(roi, kernel, iterations=4)
32 |
33 |
34 | def findEllipses(edges):
35 | contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
36 | ellipseMask = np.zeros(edges.shape, dtype=np.uint8)
37 | contourMask = np.zeros(edges.shape, dtype=np.uint8)
38 |
39 | pi_4 = np.pi * 4
40 |
41 | for i, contour in enumerate(contours):
42 | if len(contour) < 5:
43 | continue
44 |
45 | area = cv2.contourArea(contour)
46 | if area <= 100: # skip ellipses smaller then 10x10
47 | continue
48 |
49 | arclen = cv2.arcLength(contour, True)
50 | circularity = (pi_4 * area) / (arclen * arclen)
51 | ellipse = cv2.fitEllipse(contour)
52 | poly = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])), (int(ellipse[1][0] / 2), int(ellipse[1][1] / 2)), int(ellipse[2]), 0, 360, 5)
53 |
54 | # if contour is circular enough
55 | if circularity > 0.6:
56 | cv2.fillPoly(ellipseMask, [poly], 255)
57 | continue
58 |
59 | # if contour has enough similarity to an ellipse
60 | similarity = cv2.matchShapes(poly.reshape((poly.shape[0], 1, poly.shape[1])), contour, cv2.cv.CV_CONTOURS_MATCH_I2, 0)
61 | if similarity <= 0.2:
62 | cv2.fillPoly(contourMask, [poly], 255)
63 |
64 | return ellipseMask, contourMask
65 |
66 |
67 | def findCircles(hue, intensity):
68 | houghCirclesMask = np.zeros(hue.shape, dtype=np.uint8)
69 |
70 | blurred_hue = cv2.GaussianBlur(hue, (9, 9), 2)
71 | blurred_intensity = cv2.GaussianBlur(intensity, (9, 9), 2)
72 | hue_circles = cv2.HoughCircles(blurred_hue, cv2.cv.CV_HOUGH_GRADIENT, 0.5, hue.shape[0] / 8, param1=10, param2=25, maxRadius=100)
73 | intensity_circles = cv2.HoughCircles(blurred_intensity, cv2.cv.CV_HOUGH_GRADIENT, 0.5, hue.shape[0] / 8, param1=185, param2=20, maxRadius=100)
74 |
75 | circles = np.vstack((hue_circles[0] if hue_circles is not None else np.empty((0, 3)),
76 | intensity_circles[0] if intensity_circles is not None else np.empty((0, 3))))
77 |
78 | for (x, y, r) in circles:
79 | cv2.circle(houghCirclesMask, (int(round(x)), int(round(y))), int(round(r)), 255, -1)
80 |
81 | return houghCirclesMask
82 |
83 |
84 | def findRANSACCircles(edges, circleSearches=5):
85 | edges = edges.copy()
86 | mask = np.zeros(edges.shape, dtype=np.uint8)
87 |
88 | minRadius = 10
89 | maxRadius = 100
90 |
91 | def verifyCircle(dt, center, radius):
92 | minInlierDist = 2.0
93 | maxInlierDistMax = 100.0
94 | maxInlierDist = max(minInlierDist, min(maxInlierDistMax, radius / 25.0))
95 |
96 | # choose samples along the circle and count inlier percentage
97 | samples = np.arange(0, 2 * np.pi, 0.05)
98 | cX = radius * np.cos(samples) + center[0]
99 | cY = radius * np.sin(samples) + center[1]
100 |
101 | coords = np.array((cX, cY)).T
102 | counter = len(samples)
103 |
104 | cXMask = (cX < dt.shape[1]) & (cX >= 0)
105 | cYMask = (cY < dt.shape[0]) & (cY >= 0)
106 | cMask = cXMask & cYMask
107 |
108 | gdt = dt[cY[cMask].astype(int), cX[cMask].astype(int)]
109 | dtMask = gdt < maxInlierDist
110 |
111 | inlierSet = coords[cMask][dtMask]
112 | inlier = len(inlierSet)
113 |
114 | return float(inlier) / counter, inlierSet
115 |
116 | def getCircle(p1, p2, p3):
117 | x1 = float(p1[0])
118 | x2 = float(p2[0])
119 | x3 = float(p3[0])
120 |
121 | y1 = float(p1[1])
122 | y2 = float(p2[1])
123 | y3 = float(p3[1])
124 |
125 | center_x = (x1 * x1 + y1 * y1) * (y2 - y3) + (x2 * x2 + y2 * y2) * (y3 - y1) + (x3 * x3 + y3 * y3) * (y1 - y2)
126 | x = 2 * (x1 * (y2 - y3) - y1 * (x2 - x3) + x2 * y3 - x3 * y2)
127 | if not x:
128 | return None, None
129 | center_x /= x
130 |
131 | center_y = (x1 * x1 + y1 * y1) * (x3 - x2) + (x2 * x2 + y2 * y2) * (x1 - x3) + (x3 * x3 + y3 * y3) * (x2 - x1)
132 | y = 2 * (x1 * (y2 - y3) - y1 * (x2 - x3) + x2 * y3 - x3 * y2)
133 | if not y:
134 | return None, None
135 | center_y /= y
136 |
137 | radius = math.sqrt((center_x - x1) * (center_x - x1) + (center_y - y1) * (center_y - y1))
138 |
139 | return (center_x, center_y), radius
140 |
141 | def getPointPositions(binaryImage):
142 | return [(x, y) for y, x in zip(*np.where(binaryImage > 0))]
143 |
144 | for _ in xrange(circleSearches):
145 | edgePositions = getPointPositions(edges)
146 |
147 | # create distance transform to efficiently evaluate distance to nearest edge
148 | dt = cv2.distanceTransform(255 - edges, cv2.cv.CV_DIST_L1, 3)
149 |
150 | bestCircleCenter = None
151 | bestCircleRadius = 0
152 | bestCirclePercentage = 0
153 |
154 | minCirclePercentage = 0.6 # at least 60% of a circle must be present
155 |
156 | maxNrOfIterations = len(edgePositions) # TODO: adjust this parameter or include some real ransac criteria with inlier/outlier percentages to decide when to stop
157 |
158 | for its in xrange(maxNrOfIterations):
159 | # RANSAC: randomly choose 3 point and create a circle:
160 |
161 | # TODO: choose randomly but more intelligent,
162 | # so that it is more likely to choose three points of a circle.
163 | # For example if there are many small circles, it is unlikely to randomly choose 3 points of the same circle.
164 | idx1 = np.random.randint(len(edgePositions))
165 | idx2 = np.random.randint(len(edgePositions))
166 | idx3 = np.random.randint(len(edgePositions))
167 |
168 | # we need 3 different samples:
169 | if idx1 == idx2 or idx1 == idx3 or idx3 == idx2:
170 | continue
171 |
172 | # create circle from 3 points:
173 | center, radius = getCircle(edgePositions[idx1], edgePositions[idx2], edgePositions[idx3])
174 | if not center or radius > maxRadius:
175 | continue
176 |
177 | # inlier set unused at the moment but could be used to approximate a (more robust) circle from alle inlier
178 | # verify or falsify the circle by inlier counting:
179 | cPerc, inlierSet = verifyCircle(dt, center, radius)
180 |
181 | if cPerc >= bestCirclePercentage and radius >= minRadius:
182 | bestCirclePercentage = cPerc
183 | bestCircleRadius = radius
184 | bestCircleCenter = center
185 |
186 | # draw if good circle was found
187 | if bestCirclePercentage >= minCirclePercentage and bestCircleRadius >= minRadius:
188 | cv2.circle(mask, (int(round(bestCircleCenter[0])), int(round(bestCircleCenter[1]))), int(round(bestCircleRadius)), 255, -1)
189 | # mask found circle
190 | cv2.circle(edges, (int(round(bestCircleCenter[0])), int(round(bestCircleCenter[1]))), int(round(bestCircleRadius)), (0, 0, 0), 3)
191 | return mask
192 |
193 |
194 | def weightMap(hue, intensity, edges, roi):
195 | ellipseMask, contourMask = findEllipses(edges)
196 | circlesMask = findCircles(hue, intensity)
197 | ransacMask = findRANSACCircles(edges)
198 |
199 | # create a map by combining different masks
200 | # circle/ellipse detection masks have a higher weight then roi mask and ransac mask
201 | combinedMask = np.zeros(edges.shape, dtype=np.float32)
202 | combinedMask += ellipseMask
203 | combinedMask += contourMask
204 | combinedMask += circlesMask
205 | combinedMask += ransacMask.astype(np.float32) / 2.0
206 | combinedMask += roi.astype(np.float32) / 4.0
207 |
208 | nonZeroMask = combinedMask != 0
209 |
210 | # rescale mask to 0 .. 1 range
211 | # (weight is either in 0.25 .. 1 range or 0)
212 | combinedMask[nonZeroMask] /= 255 * 3.75
213 | combinedMask[nonZeroMask] *= 0.6
214 | combinedMask[nonZeroMask] += 0.4
215 | return combinedMask
216 |
217 |
218 | def roiMask(image, boundaries):
219 | scale = max([1.0, np.average(np.array(image.shape)[0:2] / 400.0)])
220 | shape = (int(round(image.shape[1] / scale)), int(round(image.shape[0] / scale)))
221 |
222 | small_color = cv2.resize(image, shape, interpolation=cv2.INTER_LINEAR)
223 |
224 | # reduce details and remove noise for better edge detection
225 | small_color = cv2.bilateralFilter(small_color, 8, 64, 64)
226 | small_color = cv2.pyrMeanShiftFiltering(small_color, 8, 64, maxLevel=1)
227 | small = cv2.cvtColor(small_color, cv2.COLOR_BGR2HSV)
228 |
229 | hue = small[::, ::, 0]
230 | intensity = cv2.cvtColor(small_color, cv2.COLOR_BGR2GRAY)
231 |
232 | edges = extractEdges(hue, intensity)
233 | roi = roiFromEdges(edges)
234 | weight_map = weightMap(hue, intensity, edges, roi)
235 |
236 | _, final_mask = cv2.threshold(roi, 5, 255, cv2.THRESH_BINARY)
237 | small = cv2.bitwise_and(small, small, mask=final_mask)
238 |
239 | kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4, 4))
240 |
241 | for (lower, upper) in boundaries:
242 | lower = np.array([lower, 80, 50], dtype="uint8")
243 | upper = np.array([upper, 255, 255], dtype="uint8")
244 |
245 | # find the colors within the specified boundaries and apply
246 | # the mask
247 | mask = cv2.inRange(small, lower, upper)
248 | mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
249 | mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
250 | final_mask = cv2.bitwise_and(final_mask, mask)
251 |
252 | # blur the mask for better contour extraction
253 | final_mask = cv2.GaussianBlur(final_mask, (5, 5), 0)
254 | return (final_mask, weight_map, scale)
255 |
256 |
257 | def extractRoi(image, winSize, stepSize):
258 | # hue boundaries
259 | colors = [
260 | (15, 30) # orange-yellow
261 | ]
262 |
263 | mask, weight_map, mask_scale = roiMask(image, colors)
264 | contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
265 | yield weight_map, mask_scale
266 |
267 | for resized in pyramid(image, winSize):
268 | scale = image.shape[0] / resized.shape[0]
269 | for x, y, w, h in boundingRects(mask_scale, contours):
270 | x /= scale
271 | y /= scale
272 | w /= scale
273 | h /= scale
274 | center = (min(x + w / 2, resized.shape[1]), min(y + h / 2, resized.shape[0]))
275 | if w > winSize[0] or h > winSize[1]:
276 | for x, y, window in sliding_window(resized, (int(x), int(y), int(w), int(h)), stepSize, winSize):
277 | yield ((x, y, winSize[0], winSize[1]), scale, window)
278 | else:
279 | x = max(0, int(center[0] - winSize[0] / 2))
280 | y = max(0, int(center[1] - winSize[1] / 2))
281 | window = resized[y:y + winSize[1], x:x + winSize[0]]
282 | yield ((x, y, winSize[0], winSize[1]), scale, window)
283 |
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/TemplateMatcher.py:
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1 | import cv2
2 | import numpy as np
3 | from multiprocessing.pool import ThreadPool
4 | from sklearn.base import BaseEstimator, TransformerMixin
5 |
6 | FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
7 |
8 |
9 | class Templates():
10 | def __init__(self, features):
11 | self.features = features
12 |
13 |
14 | class TemplateMatch(TransformerMixin, BaseEstimator):
15 | def __init__(self, templates, ratio=0.75):
16 | self.templates = templates
17 | self.ratio = ratio
18 |
19 | flann_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
20 | self.matcher = cv2.FlannBasedMatcher(flann_params, {})
21 | self.pool = ThreadPool(processes=cv2.getNumberOfCPUs())
22 |
23 | def __getstate__(self):
24 | return dict(templates=self.templates, ratio=self.ratio)
25 |
26 | def __setstate__(self, state):
27 | self.templates = state['templates']
28 | self.ratio = state['ratio']
29 |
30 | flann_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
31 | self.matcher = cv2.FlannBasedMatcher(flann_params, {})
32 | self.pool = ThreadPool(processes=1) # cv2.getNumberOfCPUs())
33 |
34 | def feature(self, match):
35 | return match.distance
36 |
37 | def process(self, features):
38 | count = len(features)
39 | if not count:
40 | return [0, 0, 0, 0, 0, 0]
41 |
42 | split = [0.08, 0.12096, 0.16192, 0.20288, 0.24384, 0.2848, 1]
43 | return np.histogram(features, bins=split)[0]
44 |
45 | def match(self, sample):
46 | feature = []
47 | for template in self.templates.features:
48 | # we have to have at least 2 descriptors for 2 nearest neighbour search
49 | if len(sample) < 2:
50 | feature.extend([0, 0, 0, 0, 0, 0])
51 | else:
52 | if template.dtype != sample.dtype or template.shape[1] != sample.shape[1]:
53 | print '!!!'
54 | print template.shape, template.dtype, sample.shape, sample.dtype
55 | raw_matches = self.matcher.knnMatch(template, trainDescriptors=sample, k=2)
56 | matches = [self.feature(m[0])
57 | for m in raw_matches
58 | if len(m) == 2 and m[0].distance < m[1].distance * self.ratio]
59 | feature.extend(self.process(matches))
60 | return np.array(feature)
61 |
62 | def fit(self, X, y=None, **fit_params):
63 | return self
64 |
65 | def transform(self, X, **transform_params):
66 | l = dict(n=0)
67 |
68 | def f(image):
69 | l['n'] += 1
70 | if (100 * l['n'] / len(X)) % 5 == 0:
71 | print '\r%d%% - %d/%d' % (100 * l['n'] / len(X), l['n'], len(X)),
72 | return self.match(image)
73 |
74 | ires = self.pool.imap(f, X)
75 | return np.array(list(ires))
76 |
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/avery_8254.pdf:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/avery_8254.pdf
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/classifier.pdf:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/classifier.pdf
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/classify.py:
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1 | import cv2
2 | import sys
3 | import pickle
4 | import numpy as np
5 |
6 | from AffineInvariantFeatures import AffineInvariant
7 | from RegionOfInterest import extractRoi
8 | from TemplateMatcher import TemplateMatch, Templates # imported to allow deserialization
9 | from util import non_max_suppression_fast
10 |
11 | from PIL import Image
12 | from matplotlib import pyplot as plt
13 |
14 |
15 | def classify(model, features, coords, weight_map, mask_scale):
16 | rects = []
17 |
18 | if len(features):
19 | y_pred = model.predict_proba(features)
20 | success = 0
21 |
22 | for (_, result), ((x, y, w, h), scale) in zip(y_pred, coords):
23 | mask_window = weight_map[y / mask_scale:(y + h) / mask_scale, x / mask_scale:(x + w) / mask_scale]
24 | weight = mask_window.max()
25 | if result * weight > 0.5:
26 | success += 1
27 | rects.append([x * scale, y * scale, (x + w) * scale, (y + h) * scale])
28 |
29 | print 'windows identified as positive: %d/%d(%0.2f%%)' % (success, len(y_pred), 100.0 * success / len(y_pred))
30 | else:
31 | print 'windows identified as positive: 0/%d(0%%)' % len(features)
32 |
33 | return np.array(rects)
34 |
35 |
36 | def main(image_file):
37 | image = Image.open(image_file)
38 | if image is None:
39 | print 'Could not load image "%s"' % sys.argv[1]
40 | return
41 |
42 | image = np.array(image.convert('RGB'), dtype=np.uint8)
43 | image = image[:, :, ::-1].copy()
44 |
45 | winSize = (200, 200)
46 | stepSize = 32
47 |
48 | roi = extractRoi(image, winSize, stepSize)
49 | weight_map, mask_scale = next(roi)
50 |
51 | samples = [(rect, scale, cv2.cvtColor(window, cv2.COLOR_BGR2GRAY))
52 | for rect, scale, window in roi]
53 |
54 | X_test = [window for rect, scale, window in samples]
55 | coords = [(rect, scale) for rect, scale, window in samples]
56 |
57 | extractor = cv2.FeatureDetector_create('SURF')
58 | detector = cv2.DescriptorExtractor_create('SURF')
59 |
60 | affine = AffineInvariant(extractor, detector)
61 |
62 | saved = pickle.load(open('classifier.pkl', 'rb'))
63 |
64 | feature_transform = saved['pipe']
65 | model = saved['model']
66 |
67 | print 'Extracting Affine transform invariant features'
68 | affine_invariant_features = affine.transform(X_test)
69 | print 'Matching features with template'
70 | features = feature_transform.transform(affine_invariant_features)
71 |
72 | rects = classify(model, features, coords, weight_map, mask_scale)
73 | for (left, top, right, bottom) in non_max_suppression_fast(rects, 0.4):
74 | cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 0), 10)
75 | cv2.rectangle(image, (left, top), (right, bottom), (32, 32, 255), 5)
76 |
77 | plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
78 | plt.show()
79 |
80 | if __name__ == '__main__':
81 | image_file = sys.argv[1] if len(sys.argv) >= 2 else 'sample.jpg'
82 | main(image_file)
83 |
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/dns_badge.png:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/dns_badge.png
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/dns_logo.png:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/dns_logo.png
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/sample.jpg:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/sample.jpg
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/setup.py:
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1 | from distutils.core import setup
2 | import py2exe
3 | import matplotlib
4 |
5 | setup(console = ['classify.py'],
6 | data_files = matplotlib.get_py2exe_datafiles(),
7 | options = dict(
8 | py2exe = dict(
9 | compressed = True,
10 | optimize = 2,
11 | dll_excludes = ['MSVCP90.dll'],
12 | packages = ['FileDialog'],
13 | includes = [
14 | 'scipy',
15 | 'scipy.integrate',
16 | 'scipy.special.*',
17 | 'scipy.linalg.*',
18 | 'scipy.sparse.csgraph._validation',
19 | 'matplotlib',
20 | 'matplotlib.backends.backend_tkagg',
21 | 'matplotlib.pyplot',
22 | 'sklearn',
23 | 'sklearn.pipeline',
24 | 'sklearn.tree',
25 | 'sklearn.tree._utils',
26 | 'sklearn.utils.lgamma'
27 | ],
28 | excludes = [
29 | '_gtkagg',
30 | '_tkagg',
31 | '_agg2',
32 | '_cairo',
33 | '_cocoaagg',
34 | '_fltkagg',
35 | '_gtk',
36 | '_gtkcairo',
37 | 'tcl'
38 | ]
39 | )
40 | ))
41 |
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/template.png:
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https://raw.githubusercontent.com/AVGInnovationLabs/DoNotSnap/e679947fd3bdfec7c9c786ee285e9ac64d8d1a18/template.png
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/train.py:
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1 | import cv2
2 | import sys
3 | import pickle
4 | import numpy as np
5 | import matplotlib.pyplot as plt
6 |
7 | from AffineInvariantFeatures import AffineInvariant
8 | from TemplateMatcher import TemplateMatch, Templates
9 |
10 | from PIL import Image
11 | from itertools import izip_longest
12 |
13 | from sklearn.cross_validation import train_test_split
14 | from sklearn.grid_search import GridSearchCV
15 | from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix, accuracy_score
16 | from sklearn.pipeline import Pipeline
17 | from sklearn.tree import export_graphviz, DecisionTreeClassifier
18 |
19 |
20 | def line_count(filename):
21 | with open(filename) as data:
22 | return sum(1 for line in data)
23 |
24 |
25 | def read_image(filename):
26 | return np.array(Image.open(filename.strip('\n')).convert('L'), np.uint8)
27 |
28 |
29 | def read_file(filename, limit=0):
30 | n = 0
31 | lines = line_count(filename)
32 |
33 | with open(filename) as data:
34 | while True:
35 | line = next(data, None)
36 | if not line or (limit and n >= limit):
37 | break
38 |
39 | n += 1
40 | print '\r%s %d/%d' % (filename, n, limit or lines),
41 | try:
42 | yield read_image(line)
43 | except:
44 | continue
45 |
46 |
47 | def get_templates():
48 | return np.array(list(read_file('templates.txt')))
49 |
50 |
51 | def get_images(limit=0):
52 | positive = read_file('positive.txt', limit / 2 if limit else 0)
53 | negative = read_file('negative.txt', limit / 2 if limit else 0)
54 |
55 | for p, n in izip_longest(positive, negative):
56 | if p is not None:
57 | yield (1, p)
58 | if n is not None:
59 | yield (0, n)
60 |
61 |
62 | def get_dataset(limit):
63 | return map(np.asarray, zip(*get_images(limit)))
64 |
65 |
66 | def plot_roc(fpr, tpr, roc_auc):
67 | # Plot all ROC curves
68 | plt.figure()
69 |
70 | plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
71 |
72 | plt.plot([0, 1], [0, 1], 'k--')
73 | plt.xlim([0.0, 1.0])
74 | plt.ylim([0.0, 1.05])
75 | plt.xlabel('False Positive Rate')
76 | plt.ylabel('True Positive Rate')
77 | plt.title('Affine Invariant SURF + Decision Tree Classifier')
78 | plt.legend(loc='lower right')
79 | plt.show()
80 |
81 |
82 | def plot_importance(feature_count, importances, indices):
83 | plt.figure()
84 | plt.title('Feature importances')
85 | plt.bar(range(feature_count), importances[indices], color='r', align='center')
86 | plt.xticks(range(feature_count), indices)
87 | plt.xlim([-1, feature_count])
88 | plt.show()
89 |
90 |
91 | def main(name, dataset_size):
92 | templates = get_templates()
93 |
94 | print 'templates: %d' % len(templates)
95 |
96 | labels, samples = get_dataset(dataset_size)
97 |
98 | print 'samples: %d' % len(samples)
99 |
100 | extractor = cv2.FeatureDetector_create('SURF')
101 | detector = cv2.DescriptorExtractor_create('SURF')
102 |
103 | print 'applying affine invariant transform'
104 |
105 | affine = AffineInvariant(extractor, detector)
106 | templates = affine.transform(templates)
107 | samples = affine.transform(samples)
108 |
109 | model = Pipeline([
110 | ('match', TemplateMatch(Templates(templates))), # XXX: hack to bypass cloning error
111 | # ('reduce_dim', PCA(n_components = 12 * 6))
112 | ])
113 |
114 | samples = model.fit_transform(samples)
115 |
116 | rng = np.random.RandomState()
117 |
118 | X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.5, random_state=rng)
119 | print 'train: %d, test: %d' % (len(X_train), len(X_test))
120 |
121 | params = dict(
122 | min_samples_split = [5, 6, 7, 8, 9, 10],
123 | min_samples_leaf = [3, 4, 5, 6, 7],
124 | max_leaf_nodes = [10, 9, 8, 7, 6],
125 | class_weight = [{1: w} for w in [10, 8, 4, 2, 1]]
126 | )
127 |
128 | tree = DecisionTreeClassifier(max_depth=4, random_state=rng)
129 |
130 | cvmodel = GridSearchCV(tree, params, cv=10, n_jobs=cv2.getNumberOfCPUs())
131 | cvmodel.fit(X_train, y_train)
132 |
133 | print 'grid scores'
134 | for params, mean_score, scores in cvmodel.grid_scores_:
135 | print '%0.3f (+/-%0.03f) for %r' % (mean_score, scores.std() * 2, params)
136 | print 'best parameters'
137 | print cvmodel.best_params_
138 |
139 | importances = cvmodel.best_estimator_.feature_importances_
140 | indices = np.argsort(importances)[::-1]
141 |
142 | plot_importance(6, importances, indices)
143 |
144 | y_pred = cvmodel.predict(X_test)
145 | accuracy = accuracy_score(y_test, y_pred)
146 |
147 | print 'accuracy: %f' % accuracy
148 | print classification_report(y_test, y_pred)
149 | print confusion_matrix(y_test, y_pred)
150 |
151 | y_score = cvmodel.predict_proba(X_test)[:, 1]
152 | fpr, tpr, _ = roc_curve(y_test, y_score)
153 | roc_auc = auc(fpr, tpr)
154 |
155 | plot_roc(fpr, tpr, roc_auc)
156 |
157 | export_graphviz(cvmodel.best_estimator_, out_file=name + '.dot', class_names=['background', 'badge'], filled=True, rounded=True, special_characters=True)
158 | pickle.dump(dict(params=params, pipe=model, model=cvmodel.best_estimator_), open(name + '.pkl', 'wb'))
159 |
160 |
161 | if __name__ == '__main__':
162 | name = sys.argv[1] if len(sys.argv) >= 2 else 'classifier'
163 | dataset_size = int(sys.argv[2]) if len(sys.argv) >= 3 else 0
164 |
165 | main(name, dataset_size)
166 |
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/util.py:
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1 | import cv2
2 | import numpy as np
3 |
4 |
5 | def sliding_window(image, start, stepSize, windowSize):
6 | width = max(1, start[2] - windowSize[0])
7 | height = max(1, start[3] - windowSize[1])
8 | # slide a window across the image
9 | for y in xrange(start[1], start[1] + height, stepSize):
10 | for x in xrange(start[0], start[0] + width, stepSize):
11 | h = min(windowSize[1], start[3])
12 | w = min(windowSize[0], start[2])
13 | center = (min(x + w / 2, image.shape[1]), min(y + h / 2, image.shape[0]))
14 | x = max(0, int(center[0] - windowSize[0] / 2))
15 | y = max(0, int(center[1] - windowSize[1] / 2))
16 | # yield the current window
17 | yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])
18 |
19 |
20 | def pyramid(image, minSize):
21 | yield image
22 |
23 | if image.shape[0] < minSize[0] and image.shape[1] < minSize[1]:
24 | # image too small - upscaling until we hit window level
25 | image = cv2.pyrUp(image)
26 |
27 | while (image.shape[0] <= minSize[0] or image.shape[1] <= minSize[1]):
28 | yield image
29 | image = cv2.pyrUp(image)
30 | else:
31 | # image too big - downscaling until we hit window level
32 | image = cv2.pyrDown(image)
33 |
34 | while (image.shape[0] >= minSize[0] or image.shape[1] >= minSize[1]):
35 | yield image
36 | image = cv2.pyrDown(image)
37 |
38 |
39 | # Malisiewicz et al.
40 | def non_max_suppression_fast(boxes, overlapThresh):
41 | # if there are no boxes, return an empty list
42 | if len(boxes) == 0:
43 | return []
44 |
45 | # if the bounding boxes integers, convert them to floats --
46 | # this is important since we'll be doing a bunch of divisions
47 | if boxes.dtype.kind == "i":
48 | boxes = boxes.astype("float")
49 |
50 | # initialize the list of picked indexes
51 | pick = []
52 |
53 | # grab the coordinates of the bounding boxes
54 | x1 = boxes[:, 0]
55 | y1 = boxes[:, 1]
56 | x2 = boxes[:, 2]
57 | y2 = boxes[:, 3]
58 |
59 | # compute the area of the bounding boxes and sort the bounding
60 | # boxes by the bottom-right y-coordinate of the bounding box
61 | area = (x2 - x1 + 1) * (y2 - y1 + 1)
62 | idxs = np.argsort(y2)
63 |
64 | # keep looping while some indexes still remain in the indexes
65 | # list
66 | while len(idxs) > 0:
67 | # grab the last index in the indexes list and add the
68 | # index value to the list of picked indexes
69 | last = len(idxs) - 1
70 | i = idxs[last]
71 | pick.append(i)
72 |
73 | # find the largest (x, y) coordinates for the start of
74 | # the bounding box and the smallest (x, y) coordinates
75 | # for the end of the bounding box
76 | xx1 = np.maximum(x1[i], x1[idxs[:last]])
77 | yy1 = np.maximum(y1[i], y1[idxs[:last]])
78 | xx2 = np.minimum(x2[i], x2[idxs[:last]])
79 | yy2 = np.minimum(y2[i], y2[idxs[:last]])
80 |
81 | # compute the width and height of the bounding box
82 | w = np.maximum(0, xx2 - xx1 + 1)
83 | h = np.maximum(0, yy2 - yy1 + 1)
84 |
85 | # compute the ratio of overlap
86 | overlap = (w * h) / area[idxs[:last]]
87 |
88 | # delete all indexes from the index list that have
89 | idxs = np.delete(idxs,
90 | np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
91 |
92 | # return only the bounding boxes that were picked using the
93 | # integer data type
94 | return boxes[pick].astype("int")
95 |
96 |
97 | # Felzenszwalb et al.
98 | def non_max_suppression_slow(boxes, overlapThresh):
99 | # if there are no boxes, return an empty list
100 | if len(boxes) == 0:
101 | return []
102 |
103 | # initialize the list of picked indexes
104 | pick = []
105 |
106 | # grab the coordinates of the bounding boxes
107 | x1 = boxes[:, 0]
108 | y1 = boxes[:, 1]
109 | x2 = boxes[:, 2]
110 | y2 = boxes[:, 3]
111 |
112 | # compute the area of the bounding boxes and sort the bounding
113 | # boxes by the bottom-right y-coordinate of the bounding box
114 | area = (x2 - x1 + 1) * (y2 - y1 + 1)
115 | idxs = np.argsort(y2)
116 |
117 | # keep looping while some indexes still remain in the indexes
118 | # list
119 | while len(idxs) > 0:
120 | # grab the last index in the indexes list, add the index
121 | # value to the list of picked indexes, then initialize
122 | # the suppression list (i.e. indexes that will be deleted)
123 | # using the last index
124 | last = len(idxs) - 1
125 | i = idxs[last]
126 | pick.append(i)
127 | suppress = [last]
128 |
129 | # loop over all indexes in the indexes list
130 | for pos in xrange(0, last):
131 | # grab the current index
132 | j = idxs[pos]
133 |
134 | # find the largest (x, y) coordinates for the start of
135 | # the bounding box and the smallest (x, y) coordinates
136 | # for the end of the bounding box
137 | xx1 = max(x1[i], x1[j])
138 | yy1 = max(y1[i], y1[j])
139 | xx2 = min(x2[i], x2[j])
140 | yy2 = min(y2[i], y2[j])
141 |
142 | # compute the width and height of the bounding box
143 | w = max(0, xx2 - xx1 + 1)
144 | h = max(0, yy2 - yy1 + 1)
145 |
146 | # compute the ratio of overlap between the computed
147 | # bounding box and the bounding box in the area list
148 | overlap = float(w * h) / area[j]
149 |
150 | # if there is sufficient overlap, suppress the
151 | # current bounding box
152 | if overlap > overlapThresh:
153 | suppress.append(pos)
154 |
155 | # delete all indexes from the index list that are in the
156 | # suppression list
157 | idxs = np.delete(idxs, suppress)
158 |
159 | # return only the bounding boxes that were picked
160 | return boxes[pick]
161 |
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