def process(im, haarc=None, silent=False):
if haarc is None:
haarc = haar.haarInit(im + '/../haar/cascade.xml')
img_ref = im
img, rect = findROI(img_ref, haarc)
imb = extractBinary(img)
imb_contours = imb.copy()
vect = None
img_tr = np.copy(img_ref)
if not silent:
debugThresh(img)
if rect is None:
img_ref = cv2.cvtColor(img_ref, cv2.COLOR_GRAY2BGR)
return img_ref, img_ref, None
contours, _ = cv2.findContours(imb_contours, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if contours:
contour = bestContourAsInt(contours)
hull = cv2.convexHull(contour, returnPoints=False).astype('int')
defects = cv2.convexityDefects(contour, hull)
hull_points = [
tuple(p[0]) for p in cv2.convexHull(contour, returnPoints=True)
]
contour_points = [tuple(p[0]) for p in contour]
hull_refined, defects_points = refineHullDefects(
hull_points, defects, contour, 2500)
features = packFeatures(contour, hull_points, defects_points,
hull_refined, rect)
img = drawResult(img, features)
img_ref = cv2.cvtColor(img_ref, cv2.COLOR_GRAY2BGR)
(x, y, w, h) = rect
img_ref[y:y + h, x:x + w] = img
cv2.rectangle(img_ref, (x, y), (x + w, y + h), (255, 0, 0))
img_tr[y:y + h, x:x + w] = imb
else:
img_ref = cv2.cvtColor(img_ref, cv2.COLOR_GRAY2BGR)
img_tr = imb
densityVect = zoning(imb)
img_tr = cv2.cvtColor(img_tr, cv2.COLOR_GRAY2BGR)
return img_ref, img_tr, densityVect
def process(im, haarc=None,silent=False):
if haarc is None:
haarc = haar.haarInit(im + '/../haar/cascade.xml')
img_ref = im
img, rect = findROI(img_ref, haarc)
imb = extractBinary(img)
imb_contours = imb.copy()
vect = None
img_tr = np.copy(img_ref)
if not silent:
debugThresh(img)
if rect is None:
img_ref = cv2.cvtColor(img_ref,cv2.COLOR_GRAY2BGR)
return img_ref, img_ref, None
contours, _ = cv2.findContours(imb_contours, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if contours:
contour = bestContourAsInt(contours)
hull = cv2.convexHull(contour, returnPoints=False).astype('int')
defects = cv2.convexityDefects(contour, hull)
hull_points = [tuple(p[0]) for p in cv2.convexHull(contour, returnPoints=True)]
contour_points = [tuple(p[0]) for p in contour]
hull_refined, defects_points = refineHullDefects(hull_points, defects, contour, 2500)
features = packFeatures(contour, hull_points, defects_points, hull_refined, rect)
img = drawResult(img, features)
img_ref = cv2.cvtColor(img_ref,cv2.COLOR_GRAY2BGR)
(x,y,w,h) = rect
img_ref[y:y+h, x:x+w] = img
cv2.rectangle(img_ref, (x,y), (x+w,y+h), (255,0,0))
img_tr[y:y+h, x:x+w] = imb
else:
img_ref = cv2.cvtColor(img_ref,cv2.COLOR_GRAY2BGR)
img_tr = imb
densityVect = zoning(imb)
img_tr = cv2.cvtColor(img_tr,cv2.COLOR_GRAY2BGR)
return img_ref, img_tr,densityVect
fichier.sort(key=lambda x: os.path.basename(x))
length = len(fichier)
return fichier
if __name__ == '__main__' :
parser = OptionParser()
parser.add_option("-d", "--directory", dest="dirname", help="Directory to parse", metavar="DIR")
(options, args) = parser.parse_args()
filesNames = listDirectory(options.dirname)
class_dict = eval(open("../bayes/dictionnary.txt").read())
haarc = haar.haarInit(os.path.dirname(os.path.realpath(__file__)) + '/../haar/cascade.xml')
trainData = []
responses = []
total = len(filesNames)
i = 1
for fileName in filesNames:
print "{0} / {1}".format(i,total)
i = i+1
_,_,densityVect = detect.process(detect.loadSample(fileName),haarc,True)
if densityVect is None:
continue
trainData.append(densityVect)
