def calc_features(self):
all_features = {
name: func(self)
for name, func in feature_funcs.feature_dict()
}
# this only retains features that were actually decorated
# all_features will also contain any features used for calculations
self.features = {
k: v
for (k, v) in all_features.items() if k in feature_funcs.labels()
}
def classify_all(src,contour_list,train=True):
print "Classifying..."
image = Sar_Image(src)
print "number of contours:",len(contour_list)
potential_spill_regions = [Region(x) for x in contour_list if cv2.contourArea(x) > MINSIZE]
print "potential spill regions:",len(potential_spill_regions)
data_dir = "data"
region_name_base = "region"
if not os.path.exists(data_dir):
os.makedirs(data_dir)
for num,region in enumerate(potential_spill_regions):
# print "setting up neighborhood matrix..."
region.setup_mat(image)
if train:
region.save(data_dir,region_name_base+"_"+str(num))
else:
region.calc_features()
# print "cleaning up region data"
region.cleanup()
if train:
print "chipping done"
sys.exit()
print "pickling feature vectors"
# maybe export feature vector so that it doesn't need to be recalculated while testing classifiers
with open("region_vectors.pkl",'w') as pklfile:
pickle.dump(potential_spill_regions,pklfile)
centroids, labels = classify(potential_spill_regions,8)
print [x for x in feature_funcs.labels()]
def to_vector(self):
return [self.features[L] for L in feature_funcs.labels()]
def classify_all(src, contour_list, train=True):
print "Classifying..."
image = Sar_Image(src)
print "number of contours:", len(contour_list)
potential_spill_regions = [
Region(x) for x in contour_list if cv2.contourArea(x) > MINSIZE
]
print "potential spill regions:", len(potential_spill_regions)
data_dir = "data"
region_name_base = "region"
if not os.path.exists(data_dir):
os.makedirs(data_dir)
for num, region in enumerate(potential_spill_regions):
# print "setting up neighborhood matrix..."
region.setup_mat(image)
if train:
region.save(data_dir, region_name_base + "_" + str(num))
else:
region.calc_features()
# print "cleaning up region data"
region.cleanup()
if train:
print "chipping done"
sys.exit()
print "pickling feature vectors"
# maybe export feature vector so that it doesn't need to be recalculated while testing classifiers
with open("region_vectors.pkl", 'w') as pklfile:
pickle.dump(potential_spill_regions, pklfile)
centroids, labels = classify(potential_spill_regions, 8)
print[x for x in feature_funcs.labels()]
print "centroids:"
for c in centroids:
print c
result_image = cv2.cvtColor(fit_to_8bit(image.to_array()),
cv2.cv.CV_GRAY2RGB)
print "Saving result image..."
result_loc = "detections.jpg"
colors = [get_rand_color(0, 255) for x in xrange(max(labels) + 1)]
for label, contour in zip(labels,
[x.coords for x in potential_spill_regions]):
cv2.drawContours(result_image, [contour], -1, colors[label], 3)
# # outline oil in red and false alarms in green
# cv2.drawContours(result_image,oil,-1,(0,255,0),3)
# cv2.drawContours(result_image,false_alarms,-1,(0,0,255),3)
# try:
# assert len(entropy_list) == len(potential_oil_spills)
# except:
# print len(entropy_list),len(potential_oil_spills),"are not equal!"
# for i,e in enumerate(entropy_list):
# M = cv2.moments(potential_oil_spills[i])
# if M['m00'] == 0:
# print "spill has no area..."
# print cv2.contourArea(potential_oil_spills[i])
# else:
# x,y = (int(M['m10']/M['m00']),int(M['m01']/M['m00']))
# cv2.putText(result_image,str(round(e,3)),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(255,255,255))
# save the result image for visual inspection
cv2.imwrite(result_loc, result_image)
print "File is:", result_loc
# return list of contours that were identified as oil
return None
def calc_features(self):
all_features = {name:func(self) for name,func in feature_funcs.feature_dict()}
# this only retains features that were actually decorated
# all_features will also contain any features used for calculations
self.features = {k:v for (k,v) in all_features.items() if k in feature_funcs.labels()}
def to_vector(self):
return [self.features[L] for L in feature_funcs.labels()]
