def process_image(frame):
output = {
"buildings": []
}
shapes = []
frame = cv2.resize(frame, IMAGE_SIZE_FOR_TRACING)
if IGNORE_RED:
frame = cv2.multiply(frame, unred_mask)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (BLUR_RADIUS, BLUR_RADIUS))
edges = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, THRESH_RADIUS, THRESH_CONSTANT)
retr_external = True
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
hierarchy = hierarchy[0]
top_level_contour_indices = [i for i in range(len(hierarchy)) if -1 == hierarchy[i][3]]
biggest_top_level_contour_index = max(top_level_contour_indices, key=lambda i: cv2.contourArea(contours[i])) if len(top_level_contour_indices) else None
shape_contour_indices = [i for i in range(len(hierarchy)) if hierarchy[i][3] == biggest_top_level_contour_index]
contours = [contours[i] for i in shape_contour_indices]
# print hierarchy
# (contour, id, name, descriptor)
contours = [c for c in contours if cv2.contourArea(c) > MIN_SHAPE_SIZE]
contours = [cv2.approxPolyDP(c, cv2.arcLength(c, True) / CONTOUR_SIMPLIFICATION, True) for c in contours]
contours.sort(key=lambda x: cv2.contourArea(x), reverse=True)
contours = contours[:min(len(contours), MAX_CONTOURS)]
if SHOW_SINGLE_CAPTURE_AND_DUMP:
for contour in contours:
cv2.drawContours(frame, [contour], -1, (255,0,0))
x,y = contour[0][0]
i = len(shapes)
cv2.putText(frame, str(i), (x,y), cv2.FONT_HERSHEY_PLAIN, 0.7, (0, 0, 255))
shapes.append(contour)
else:
global TIMESTAMP
TIMESTAMP += 1
for (contour, id, name, descriptor) in matcher.run_frame(contours):
cv2.drawContours(frame, [contour], -1, (255,0,0))
x,y = contour[0][0]
rotation, flipped, rot_without_flipping = matcher.get_rotation(name, contour)
label = "{0}:{1}:{2}".format(id, name, int(rotation / math.pi * 180))
if flipped: label += '(F)'
cv2.putText(frame, label, (x,y), cv2.FONT_HERSHEY_PLAIN, 0.7, (0, 0, 255))
if matcher.lifetimes[id] >= MIN_SHAPE_LIFETIME:
output['buildings'].append(shape_json(contour, id, name, rotation, flipped, rot_without_flipping))
output['timestamp'] = str(TIMESTAMP)
if SHOW_SINGLE_CAPTURE_AND_DUMP:
f = open('last_rec.pickle', 'w')
f.write(pickle.dumps(shapes))
f.close()
return frame, output
def features_from_contour(contour):
# print contour
moments = cv2.moments(contour)
# print cv2.HuMoments(contour)
feats = []
feats += list([x[0] for x in cv2.HuMoments(moments)])[:-1]
# print feats[0]
feats.append(cv2.contourArea(contour))
feats.append(cv2.arcLength(contour, True))
points = points_to_np([ps[0] for ps in contour])
# ((center_x,center_y),(w,h),rotation) = cv2.minAreaRect(points)
# feats += [w, h, w*1.0/h]
feats += extra_features(contour)
feats.append(len(contour))
return feats
def features_from_contour(contour):
# print contour
moments = cv2.moments(contour)
# print cv2.HuMoments(contour)
feats = []
feats += list([x[0] for x in cv2.HuMoments(moments)])[:-1]
# print feats[0]
feats.append(cv2.contourArea(contour))
feats.append(cv2.arcLength(contour, True))
points = points_to_np([ps[0] for ps in contour])
# ((center_x,center_y),(w,h),rotation) = cv2.minAreaRect(points)
# feats += [w, h, w*1.0/h]
feats += extra_features(contour)
feats.append(len(contour))
return feats
