def run(x, inner_A=__cheat_A, inner_B=__cheat_B): """ Run the neural network forward on the input x using the matrix A,B. Log the result as having happened so that we can debug errors and improve query efficiency. """ Tracker().query_count += x.shape[0] assert len(x.shape) == 2 orig_x = x x = model(torch.tensor(orig_x)).detach().numpy() Tracker().save_queries(zip(orig_x, x)) if TRACK_LINES: for line in traceback.format_stack(): if 'repeated' in line: continue line_no = int(line.split("line ")[1].split()[0][:-1]) if line_no not in Tracker().query_count_at: Tracker().query_count_at[line_no] = 0 Tracker().query_count_at[line_no] += x.shape[0] return x
def __init__(self, cfg, src, n, LED_pos, LED_tresholds): threading.current_thread().name = 'HexTrack' self.cfg = cfg self.frame_idx = 0 self.n = n self.mask_init = True self.made_mask = None # Create path to csv log file for tracking mouse position and LED-light state path = pkg_resources.resource_filename(__name__, "/data/interim/position_log_files/{}".format(src[len(src)-29:len(src)-10])) if not os.path.exists(path): try: os.mkdir(path) except OSError: print("Creation of the directory %s failed, this path probably already exists" % path) self.path = pkg_resources.resource_filename(__name__, '/data/interim/Position_log_files/{}/pos_log_file_{}.csv' .format(src[len(src)-29:len(src)-10], n)) # Initiation of the Grabbers and Trackers and creation of csv log file self.grabber = Grabber(src) self.tracker = Tracker(cfg, pos_log_file=open(self.path, 'w'), name=__name__, LED_pos=LED_pos, LED_thresholds=LED_tresholds) logging.debug('HexTrack initialization done!') self.vid = VideoFileClip(src) self.duration = self.vid.duration*15
def main(): TIME = 60 * 60 # 60 min * 60s --> s # getting twitter keys CONSUMER_KEY = environ['CONSUMER_KEY'] CONSUMER_SECRET = environ['CONSUMER_SECRET'] ACCESS_TOKEN = environ['ACCESS_KEY'] ACCESS_TOKEN_SECRET = environ['ACCESS_SECRET'] # init bot bot = Bot(CONSUMER_KEY=CONSUMER_KEY, CONSUMER_SECRET=CONSUMER_SECRET, ACCESS_TOKEN=ACCESS_TOKEN, ACCESS_TOKEN_SECRET=ACCESS_TOKEN_SECRET) # init tracker (database api call) tracker = Tracker() # tweet init tweet = Tweet(totalDeaths=(tracker.getTotalDeaths()), totalInfected=(tracker.getTotalInfected())) while True: # Get latest data from Tracker tracker.update() # Generate tweet with latest data tweet.update(totalDeaths=(tracker.totalDeaths), totalInfected=(tracker.totalInfected)) # Get old tweets oldTweets = bot.getOldTweets() # Check if tweet is not duplicated if (tweet.isDuplicated(oldTweets=oldTweets) == False): bot.postTweet(text=(tweet.text)) time.sleep(TIME) #s
def sweep_for_critical_points(std=1, known_T=None): while True: logger.log("Start another sweep", level=Logger.INFO) qs = Tracker().query_count sweep = do_better_sweep(offset=np.random.normal(0, np.random.uniform( std / 10, std), size=DIM), known_T=known_T, low=-std * 1e3, high=std * 1e3, debug=False) logger.log("Total intersections found", len(sweep), level=Logger.INFO) logger.log("delta queries", Tracker().query_count - qs, level=Logger.INFO) for point in sweep: yield point
def gather_ratios(critical_points, known_T, check_fn, LAYER, COUNT): this_layer_critical_points = [] logger.log("Gathering", COUNT, "critical points", level=Logger.INFO) for point in critical_points: if LAYER > 0: if any(np.any(np.abs(x) < 1e-5) for x in known_T.get_hidden_layers(point)): continue if CHEATING: if np.any(np.abs(cheat_get_inner_layers(point)[0]) < 1e-10): logger.log(cheat_get_inner_layers(point), level=Logger.INFO) logger.log("Looking at one I don't need to", level=Logger.INFO) if LAYER > 0 and np.sum(known_T.forward(point) != 0) <= 1: logger.log("Not enough hidden values are active to get meaningful data", level=Logger.INFO) continue if not check_fn(point): # print("Check function rejected it") continue if CHEATING: logger.log("What layer is this neuron on (by cheating)?", [(np.min(np.abs(x)), np.argmin(np.abs(x))) for x in cheat_get_inner_layers(point)], level=Logger.INFO) tmp = Tracker().query_count for EPS in [GRAD_EPS, GRAD_EPS / 10, GRAD_EPS / 100]: try: normal = get_ratios_lstsq(LAYER, [point], [range(DIM)], known_T, eps=EPS)[0].flatten() # normal = get_ratios([point], [range(DIM)], eps=EPS)[0].flatten() break except AcceptableFailure: logger.log("Try again with smaller eps", level=Logger.INFO) pass # print("LSTSQ Delta queries", query_count-tmp) this_layer_critical_points.append((normal, point)) # coupon collector: we need nlogn points. logger.log("Up to", len(this_layer_critical_points), 'of', COUNT, level=Logger.INFO) if len(this_layer_critical_points) >= COUNT: break return this_layer_critical_points
def main(input, project, skip_tracking=False): if not skip_tracking: # TODO check if input is a csv or a folder df = pd.read_csv(input) tr = Tracker(project=project) all_results = [] v = None old = None for i, x in tqdm(df.iterrows(), total=len(df)): start = int(x['start']) if 'start' in x else None end = int(x['end']) if 'end' in x else None fragment = f'{start},{end+1}' if start is not None else None media = x['media'] if media != old: v, metadata = uri_utils.uri2video(media) res = tr.run(v, export_frames=True, fragment=fragment, video_id=x['media'], verbose=False) all_results.append(res) with open(f'results_{project}.json', 'w') as f: json.dump(all_results, f) else: with open(f'results_{project}.json', 'r') as f: all_results = json.load(f) clusters = [] for r in all_results: c = clusterize.main(clusterize.from_dict(r), dominant_ratio=0.6, weighted_dominant_ratio=0.4, confidence_threshold=0.6, merge_cluster=True, min_length=1) clusters.append(c) with open(f'results_{project}_clusters.json', 'w') as f: json.dump(clusters, f)
async def download(torrent_file: str, download_location: str, loop=None): # Parse torrent file torrent = Torrent(torrent_file) LOG.info('Torrent: {}'.format(torrent)) torrent_writer = FileSaver(download_location, torrent) session = DownloadSession(torrent, torrent_writer.get_received_blocks_queue()) # Instantiate tracker object tracker = Tracker(torrent) peers_info = await tracker.get_peers() seen_peers = set() peers = [Peer(session, host, port) for host, port in peers_info] seen_peers.update([str(p) for p in peers]) LOG.info('[Peers] {}'.format(seen_peers)) await (asyncio.gather(*[peer.download() for peer in peers]))
def __init__(self): # load the serialized model from disk self.net = cv2.dnn.readNet(car_detection_bin_model, car_detection_xml_model) self.net2 = cv2.dnn.readNet(car_classification_bin_model, car_classification_xml_model) self.net3 = cv2.dnn.readNet(plate_detecttion_bin_model, plate_detection_xml_model) # initialize the tracker and frame dimensions self.tr = Tracker(df) self.last_ids = [] self.last_positions = [] self.dt = 0 self.frame_num = 1 self.start = 0 self.ppm = 0 self.fn = 1 self.ids = [] self.fns = [] self.ids2 = [] self.sp = [] self.cnt = 0
def __init__(self, init_thread): self.tracker = Tracker(self, init_thread) self.thread = create_and_start_thread(self.tracker.loop)
def run_full_attack(): extracted_normals = [] extracted_biases = [] known_T = KnownT(extracted_normals, extracted_biases) for layer_num in range(0, len(A) - 1): # For each layer of the network ... # First setup the critical points generator critical_points = sweep_for_critical_points(PARAM_SEARCH_AT_LOCATION, known_T) # Extract weights corresponding to those critical points extracted_normal, extracted_bias, mask = layer_recovery.compute_layer_values( critical_points, known_T, layer_num) # Report how well we're doing check_quality(layer_num, extracted_normal, extracted_bias) # Now, make them more precise extracted_normal, extracted_bias = refine_precision.improve_layer_precision( layer_num, known_T, extracted_normal, extracted_bias) logger.log("Query count", Tracker().query_count, level=Logger.INFO) # And print how well we're doing check_quality(layer_num, extracted_normal, extracted_bias) # New generator critical_points = sweep_for_critical_points(1e1) # Solve for signs if layer_num == 0 and sizes[1] <= sizes[0]: extracted_sign = sign_recovery.solve_contractive_sign( known_T, extracted_normal, extracted_bias, layer_num) elif layer_num > 0 and sizes[1] <= sizes[0] and all( sizes[x + 1] <= sizes[x] / 2 for x in range(1, len(sizes) - 1)): try: extracted_sign = sign_recovery.solve_contractive_sign( known_T, extracted_normal, extracted_bias, layer_num) except AcceptableFailure as e: logger.log( "Contractive solving failed; fall back to noncontractive method", level=Logger.INFO) if layer_num == len(A) - 2: logger.log("Solve final two", level=Logger.INFO) break extracted_sign, _ = sign_recovery.solve_layer_sign( known_T, extracted_normal, extracted_bias, critical_points, layer_num, l1_mask=np.int32(np.sign(mask))) else: if layer_num == len(A) - 2: logger.log("Solve final two", level=Logger.INFO) break extracted_sign, _ = sign_recovery.solve_layer_sign( known_T, extracted_normal, extracted_bias, critical_points, layer_num, l1_mask=np.int32(np.sign(mask))) logger.log("Extracted", extracted_sign, level=Logger.INFO) logger.log('real sign', np.int32(np.sign(mask)), level=Logger.INFO) logger.log("Total query count", Tracker().query_count, level=Logger.INFO) # Correct signs extracted_normal *= extracted_sign extracted_bias *= extracted_sign extracted_bias = np.array(extracted_bias, dtype=np.float64) # Report how we're doing extracted_normal, extracted_bias = check_quality(layer_num, extracted_normal, extracted_bias, do_fix=True) extracted_normals.append(extracted_normal) extracted_biases.append(extracted_bias) known_T = KnownT(extracted_normals, extracted_biases) for a, b in sorted(Tracker().query_count_at.items(), key=lambda x: -x[1]): logger.log('count', b, '\t', 'line:', a, ':', self_lines[a - 1].strip(), level=Logger.INFO) # And then finish up if len(extracted_normals) == len(sizes) - 2: logger.log("Just solve final layer", level=Logger.INFO) N = int(Tracker().nr_of_queries / 1000) or 1 ins, outs = zip(*Tracker().saved_queries[::N]) solve_final_layer(known_T, np.array(ins), np.array(outs)) else: logger.log("Solve final two", level=Logger.INFO) solve_final_two_layers(known_T, extracted_normal, extracted_bias)
def solve_final_two_layers(known_T, known_A0, known_B0): ## Recover the final two layers through brute forcing signs, then least squares ## Yes, this is mostly a copy of solve_layer_sign. I am repeating myself. Sorry. LAYER = len(sizes) - 2 filtered_inputs = [] filtered_outputs = [] # How many unique points to use. This seems to work. Tweak if needed... # (In checking consistency of the final layer signs) N = int(Tracker().nr_of_queries / 100) or 1 ins, outs = zip(*Tracker().saved_queries[::N]) filtered_inputs, filtered_outputs = zip(*Tracker().saved_queries[::N]) logger.log('Total query count', Tracker().nr_of_queries, level=Logger.INFO) logger.log("Solving on", len(filtered_inputs), level=Logger.INFO) inputs, outputs = np.array(filtered_inputs), np.array(filtered_outputs) known_hidden_so_far = known_T.forward(inputs, with_relu=True) K = sizes[LAYER] logger.log("K IS", K, level=Logger.INFO) shuf = list(range(1 << K))[::-1] logger.log("Here before start", known_hidden_so_far.shape, level=Logger.INFO) start_time = time.time() extra_args_tup = (known_A0, known_B0, LAYER - 1, known_hidden_so_far, K, -outputs) def shufpp(s): for elem in s: yield elem, extra_args_tup # Brute force all sign assignments... all_res = pool[0].map(sign_recovery.is_solution, shufpp(shuf)) end_time = time.time() scores = [r[0] for r in all_res] solution_attempts = sum([r[1] for r in all_res]) total_attempts = len(all_res) logger.log("Attempts at solution:", (solution_attempts), 'out of', level=Logger.INFO) logger.log("Took", end_time - start_time, 'seconds', level=Logger.INFO) std = np.std([x[0] for x in scores]) logger.log('std', std, level=Logger.INFO) logger.log('median', np.median([x[0] for x in scores]), level=Logger.INFO) logger.log('min', np.min([x[0] for x in scores]), level=Logger.INFO) score, recovered_signs, final = min(scores, key=lambda x: x[0]) logger.log('recover', recovered_signs, level=Logger.INFO) known_A0 *= recovered_signs known_B0 *= recovered_signs out = known_T.extend_by(known_A0, known_B0) return solve_final_layer(out, inputs, outputs)
def compute_layer_values(critical_points, known_T, LAYER): if LAYER == 0: COUNT = neuron_count[LAYER + 1] * 3 else: COUNT = neuron_count[LAYER + 1] * np.log(sizes[LAYER + 1]) * 3 # type: [(ratios, critical_point)] this_layer_critical_points = [] partial_weights = None partial_biases = None def check_fn(point): if partial_weights is None: return True hidden = matmul(known_T.forward(point, with_relu=True), partial_weights.T, partial_biases) if np.any(np.abs(hidden) < 1e-4): return False return True logger.log("", level=Logger.INFO) logger.log("Start running critical point search to find neurons on layer", LAYER, level=Logger.INFO) while True: logger.log("At this iteration I have", len(this_layer_critical_points), "critical points", level=Logger.INFO) def reuse_critical_points(): for witness in critical_points: yield witness this_layer_critical_points.extend(gather_ratios(reuse_critical_points(), known_T, check_fn, LAYER, COUNT)) logger.log("Query count after that search:", Tracker().query_count, level=Logger.INFO) logger.log("And now up to ", len(this_layer_critical_points), "critical points", level=Logger.INFO) ## filter out duplicates filtered_points = [] # Let's not add points that are identical to onees we've already done. for i, (ratio1, point1) in enumerate(this_layer_critical_points): for ratio2, point2 in this_layer_critical_points[i + 1:]: if np.sum((point1 - point2) ** 2) ** .5 < 1e-10: break else: filtered_points.append((ratio1, point1)) this_layer_critical_points = filtered_points logger.log("After filtering duplicates we're down to ", len(this_layer_critical_points), "critical points", level=Logger.INFO) logger.log("Start trying to do the graph solving", level=Logger.INFO) try: critical_groups, extracted_normals = graph_solve([x[0] for x in this_layer_critical_points], [x[1] for x in this_layer_critical_points], neuron_count[LAYER + 1], LAYER=LAYER, debug=True) break except GatherMoreData as e: logger.log("Graph solving failed because we didn't explore all sides of at least one neuron", level=Logger.INFO) logger.log("Fall back to the hyperplane following algorithm in order to get more data", level=Logger.INFO) def mine(r): while len(r) > 0: logger.log("Yielding a point", level=Logger.INFO) yield r[0] r = r[1:] logger.log("No more to give!", level=Logger.INFO) prev_T = KnownT(known_T.A[:-1], known_T.B[:-1]) _, more_critical_points = sign_recovery.solve_layer_sign(prev_T, known_T.A[-1], known_T.B[-1], mine(e.data), LAYER - 1, already_checked_critical_points=True, only_need_positive=True) logger.log("Add more", len(more_critical_points), level=Logger.INFO) this_layer_critical_points.extend(gather_ratios(more_critical_points, known_T, check_fn, LAYER, 1e6)) logger.log("Done adding", level=Logger.INFO) COUNT = neuron_count[LAYER + 1] except AcceptableFailure as e: logger.log("Graph solving failed; get more points", level=Logger.INFO) COUNT = neuron_count[LAYER + 1] if 'partial_solution' in dir(e): if len(e.partial_solution[0]) > 0: partial_weights, corresponding_examples = e.partial_solution logger.log("Got partial solution with shape", partial_weights.shape, level=Logger.INFO) if CHEATING: logger.log("Corresponding to", np.argmin( np.abs(cheat_get_inner_layers([x[0] for x in corresponding_examples])[LAYER]), axis=1), level=Logger.INFO) partial_biases = [] for weight, examples in zip(partial_weights, corresponding_examples): hidden = known_T.forward(examples, with_relu=True) logger.log("hidden", np.array(hidden).shape, level=Logger.INFO) bias = -np.median(np.dot(hidden, weight)) partial_biases.append(bias) partial_biases = np.array(partial_biases) logger.log("Number of critical points per cluster", [len(x) for x in critical_groups], level=Logger.INFO) point_per_class = [x[0] for x in critical_groups] extracted_normals = np.array(extracted_normals).T # Compute the bias because we know wx+b=0 extracted_bias = [matmul(known_T.forward(point_per_class[i], with_relu=True), extracted_normals[:, i], c=None) for i in range(neuron_count[LAYER + 1])] # Don't forget to negate it. # That's important. # No, I definitely didn't forget this line the first time around. extracted_bias = -np.array(extracted_bias) # For the failed-to-identify neurons, set the bias to zero extracted_bias *= np.any(extracted_normals != 0, axis=0)[:, np.newaxis] if CHEATING: # Compute how far we off from the true matrix real_scaled = A[LAYER] / A[LAYER][0] extracted_scaled = extracted_normals / extracted_normals[0] mask = [] reorder_rows = [] for i in range(len(extracted_bias)): which_idx = np.argmin(np.sum(np.abs(real_scaled - extracted_scaled[:, [i]]), axis=0)) reorder_rows.append(which_idx) mask.append((A[LAYER][0, which_idx])) logger.log('matrix norm difference', np.sum(np.abs(extracted_normals * mask - A[LAYER][:, reorder_rows])), level=Logger.INFO) else: mask = [1] * len(extracted_bias) return extracted_normals, extracted_bias, mask
def start(sequence_path, detection_path, output_path, min_conf, nms_thresh, min_detect_height_thresh, cosine_thresh,nn_budget, disp): image_dir = os.path.join(sequence_path, "img1") images = { int(os.path.splitext(f)[0]): os.path.join(image_dir, f) for f in os.listdir(image_dir)} gt_file = os.path.join(sequence_path, "gt/gt.txt") detections = None if detection_path is not None: detections = np.load(detection_path) gt = None if os.path.exists(gt_file): gt = np.loadtxt(gt_file, delimiter=',') if len(images) > 0: image = cv2.imread(next(iter(images.values())), cv2.IMREAD_GRAYSCALE) image_shape = image.shape else: image_shape = None if len(images) > 0: min_idx = min(images.keys()) max_idx = max(images.keys()) else: min_idx = int(detections[:, 0].min()) max_idx = int(detections[:, 0].max()) info_filename = os.path.join(sequence_path, "seqinfo.ini") if os.path.exists(info_filename): with open(info_filename, "r") as f: line_splits = [l.split('=') for l in f.read().splitlines()[1:]] info_dict = dict( s for s in line_splits if isinstance(s, list) and len(s) == 2) update_ms = 1000 / int(info_dict["frameRate"]) else: update_ms = None print(len(detections)) feature_dim = detections.shape[1] - 10 if detections is not None else 0 info = { "sequence_name": os.path.basename(sequence_path), "images": images, "detections": detections, "gt": gt, "image_shape": image_shape, "min_idx": min_idx, "max_idx": max_idx, "feature_dim": feature_dim, "update_ms": update_ms } metric = NNDistanceMetric(cosine_thresh, nn_budget) trker = Tracker(metric) outputs = [] def process_frames(visualizer, index_frame): detection_list = [] for row in info["detections"][info["detections"][:, 0].astype(np.int) == index_frame]: bbox, confidence, feature = row[2:6], row[6], row[10:] if bbox[3] < min_detect_height_thresh: continue detection_list.append(Detection(bbox, confidence, feature)) detections = [d for d in detection_list if d.score >= min_conf] indices = nms( np.array([d.bbox for d in detections]), nms_thresh, np.array([d.score for d in detections])) detections = [detections[i] for i in indices] trker.predict_tracker() trker.update_tracker(detections) if disp=="True": img = cv2.imread(info["images"][index_frame], cv2.IMREAD_COLOR) visualizer.image = img visualizer.detections(detections) visualizer.trackers(trker.track_list) for trk in trker.track_list: if not trk.state==2 or trk.last_update > 1: continue bbox = trk.to_bbox() outputs.append([ index_frame, trk.id, bbox[0], bbox[1], bbox[2], bbox[3]]) if disp =="False": while info['min_idx'] <= info['max_idx']: process_frames(None,info['min_idx']) info['min_idx'] += 1 else: vis = visualize.Visualization(info, time_to_update_in_ms=15) vis.start_viewer_(process_frames)
from src.tracker import Tracker if __name__ == "__main__": Tracker()
logging.getLogger('src.discord_handler').addHandler(handler) logging.getLogger('src.epicinium_client').setLevel(log_level) logging.getLogger('src.epicinium_client').addHandler(handler) epicinium_application_id = config['application-id'] guild_id = config['guild-id'] listen_to_dm = config['listen-to-dm'] intents = discord.Intents.default() intents.members = True intents.presences = True bot = commands.Bot(command_prefix='!', help_command=None, intents=intents) bot.add_cog(State()) bot.add_cog(Tracker(bot)) bot.add_cog(DiscordManager(bot, guild_id)) bot.add_cog(BotData(bot)) bot.add_cog(DynoPlaceholder()) bot.add_cog(DiscordHandler(bot)) bot.add_cog(EpiciniumClient(bot, config)) @bot.event async def on_ready(): log.info("Logged in as {0.user}".format(bot)) print("Logged in as {0.user}".format(bot)) tracker = cast(Tracker, bot.get_cog('Tracker')) tracker.go_online()
def get_more_points(NUM): """ Gather more points. This procedure is really kind of ugly and should probably be fixed. We want to find points that are near where we expect them to be. So begin by finding preimages to points that are on the line with gradient descent. This should be completely possible, because we have d_0 input dimensions but only want to control one inner layer. """ logger.log("Gather some more actual critical points on the plane", level=Logger.INFO) stepsize = .1 critical_points = [] while len(critical_points) <= NUM: logger.log("On this iteration I have ", len(critical_points), "critical points on the plane", level=Logger.INFO) points = np.random.normal(0, 1e3, size=( 100, DIM, )) lr = 10 for step in range(5000): # Use JaX's built in optimizer to do this. # We want to adjust the LR so that we get a better solution # as we optimize. Probably there is a better way to do this, # but this seems to work just fine. # No queries involvd here. if step % 1000 == 0: lr *= .5 init, opt_update, get_params = jax.experimental.optimizers.adam( lr) @jax.jit def update(i, opt_state, batch): params = get_params(opt_state) return opt_update(i, loss_grad(batch, row), opt_state) opt_state = init(points) if step % 100 == 0: ell = loss(points, row) if CHEATING: # This isn't cheating, but makes things prettier print(ell) if ell < 1e-5: break opt_state = update(step, opt_state, points) points = opt_state.packed_state[0][0] for point in points: # For each point, try to see where it actually is. # First, if optimization failed, then abort. if loss(point, row) > 1e-5: continue if LAYER > 0: # If wee're on a deeper layer, and if a prior layer is zero, then abort if min( np.min(np.abs(x)) for x in known_T.get_hidden_layers(point)) < 1e-4: logger.log("is on prior", level=Logger.INFO) continue # print("Stepsize", stepsize) tmp = Tracker().query_count solution = do_better_sweep(offset=point, low=-stepsize, high=stepsize, known_T=known_T) # print("qs", query_count-tmp) if len(solution) == 0: stepsize *= 1.1 elif len(solution) > 1: stepsize /= 2 elif len(solution) == 1: stepsize *= 0.98 potential_solution = solution[0] hiddens = extended_T.get_hidden_layers(potential_solution) this_hidden_vec = extended_T.forward(potential_solution) this_hidden = np.min(np.abs(this_hidden_vec)) if min(np.min(np.abs(x)) for x in this_hidden_vec) > np.abs(this_hidden) * 0.9: critical_points.append(potential_solution) else: logger.log("Reject it", level=Logger.INFO) logger.log("Finished with a total of", len(critical_points), "critical points", level=Logger.INFO) return critical_points
def main(track_alg): warnings.filterwarnings('ignore') path = get_path() beetle_tracker = Tracker(video_path=path, track_alg=track_alg) # read video video = cv2.VideoCapture(find_data_file(beetle_tracker._video)) out = cv2.VideoWriter( "tracked_%s" % beetle_tracker.file_name, beetle_tracker.fourcc, beetle_tracker.fps, (beetle_tracker.resolution[0], beetle_tracker.resolution[1] + 80)) # exit if video not opend if not video.isOpened(): beetle_tracker.alert( 'Could not open video: %s \n %s' % (beetle_tracker._video, find_data_file(beetle_tracker._video))) sys.exit() # store the length of frame and read the first frame beetle_tracker._frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) ok, frame = video.read() # setup up the window and mouse callback cv2.namedWindow(beetle_tracker.window_name, cv2.WINDOW_AUTOSIZE) cv2.setMouseCallback(beetle_tracker.window_name, beetle_tracker._mouse_ops) while True: # Read a new frame and wait for a keypress video.set(cv2.CAP_PROP_POS_FRAMES, beetle_tracker.count - 1) ok, beetle_tracker.frame = video.read() beetle_tracker._fix_target = False key = cv2.waitKey(1) # check if we have reached the end of the video if not ok: break # resize the frame into 960 x 720 beetle_tracker._init_frame() beetle_tracker.detect_rat_contour() if len(beetle_tracker._roi) > 0: beetle_tracker._roi = [ convert(a[0], a[1], a[2], a[3]) for a in beetle_tracker._bboxes ] # if this is init mode, let user targets the beetles if beetle_tracker._add_box: time.sleep(0.2) beetle_tracker._add_bboxes() if beetle_tracker.count > 1: beetle_tracker._start = time.clock() # run stop model if len(beetle_tracker._bboxes) > 0 and beetle_tracker._run_model: beetle_tracker._is_stop, beetle_tracker._stop_obj = beetle_tracker.detect_and_auto_update( RESIZE, N_MAX) if beetle_tracker._run_motion: beetle_tracker._motion_detector(RESIZE, N_MAX) beetle_tracker._draw_bbox() cv2.imshow(beetle_tracker.window_name, beetle_tracker.frame) beetle_tracker._ask_add_box() # if 'r' was pressed or stop model return True, enter to retarget mode if key == KEY_RETARGET or beetle_tracker._is_stop: if len(beetle_tracker._bboxes) > 0: beetle_tracker._retarget_bboxes() else: beetle_tracker._add_bboxes() # if 'a' was pressed, enter add boudning box mode elif key == KEY_ADD: beetle_tracker._add_bboxes() # if 'd' was pressed, enter delete boudning box mode elif key == KEY_DELETE: beetle_tracker._delete_bboxes() elif key == KEY_CONTINUE: beetle_tracker._pause_frame() elif key == KEY_MODEL: beetle_tracker._run_model = not beetle_tracker._run_model elif key == KEY_MOTION: beetle_tracker._run_motion = not beetle_tracker._run_motion elif key == KEY_HELP: beetle_tracker.help() # elif key == KEY_UPDATE: # beetle_tracker._update = not beetle_tracker._update elif key == KEY_JUMP: beetle_tracker._jump_frame() # restart the program elif key == KEY_CHANGE: cv2.destroyAllWindows() main(track_alg=TRACK_ALGORITHM) # friendly switch on off for detector elif key in [ord('1'), ord('2'), ord('3'), ord('4')]: beetle_tracker.switch(key) elif key == KEY_RAT: beetle_tracker._show_rat = not beetle_tracker._show_rat # otherwise, update bounding boxes from tracker else: ok, beetle_tracker._bboxes = beetle_tracker.tracker.update( beetle_tracker.frame) beetle_tracker._roi = [ convert(a[0], a[1], a[2], a[3]) for a in beetle_tracker._bboxes ] if key == KEY_ESC or (cv2.getWindowProperty(beetle_tracker.window_name, 0) < 0): # draw current frame beetle_tracker._draw_bbox() cv2.imshow(beetle_tracker.window_name, beetle_tracker.frame) if beetle_tracker._ask_quit(): break else: pass if ok: beetle_tracker.frame = beetle_tracker.orig_col.copy() # draw current frame beetle_tracker._draw_bbox() # append trace and img beetle_tracker._append_record() # save image inside the bounding boxes beetle_tracker._write_bboxes() if args['save_pos'] and len(beetle_tracker.object_name) > 0: beetle_tracker._save_pos() # write current frame to output video out.write(beetle_tracker.frame) beetle_tracker.count += 1 beetle_tracker._n_pass_frame += 1 else: break # Display result cv2.imshow(beetle_tracker.window_name, beetle_tracker.frame) video.release() out.release() cv2.destroyAllWindows()
def solve_layer_sign(known_T, known_A0, known_B0, critical_points, LAYER, already_checked_critical_points=False, only_need_positive=False, l1_mask=None): """ Compute the signs for one layer of the network. known_T is the transformation that computes up to layer K-1, with known_A and known_B being the layer K matrix up to sign. """ def get_critical_points(): logger.log("Init", level=Logger.INFO) logger.log(critical_points, level=Logger.INFO) for point in critical_points: logger.log("Tick", level=Logger.INFO) if already_checked_critical_points or is_on_following_layer(known_T, known_A0, known_B0, point): logger.log("Found layer N point at ", point, already_checked_critical_points, level=Logger.INFO) yield point get_critical_point = get_critical_points() logger.log("Start looking for critical point", level=Logger.INFO) MAX_POINTS = 200 which_point = next(get_critical_point) logger.log("Done looking for critical point", level=Logger.INFO) initial_points = [] history = [] pts = [] if already_checked_critical_points: for point in get_critical_point: initial_points.append(point) pts.append(point) which_polytope = get_polytope_at(known_T, known_A0, known_B0, point, False) # [-1 1 -1] hidden_vector = get_hidden_at(known_T, known_A0, known_B0, LAYER, point, False) if CHEATING: layers = cheat_get_inner_layers(point) logger.log('have', [(np.argmin(np.abs(x)), np.min(np.abs(x))) for x in layers], level=Logger.INFO) history.append((which_polytope, hidden_vector, np.copy(point))) while True: if not already_checked_critical_points: history = [] pts = [] prev_count = -10 good = False while len(pts) > prev_count + 2: logger.log("======" * 10, level=Logger.INFO) logger.log("RESTART SEARCH", len(pts), prev_count, level=Logger.INFO) logger.log(which_point, level=Logger.INFO) prev_count = len(pts) more_points, done = follow_hyperplane(LAYER, which_point, known_T, known_A0, known_B0, history=history, only_need_positive=only_need_positive) pts.extend(more_points) if len(pts) >= MAX_POINTS: logger.log("Have enough; break", level=Logger.INFO) break if len(pts) == 0: break neuron_values = known_T.extend_by(known_A0, known_B0).forward(pts) neuron_positive_count = np.sum(neuron_values > 1, axis=0) neuron_negative_count = np.sum(neuron_values < -1, axis=0) logger.log("Counts", level=Logger.INFO) logger.log(neuron_positive_count, level=Logger.INFO) logger.log(neuron_negative_count, level=Logger.INFO) logger.log("SHOULD BE DONE?", done, only_need_positive, level=Logger.INFO) if done and only_need_positive: good = True break if np.all(neuron_positive_count > 0) and np.all(neuron_negative_count > 0) or \ (only_need_positive and np.all(neuron_positive_count > 0)): logger.log("Have all the points we need (2)", level=Logger.INFO) good = True break if len(pts) < MAX_POINTS / 2 and good == False: logger.log("=======" * 10, level=Logger.INFO) logger.log("Select a new point to start from", level=Logger.INFO) logger.log("=======" * 10, level=Logger.INFO) if already_checked_critical_points: logger.log("CHOOSE FROM", len(initial_points), initial_points, level=Logger.INFO) which_point = initial_points[np.random.randint(0, len(initial_points) - 1)] else: which_point = next(get_critical_point) else: logger.log("Abort", level=Logger.INFO) break critical_points = np.array(pts) # sorted(list(set(map(tuple,pts)))) logger.log("Now have critical points", len(critical_points), level=Logger.INFO) if CHEATING: layer = [[np.min(np.abs(x)) for x in cheat_get_inner_layers(x[np.newaxis, :])][LAYER + 1] for x in critical_points] # print("Which layer is zero?", sorted(layer)) layer = np.abs(cheat_get_inner_layers(np.array(critical_points))[LAYER + 1]) logger.log(layer, level=Logger.INFO) which_is_zero = np.argmin(layer, axis=1) logger.log("Which neuron is zero?", which_is_zero, level=Logger.INFO) which_is_zero = which_is_zero[0] logger.log("Query count", Tracker().query_count, level=Logger.INFO) K = neuron_count[LAYER + 1] MAX = (1 << K) if already_checked_critical_points: bounds = [(MAX - 1, MAX)] else: bounds = [] for i in range(1024): bounds.append(((MAX * i) // 1024, (MAX * (i + 1)) // 1024)) logger.log("Created a list", level=Logger.INFO) known_hidden_so_far = known_T.forward(critical_points, with_relu=True) debug = False start_time = time.time() extra_args_tup = (known_A0, known_B0, LAYER, known_hidden_so_far, K, None) all_res = pool[0].map(is_solution_map, [(bound, extra_args_tup) for bound in bounds]) end_time = time.time() logger.log("Done map, now collect results", level=Logger.INFO) logger.log("Took", end_time - start_time, 'seconds', level=Logger.INFO) all_res = [x for y in all_res for x in y] scores = [r[0] for r in all_res] solution_attempts = sum([r[1] for r in all_res]) total_attempts = len(all_res) logger.log("Attempts at solution:", (solution_attempts), 'out of', total_attempts, level=Logger.INFO) std = np.std([x[0] for x in scores]) logger.log('std', std, level=Logger.INFO) logger.log('median', np.median([x[0] for x in scores]), level=Logger.INFO) logger.log('min', np.min([x[0] for x in scores]), level=Logger.INFO) return min(scores, key=lambda x: x[0])[1], critical_points
def follow_hyperplane(LAYER, start_point, known_T, known_A, known_B, history=[], MAX_POINTS=1e3, only_need_positive=False): """ This is the ugly algorithm that will let us recover sign for expansive networks. Assumes we have extracted up to layer K-1 correctly, and layer K up to sign. start_point is a neuron on layer K+1 known_T is the transformation that computes up to layer K-1, with known_A and known_B being the layer K matrix up to sign. We're going to come up with a bunch of different inputs, each of which has the same critical point held constant at zero. """ def choose_new_direction_from_minimize(previous_axis): """ Given the current point which is at a critical point of the next layer neuron, compute which direction we should travel to continue with finding more points on this hyperplane. Our goal is going to be to pick a direction that lets us explore a new part of the space we haven't seen before. """ logger.log("Choose a new direction to travel in", level=Logger.INFO) if len(history) == 0: which_to_change = 0 new_perp_dir = perp_dir new_start_point = start_point initial_signs = get_polytope_at(known_T, known_A, known_B, start_point) # If we're in the 1 region of the polytope then we try to make it smaller # otherwise make it bigger fn = min if initial_signs[0] == 1 else max else: neuron_values = np.array([x[1] for x in history]) neuron_positive_count = np.sum(neuron_values > 1, axis=0) neuron_negative_count = np.sum(neuron_values < -1, axis=0) mean_plus_neuron_value = neuron_positive_count / (neuron_positive_count + neuron_negative_count + 1) mean_minus_neuron_value = neuron_negative_count / (neuron_positive_count + neuron_negative_count + 1) # we want to find values that are consistently 0 or 1 # So map 0 -> 0 and 1 -> 0 and the middle to higher values if only_need_positive: neuron_consistency = mean_plus_neuron_value else: neuron_consistency = mean_plus_neuron_value * mean_minus_neuron_value # Print out how much progress we've made. # This estimate is probably worse than Windows 95's estimated time remaining. # At least it's monotonic. Be thankful for that. logger.log("Progress", "%.1f" % int(np.mean(neuron_consistency != 0) * 100) + "%", level=Logger.INFO) logger.log("Counts on each side of each neuron", level=Logger.INFO) logger.log(neuron_positive_count, level=Logger.INFO) logger.log(neuron_negative_count, level=Logger.INFO) # Choose the smallest value, which is the most consistent which_to_change = np.argmin(neuron_consistency) logger.log("Try to explore the other side of neuron", which_to_change, level=Logger.INFO) if which_to_change != previous_axis: if previous_axis is not None and neuron_consistency[previous_axis] == neuron_consistency[ which_to_change]: # If the previous thing we were working towards has the same value as this one # the don't change our mind and just keep going at that one # (almost always--sometimes we can get stuck, let us get unstuck) which_to_change = previous_axis new_start_point = start_point new_perp_dir = perp_dir else: valid_axes = np.where(neuron_consistency == neuron_consistency[which_to_change])[0] best = (np.inf, None, None) for _, potential_hidden_vector, potential_point in history[-1:]: for potential_axis in valid_axes: value = potential_hidden_vector[potential_axis] if np.abs(value) < best[0]: best = (np.abs(value), potential_axis, potential_point) _, which_to_change, new_start_point = best new_perp_dir = perp_dir else: new_start_point = start_point new_perp_dir = perp_dir # If we're in the 1 region of the polytope then we try to make it smaller # otherwise make it bigger fn = min if neuron_positive_count[which_to_change] > neuron_negative_count[which_to_change] else max arg_fn = np.argmin if neuron_positive_count[which_to_change] > neuron_negative_count[ which_to_change] else np.argmax logger.log("Changing", which_to_change, 'to flip sides because mean is', mean_plus_neuron_value[which_to_change], level=Logger.INFO) val = matmul(known_T.forward(new_start_point, with_relu=True), known_A, known_B)[which_to_change] initial_signs = get_polytope_at(known_T, known_A, known_B, new_start_point) # Now we're going to figure out what direction makes this biggest/smallest # this doesn't take any queries # There's probably an analytical way to do this. # But thinking is hard. Just try 1000 random angles. # There are no queries involved in this process. choices = [] for _ in range(1000): random_dir = np.random.normal(size=DIM) perp_component = np.dot(random_dir, new_perp_dir) / (np.dot(new_perp_dir, new_perp_dir)) * new_perp_dir parallel_dir = random_dir - perp_component # This is the direction we're going to travel in. go_direction = parallel_dir / np.sum(parallel_dir ** 2) ** .5 try: a_bit_further, high = binary_search_towards(known_T, known_A, known_B, new_start_point, initial_signs, go_direction) except AcceptableFailure: continue if a_bit_further is None: continue # choose a direction that makes the Kth value go down by the most val = matmul(known_T.forward(a_bit_further[np.newaxis, :], with_relu=True), known_A, known_B)[0][ which_to_change] # print('\t', val, high) choices.append([val, new_start_point + high * go_direction]) best_value, multiple_intersection_point = fn(choices, key=lambda x: x[0]) logger.log('Value', best_value, level=Logger.INFO) return new_start_point, multiple_intersection_point, which_to_change ################################################### ### Actual code to do the sign recovery starts. ### ################################################### start_box_step = 0 points_on_plane = [] if CHEATING: layer = np.abs(cheat_get_inner_layers(np.array(start_point))[LAYER + 1]) logger.log("Layer", layer, level=Logger.INFO) which_is_zero = np.argmin(layer) current_change_axis = 0 while True: logger.log("\n\n", level=Logger.INFO) logger.log("-----" * 10, level=Logger.INFO) if CHEATING: layer = np.abs(cheat_get_inner_layers(np.array(start_point))[LAYER + 1]) # print('layer',LAYER+1, layer) # print('all inner layers') # for e in cheat_get_inner_layers(np.array(start_point)): # print(e) which_is_zero_2 = np.argmin(np.abs(layer)) if which_is_zero_2 != which_is_zero: logger.log("STARTED WITH", which_is_zero, "NOW IS", which_is_zero_2, level=Logger.INFO) logger.log(layer, level=Logger.INFO) raise # Keep track of where we've been, so we can go to new places. which_polytope = get_polytope_at(known_T, known_A, known_B, start_point, False) # [-1 1 -1] hidden_vector = get_hidden_at(known_T, known_A, known_B, LAYER, start_point, False) sign_at_init = sign_to_int(which_polytope) # 0b010 -> 2 logger.log("Number of collected points", len(points_on_plane), level=Logger.INFO) if len(points_on_plane) > MAX_POINTS: return points_on_plane, False neuron_values = np.array([x[1] for x in history]) neuron_positive_count = np.sum(neuron_values > 1, axis=0) neuron_negative_count = np.sum(neuron_values < -1, axis=0) if (np.all(neuron_positive_count > 0) and np.all(neuron_negative_count > 0)) or \ (only_need_positive and np.all(neuron_positive_count > 0)): logger.log("Have all the points we need (1)", level=Logger.INFO) logger.log(Tracker().query_count, level=Logger.INFO) logger.log(neuron_positive_count, level=Logger.INFO) logger.log(neuron_negative_count, level=Logger.INFO) neuron_values = np.array( [get_hidden_at(known_T, known_A, known_B, LAYER, x, False) for x in points_on_plane]) neuron_positive_count = np.sum(neuron_values > 1, axis=0) neuron_negative_count = np.sum(neuron_values < -1, axis=0) logger.log(neuron_positive_count, level=Logger.INFO) logger.log(neuron_negative_count, level=Logger.INFO) return points_on_plane, True # 1. find a way to move along the hyperplane by computing the normal # direction using the ratios function. Then find a parallel direction. try: # perp_dir = get_ratios([start_point], [range(DIM)], eps=1e-4)[0].flatten() perp_dir = get_ratios_lstsq(0, [start_point], [range(DIM)], KnownT([], []), eps=1e-5)[0].flatten() except AcceptableFailure: logger.log("Failed to compute ratio at start point. Something very bad happened.", level=Logger.ERROR) return points_on_plane, False # Record these points. history.append((which_polytope, hidden_vector, np.copy(start_point))) # We can't just pick any parallel direction. If we did, then we would # not end up covering much of the input space. # Instead, we're going to figure out which layer-1 hyperplanes are "visible" # from the current point. Then we're going to try and go reach all of them. # This is the point at which the first and second layers intersect. start_point, multiple_intersection_point, new_change_axis = choose_new_direction_from_minimize( current_change_axis) if new_change_axis != current_change_axis: start_point, multiple_intersection_point, current_change_axis = choose_new_direction_from_minimize(None) # if CHEATING: # print("INIT MULTIPLE", cheat_get_inner_layers(multiple_intersection_point)) # Refine the direction we're going to travel in---stay numerically stable. towards_multiple_direction = multiple_intersection_point - start_point step_distance = np.sum(towards_multiple_direction ** 2) ** .5 logger.log("Distance we need to step:", step_distance, level=Logger.INFO) if step_distance > 1 or True: mid_point = 1e-4 * towards_multiple_direction / np.sum(towards_multiple_direction ** 2) ** .5 + start_point random_dir = np.random.normal(size=DIM) mid_points = do_better_sweep(mid_point, perp_dir / np.sum(perp_dir ** 2) ** .5, low=-1e-3, high=1e-3, known_T=known_T) if len(mid_points) > 0: mid_point = mid_points[np.argmin(np.sum((mid_point - mid_points) ** 2, axis=1))] towards_multiple_direction = mid_point - start_point towards_multiple_direction = towards_multiple_direction / np.sum(towards_multiple_direction ** 2) ** .5 initial_signs = get_polytope_at(known_T, known_A, known_B, start_point) _, high = binary_search_towards(known_T, known_A, known_B, start_point, initial_signs, towards_multiple_direction) multiple_intersection_point = towards_multiple_direction * high + start_point # Find the angle of the next hyperplane # First, take random steps away from the intersection point # Then run the search algorithm to find some intersections # what we find will either be a layer-1 or layer-2 intersection. logger.log("Now try to find the continuation direction", level=Logger.INFO) success = None while success is None: if start_box_step < 0: start_box_step = 0 logger.log("VERY BAD FAILURE", level=Logger.INFO) logger.log("Choose a new random point to start from", level=Logger.INFO) which_point = np.random.randint(0, len(history)) start_point = history[which_point][2] logger.log("New point is", which_point, level=Logger.INFO) current_change_axis = np.random.randint(0, sizes[LAYER + 1]) logger.log("New axis to change", current_change_axis, level=Logger.INFO) break logger.log("\tStart the box step with size", start_box_step, level=Logger.INFO) try: success, camefrom, stepsize = find_plane_angle(known_T, known_A, known_B, multiple_intersection_point, sign_at_init, start_box_step) except AcceptableFailure: # Go back to the top and try with a new start point logger.log("\tOkay we need to try with a new start point", level=Logger.INFO) start_box_step = -10 start_box_step -= 2 if success is None: continue val = matmul(known_T.forward(multiple_intersection_point, with_relu=True), known_A, known_B)[new_change_axis] logger.log("Value at multiple:", val, level=Logger.INFO) val = matmul(known_T.forward(success, with_relu=True), known_A, known_B)[new_change_axis] logger.log("Value at success:", val, level=Logger.INFO) if stepsize < 10: new_move_direction = success - multiple_intersection_point # We don't want to be right next to the multiple intersection point. # So let's binary search to find how far away we can go while remaining in this polytope. # Then we'll go half as far as we can maximally go. initial_signs = get_polytope_at(known_T, known_A, known_B, success) logger.log("polytope at initial", sign_to_int(initial_signs), level=Logger.INFO) low = 0 high = 1 while high - low > 1e-2: mid = (high + low) / 2 query_point = multiple_intersection_point + mid * new_move_direction next_signs = get_polytope_at(known_T, known_A, known_B, query_point) logger.log("polytope at", mid, sign_to_int(next_signs), "%x" % (sign_to_int(next_signs) ^ sign_to_int(initial_signs)), level=Logger.INFO) if initial_signs == next_signs: low = mid else: high = mid logger.log("GO TO", mid, level=Logger.INFO) success = multiple_intersection_point + (mid / 2) * new_move_direction val = matmul(known_T.forward(success, with_relu=True), known_A, known_B)[new_change_axis] logger.log("Value at moved success:", val, level=Logger.INFO) logger.log("Adding the points to the set of known good points", level=Logger.INFO) points_on_plane.append(start_point) if camefrom is not None: points_on_plane.append(camefrom) # print("Old start point", start_point) # print("Set to success", success) start_point = success start_box_step = max(stepsize - 1, 0) return points_on_plane, False
import cv2 import numpy as np import time import imutils from filterpy.kalman import KalmanFilter from src.tracker import Tracker import argparse parser = argparse.ArgumentParser() parser.add_argument('--file_name', '-f', type=str, default=0) args = parser.parse_args() track = Tracker() #if file name is provided, setupVideoStream will use the file #otherwise it will use 0, leading to camera capture track.setupVideoStream(args.file_name) track.drawTrackbars() while (True): track.setFrame() time.sleep(.01) ball_mask = track.applyMask(track.currentFrame, track.BALL_HSV[0], track.BALL_HSV[1], "Mask") track.findContours(ball_mask) track.showFrame() track.returnTrackbarPosition() if cv2.waitKey(1) & 0xFF == ord('q'): break
}) db = firebase.database() from src.detector import Detectors from src.tracker import Tracker # from src.counting import * _lock = threading.Lock() _server_ip = '192.168.192.50' _port_number = '5000' _phone_number = '123456789' _detector = Detectors() _tracker = Tracker(dist_thresh=80, max_frames_to_skip=50, max_trace_length=5, axis='x', direction=1) _diret_id = 0 _roi_line = [] _total_in, _total_ou = 0, 0 _banner_shape = [50, 150] _spaces = [.1, .4, .8] _s_point, _e_point = (0, 0), (0, 0) track_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255), (255, 0, 255), (255, 127, 255), (127, 0, 255), (127, 0, 127)]