def load_parser(obj: dict, count: int, ret: List[dict]) -> Optional[bool]:
progress_load_filter(obj, count, ret)
load_image(obj, False)
st = '%03d' % ((count - 1) // 1000)
stored[obj[ID]] = st
store_png(OUTPUT_DIR, ['images', st], str(obj[ID]), obj[FRAME_CONTENT])
id_array.append(obj[ID])
bitmap_array.append([obj[GRAY]])
return False
def measure_angle(fn: str):
hits, count, errors = load_json('../../data/%s' % fn,
progress_and_process_image)
for h in hits:
nkg_mark_hit_area(h)
nkg_make_track(h, scale=1, downscale=False, skeleton_method='zhang')
angle = 0
if h.get(NKG_PATH):
angles = analyse_path(h.get(NKG_PATH), cut_first=1, cut_latest=1)
if len(angles):
angle = angles[0]
if math.isnan(angle):
angle = 0
store_png('/tmp/credo', [fn], '%03d_%s' % (abs(angle), str(h.get(ID))),
h.get(IMAGE))
def store_pngs(arr, subdir):
for a in arr:
store_png(
OUTPUT_DIR, [subdir, 'by_solidity'], '%.3f_%.3f_%d' %
(a[ASTROPY_SOLIDITY][0], a[ASTROPY_ELLIPTICITY][0], a[ID]),
a[IMAGE])
store_png(
OUTPUT_DIR, [subdir, 'by_ellipticity'], '%.3f_%.3f_%d' %
(a[ASTROPY_ELLIPTICITY][0], a[ASTROPY_SOLIDITY][0], a[ID]),
a[IMAGE])
s = int(a[ASTROPY_SOLIDITY][0] * 5) * 2
e = int(a[ASTROPY_ELLIPTICITY][0] * 5) * 2
store_png(OUTPUT_DIR, [
subdir, 'by_matrix_solidity_per_ellipticity',
'%02d-%02d_%02d-%02d' % (s, s + 2, e, e + 2)
], '%d' % a[ID], a[IMAGE])
def start_analyze(all_detections, log_prefix):
print('%s group by devices...' % log_prefix)
by_devices = group_by_device_id(all_detections)
print('%s ... done' % log_prefix)
dev_no = 0
dev_count = len(by_devices.keys())
for device_id, device_detections in by_devices.items():
by_resolution = group_by_resolution(device_detections)
for resolution, detections in by_resolution.items():
dev_no += 1
print(
'%s start device %d of %d, device id: %s, resolution: %dx%d, detections count: %d'
% (log_prefix, dev_no, dev_count, str(device_id),
resolution[0], resolution[1], len(detections)))
# try to merge hits on the same frame
by_frame = group_by_timestamp_division(detections)
reconstructed = 0
for timestmp, in_frame in by_frame.items():
if len(in_frame) <= 1:
continue
image = Image.new('RGBA', (resolution[0], resolution[1]),
(0, 0, 0))
for d in reversed(in_frame):
cx = d.get(X) - 30
cy = d.get(Y) - 30
w, h = (60, 60)
if DEBUG:
store_png(DEBUG_DIR, [
'reconstruct',
str(device_id),
str(timestmp), 'before'
], str(d.get(ID)), d.get(FRAME_CONTENT))
image.paste(d.get(IMAGE), (cx, cy, cx + w, cy + h))
# fix bug in early CREDO Detector App: black filled boundary 1px too large
image.paste(image.crop((cx + w - 1, cy, cx + w, cy + h)),
(cx + w, cy, cx + w + 1, cy + h))
image.paste(image.crop((cx, cy + h - 1, cx + w, cy + h)),
(cx, cy + h, cx + w, cy + h + 1))
image.paste(
image.crop((cx + w - 1, cy + h - 1, cx + w, cy + h)),
(cx + w, cy + h, cx + w + 1, cy + h + 1))
for d in in_frame:
cx = d.get(X) - 30
cy = d.get(Y) - 30
w, h = (60, 60)
hit_img = image.crop((cx, cy, cx + w, cy + h))
with BytesIO() as output:
hit_img.save(output, format="png")
d[FRAME_CONTENT] = encode_base64(output.getvalue())
if DEBUG:
store_png(DEBUG_DIR, [
'reconstruct',
str(device_id),
str(timestmp), 'after'
], str(d.get(ID)), d.get(FRAME_CONTENT))
reconstructed += 1
if DEBUG:
store_png(DEBUG_DIR,
['reconstruct', str(device_id)], str(timestmp),
image)
print('%s ... reconstructed frames: %d' %
(log_prefix, reconstructed))
def store_png_for_debug(detections: List[dict], subdirs: List[str]):
if DEBUG:
for d in detections:
store_png(DEBUG_DIR, subdirs, str(d.get(ID)), d.get(FRAME_CONTENT))
store_png(DEBUG_DIR, [*subdirs, d.get(DEVICE_ID)], str(d.get(ID)),
d.get(FRAME_CONTENT))
def save_html_and_pngs_and_labels(stage: int, kmeans: KMeans, data: dict):
sn = get_labels_file(stage)
if os.path.exists(sn):
return
# Save PNG files in file system
labels = {}
images = {}
for i in range(0, len(kmeans.labels_)):
label = kmeans.labels_[i]
_id = data['id_array'][i]
image = data['bitmap_array'][i][0]
if STORE_GRAY_CLUSTER:
store_png(OUTPUT_DIR,
['%02d' % stage, '%03d' % label], str(_id), image)
in_label = labels.get(label, [])
in_label.append(_id)
labels[label] = in_label
in_image = images.get(label, [])
in_image.append([image])
images[label] = in_image
# make normalized sum and corona sum
corona_radius = [2, 4, 8, 16]
for label, in_image in images.items():
stacked = np.vstack(in_image)
summed = np.sum(stacked, 0)
m = np.amax(summed)
image = summed * 255 / m
store_png(OUTPUT_DIR, ['%02d' % stage], 'sum_%03d' % label,
image.astype(np.uint8))
# corona:
for radius in corona_radius:
rr, cc = draw.disk((30, 30), radius=radius, shape=summed.shape)
summed[rr, cc] = 0
m = np.amax(summed)
image = summed * 255 / m
store_png(OUTPUT_DIR, ['%02d' % stage],
'sum_corona_%03d_%d' % (label, radius),
image.astype(np.uint8))
# Save HTML for preview clusters
max_files_per_cluster = 66
with open('%s/%02d.html' % (OUTPUT_DIR, stage), 'wt') as html:
html.write(html_head)
for label in sorted(labels.keys()):
html.write(' <tr><th>%d</th><th>%d</th>\n' %
(label, len(labels[label])))
html.write('<td><img src="%02d/sum_%03d.png"/></td><td>' %
(stage, label))
for radius in corona_radius:
html.write('<img src="%02d/sum_corona_%03d_%d.png"/>' %
(stage, label, radius))
html.write('</td><td>')
used = 0
for _id in labels[label]:
html.write(' <img src="images/%s/%s.png"/>\n' %
(data['stored'][_id], str(_id)))
used += 1
if used >= max_files_per_cluster:
break
html.write(' </td></tr>\n')
html.write(html_foot)
serialize(sn, labels)