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add new chirps
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gferraro committed Jun 28, 2023
1 parent 36dfeae commit 42589fd
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Showing 2 changed files with 57 additions and 102 deletions.
2 changes: 1 addition & 1 deletion Melt/chain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,7 @@ def species_identify(file_name, morepork_model, bird_models):
cacophony_index, version = calc_cacophony_index(bird_ids, length)
result["cacophony_index"] = cacophony_index
result["cacophony_index_version"] = version

result["chirps"] = chirps
result["species_identify"] = labels
result["species_identify_version"] = "2021-02-01"
return result
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157 changes: 56 additions & 101 deletions Melt/identify_bird.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,39 +319,33 @@ def classify(file, model_file):

start += segment_stride
chirps = 0
# tracks = [t for t in tracks if t.end > t.start]
# signals, noise = signal_noise(frames, sr, hop_length)
# signals = join_signals(signals, max_gap=0.2)
# sorted_tracks = [t for t in tracks if t.label in bird_labels]
# sorted_tracks = sorted(
# sorted_tracks,
# key=lambda track: track.start,
# )
# chirps = 0
# last_end = 0
# track_index = 0
# for s in signals:
# if track_index >= len(sorted_tracks):
# break
# while track_index < len(sorted_tracks):
# t = sorted_tracks[track_index]
# start = t.start
# end = t.end
# if start < last_end:
# start = last_end
# end = max(start, end)
# # overlap
# if ((end - start) + (s[1] - s[0])) > max(end, s[1]) - min(start, s[0]):
# # print("Have track", t, " for ", s, t.start, t.end, t.label)
# if t.label in bird_labels:
# chirps += 1
# if end > s[1]:
# # check next signal
# break
# elif start > s[1]:
# break
# last_end = end
# track_index += 1
tracks = [t for t in tracks if t.end > t.start]
signals = signal_noise(frames, sr, hop_length)
sorted_tracks = [t for t in tracks if t.label in bird_labels]
sorted_tracks = sorted(
sorted_tracks,
key=lambda track: track.start,
)
last_end = 0
track_index = 0
for s in signals:
if track_index >= len(sorted_tracks):
break
while track_index < len(sorted_tracks):
t = sorted_tracks[track_index]
start = t.start
end = t.end
if start < last_end:
start = last_end
end = max(start, end)
# overlap
if ((end - start) + (s[1] - s[0])) > max(end, s[1]) - min(start, s[0]):
chirps += 1
break
elif start > s[1]:
break
last_end = end
track_index += 1
return [t.get_meta() for t in tracks], length, chirps


Expand Down Expand Up @@ -392,83 +386,44 @@ def signal_noise(frames, sr, hop_length=281):
column_medians = np.repeat(column_medians, rows, axis=0)

signal = (spectogram > 3 * column_medians) & (spectogram > 3 * row_medians)
noise = (spectogram > 2.5 * column_medians) & (spectogram > 2.5 * row_medians)
noise[signal == noise] = 0
noise = noise.astype(np.uint8)

signal = signal.astype(np.uint8)
kernel = np.ones((4, 4), np.uint8)
signal = cv2.morphologyEx(signal, cv2.MORPH_OPEN, kernel)
noise = cv2.morphologyEx(noise, cv2.MORPH_OPEN, kernel)
# plot_spec(spectogram)

signal_indicator_vector = np.amax(signal, axis=0)
noise_indicator_vector = np.amax(noise, axis=0)

signal_indicator_vector = signal_indicator_vector[np.newaxis, :]
signal_indicator_vector = cv2.dilate(
signal_indicator_vector, np.ones((4, 1), np.uint8)
)
signal_indicator_vector = np.where(signal_indicator_vector > 0, 1, 0)
signal_indicator_vector = signal_indicator_vector * 255
min_width = 0.1
min_width = min_width * sr / hop_length
min_width = int(min_width)
width = 0.25 # seconds
width = width * sr / hop_length
width = int(width)
freq_range = 1000
height = 0
freqs = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
for i, f in enumerate(freqs):
if f > freq_range:
height = i + 1
break

noise_indicator_vector = noise_indicator_vector[np.newaxis, :]
noise_indicator_vector = cv2.dilate(
noise_indicator_vector, np.ones((4, 1), np.uint8)
)
noise_indicator_vector = np.where(noise_indicator_vector > 0, 1, 0)
signal = cv2.dilate(signal, np.ones((height, width), np.uint8))
signal = cv2.erode(signal, np.ones((height // 10, width), np.uint8))

noise_indicator_vector = noise_indicator_vector * 128
components, small_mask, stats, _ = cv2.connectedComponentsWithStats(signal)
stats = stats[1:]
stats = sorted(stats, key=lambda stat: stat[0])
stats = [s for s in stats if s[2] > min_width]

indicator_vector = np.concatenate(
(signal_indicator_vector, noise_indicator_vector), axis=0
)
i = 0
indicator_vector = np.uint8(indicator_vector)
# indicator_vector = np.uint8(indicator_vector)
s_start = -1
noise_start = -1
signals = []
noise = []
for c in indicator_vector.T:
# print("indicator", c)
if c[0] == 255:
if s_start == -1:
s_start = i
elif s_start != -1:
signals.append((s_start * 281 / sr, (i - 1) * 281 / sr))
s_start = -1
if c[1] == 128:
if noise_start == -1:
noise_start = i
elif noise_start != -1:
noise.append((noise_start * 281 / sr, (i - 1) * 281 / sr))
noise_start = -1

i += 1
if s_start != -1:
signals.append((s_start * 281 / sr, (i - 1) * 281 / sr))
if noise_start != -1:
noise.append((noise_start * 281 / sr, (i - 1) * 281 / sr))
return signals, noise

bins = len(freqs)
for s in stats:
max_freq = min(len(freqs) - 1, s[1] + s[3])
freq_range = (freqs[s[1]], freqs[max_freq])
start = s[0] * 281 / sr
end = (s[0] + s[2]) * 281 / sr
signals.append((start, end, freq_range[0], freq_range[1]))

# join signals that are close toghetr
def join_signals(signals, max_gap=0.1):
new_signals = []
prev_s = None
for s in signals:
if prev_s is None:
prev_s = s
else:
if s[0] < prev_s[1] + max_gap:
# combine them
prev_s = (prev_s[0], s[1])
else:
new_signals.append(prev_s)
prev_s = s
if prev_s is not None:
new_signals.append(prev_s)
#
# print("spaced have", len(new_signals))
# for s in new_signals:
# print(s)
return new_signals
return signals

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