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Cleanup classification

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This commit is contained in:
Nicolas Mowen 2025-01-13 07:24:59 -07:00
parent f87e82481d
commit 61870184df
1 changed files with 40 additions and 4 deletions
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44
frigate/data_processing/real_time/bird_processor.py
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@ -5,9 +5,10 @@ import os
import cv2
import numpy as np
import requests
from frigate.config import FrigateConfig
from frigate.const import MODEL_CACHE_DIR
from frigate.const import FRIGATE_LOCALHOST, MODEL_CACHE_DIR
from frigate.util.object import calculate_region
from ..types import DataProcessorMetrics
@ -28,6 +29,7 @@ class BirdProcessor(RealTimeProcessorApi):
self.tensor_input_details: dict[str, any] = None
self.tensor_output_details: dict[str, any] = None
self.detected_birds: dict[str, float] = {}
self.labelmap: dict[int, str] = {}
download_path = os.path.join(MODEL_CACHE_DIR, "bird")
self.model_files = {
@ -73,6 +75,17 @@ class BirdProcessor(RealTimeProcessorApi):
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
i = 0
with open(os.path.join(MODEL_CACHE_DIR, "bird/birdmap.txt")) as f:
line = f.readline()
while line:
start = line.find("(")
end = line.find(")")
self.labelmap[i] = line[start + 1 : end]
i += 1
line = f.readline()
def process_frame(self, obj_data, frame):
if obj_data["label"] != "bird":
return
@ -84,7 +97,7 @@ class BirdProcessor(RealTimeProcessorApi):
obj_data["box"][2],
obj_data["box"][3],
224,
1.4,
1.0,
)
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
@ -99,11 +112,34 @@ class BirdProcessor(RealTimeProcessorApi):
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
self.interpreter.invoke()
res: np.ndarray = self.interpreter.get_tensor(self.tensor_output_details[0]["index"])[0]
res: np.ndarray = self.interpreter.get_tensor(
self.tensor_output_details[0]["index"]
)[0]
probs = res / res.sum(axis=0)
best_id = np.argmax(probs)
if best_id == 964:
logger.debug("No bird classification was detected.")
return
score = round(probs[best_id], 2)
logger.info(f"the best scoring index is {best_id} {score}%")
previous_score = self.detected_birds.get(obj_data["id"], 0.0)
if score <= previous_score:
logger.debug(f"Score {score} is worse than previous score {previous_score}")
return
resp = requests.post(
f"{FRIGATE_LOCALHOST}/api/events/{obj_data['id']}/sub_label",
json={
"camera": obj_data.get("camera"),
"subLabel": self.labelmap[best_id],
"subLabelScore": score,
},
)
if resp.status_code == 200:
self.detected_birds[obj_data["id"]] = score
def handle_request(self, request_data):
return None