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didier committed 2024-11-04 18:50:34 -05:00
1 parent 2c798c8ca5
commit 35c5ab9a32
5 files changed
+51 -8

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+10 -3
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@@ -107,9 +107,12 @@ class DodgeballTracker:
def run_model_on_frame(self, model, frame: VideoFrame):
model_outputs = model(frame)[0]
return model_outputs.boxes.data.cpu().numpy()
# return model_outputs.boxes.data.tolist()
foo = model_outputs.boxes.data
for data in model_outputs.boxes.data.tolist():
detected_obj = DetectedObject(*data)
if detected_obj.confidence > 0.5:
if detected_obj.score > 0.5:
yield detected_obj
def persist_points(self):
@@ -130,7 +133,7 @@ class DodgeballTracker:
return YOLO("yolo11n.pt", verbose=True)
def update_tracker(self, tracker, detected_objs: list[DetectedObject], frame: VideoFrame) -> None:
important_detected_objs = [do for do in detected_objs if do.confidence > 0.5]
important_detected_objs = [do for do in detected_objs if do.score > 0.5]
def main(self):
# define some constants
@@ -141,7 +144,11 @@ class DodgeballTracker:
for i, (frame, ts) in enumerate(iter_frames(video_cap)):
# run the YOLO model on the frame
detected_objs = list(self.run_model_on_frame(model, frame))
self.video_writer.plot_boxes_to_frame(detected_objs, frame)
data_ = tracker.update(
np.array(detected_objs), frame)
if i == 100:
print(data_)
# self.video_writer.plot_boxes_to_frame(detected_objs, frame)
# data_ = tracker.update(np.array([dobj.as_nparray() for dobj in detected_objs]), frame)
# if data_:
# print("dkld")
+3 -3
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@@ -4,7 +4,7 @@ import numpy as np
XCoord = NewType('XCoord', float)
YCoord = NewType('YCoord', float)
Confidence = NewType('Confidence', float)
Score = NewType('Confidence', float)
Category = NewType('Category', float)
@@ -23,14 +23,14 @@ class DetectedObject:
ymin: YCoord
xmax: XCoord
ymax: YCoord
confidence: Confidence
score: Score
category: Category
def as_int_box(self) -> tuple[tuple[int, int], tuple[int, int]]:
return (int(self.xmin), int(self.ymin)), (int(self.xmax), int(self.ymax))
def as_nparray(self):
return np.array([self.xmin, self.ymax, self.xmax, self.ymax, self.confidence, self.category])
return np.array([self.xmin, self.ymax, self.xmax, self.ymax, self.score, self.category])
+1 -1
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@@ -81,7 +81,7 @@ class MP4VVideoWriter(VideoWriter):
self._video_writer.write(frame)
def plot_box_to_frame(self, data: DetectedObject, frame: VideoFrame) -> None:
confidence = float(data.confidence)
confidence = float(data.score)
for threshold in self._thresholds:
if threshold.min_threshold < confidence:
# xmin, ymin, xmax, ymax = self._extract_box(data)