feat: NavGPT w/ our distance module(expr2)
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@ -57,7 +57,7 @@ NOW_LOCATION = None
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FOUND_BBOX = ""
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LAST_VP = ""
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THRESHOLD = 0.278
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THRESHOLD = 0.75
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SCAN = ""
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MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = (
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@ -70,9 +70,14 @@ FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
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"Parsing LLM output produced both a final answer and a parse-able action:"
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)
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print("Load GroundingDINO confidence file...")
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confidences = load_json('../datasets/REVERIE/annotations/grounding_dino_confidence.json')
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print("Loaded")
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'''
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print("Load CLIP confidence file...")
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confidences = load_json('../datasets/REVERIE/annotations/confidence.json')
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print("Loaded")
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'''
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print()
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print("Load distance file...")
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@ -128,8 +133,8 @@ class NavGPTOutputParser(AgentOutputParser):
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found = False
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max_bbox, max_bbox_confidence = "", 0
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for bbox in confidences[SCAN][tool_input]:
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confidence = confidences[SCAN][tool_input][bbox][CLIP_TARGET]
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for bbox in confidences[SCAN][tool_input][CLIP_TARGET]:
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confidence = bbox['score']
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if confidence >= THRESHOLD and confidence >= max_bbox_confidence:
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max_bbox = bbox
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max_bbox_confidence = confidence
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@ -401,7 +406,8 @@ class NavGPTAgent(BaseAgent):
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rel_viewpoint_heading = viewpoint_heading - heading_angle
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rel_viewpoint_heading = normalize_angle(rel_viewpoint_heading)
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rel_viewpoint_heading = angle_to_left_right(rel_viewpoint_heading)
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vp_description = rel_viewpoint_heading + f', {viewpoint_data["distance"]:.2f}m'
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# vp_description = rel_viewpoint_heading + f', {viewpoint_data["distance"]:.2f}m'
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vp_description = rel_viewpoint_heading
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vp_description = vp_description + f', {viewpoint_data["wall_distance"]:.2f}m to the wall'
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# rel_range_idx = (vp_range_idx - range_idx) % 8
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candidate_range.setdefault(vp_range_idx, {}).update({viewpoint_id: vp_description})
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