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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c133eb2c",
"metadata": {},
"outputs": [],
"source": [
"name_to_id = {\n",
" \"NA\": 'NA',\n",
" \"Bullseye\": 10,\n",
" \"One\": 11,\n",
" \"Two\": 12,\n",
" \"Three\": 13,\n",
" \"Four\": 14,\n",
" \"Five\": 15,\n",
" \"Six\": 16,\n",
" \"Seven\": 17,\n",
" \"Eight\": 18,\n",
" \"Nine\": 19,\n",
" \"A\": 20,\n",
" \"B\": 21,\n",
" \"C\": 22,\n",
" \"D\": 23,\n",
" \"E\": 24,\n",
" \"F\": 25,\n",
" \"G\": 26,\n",
" \"H\": 27,\n",
" \"S\": 28,\n",
" \"T\": 29,\n",
" \"U\": 30,\n",
" \"V\": 31,\n",
" \"W\": 32,\n",
" \"X\": 33,\n",
" \"Y\": 34,\n",
" \"Z\": 35,\n",
" \"Up\": 36,\n",
" \"Down\": 37,\n",
" \"Right\": 38,\n",
" \"Left\": 39,\n",
" \"Up Arrow\": 36,\n",
" \"Down Arrow\": 37,\n",
" \"Right Arrow\": 38,\n",
" \"Left Arrow\": 39,\n",
" \"Stop\": 40\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a89ceef6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"优先近距离物体:\n",
"200\n",
"{'detection': {'annotated_image_path': 'results/annotated_image_Up-36_1757948296.jpg', 'bbox_coordinates': {'x1': 545.3320312499999, 'x2': 560.7070312499999, 'y1': 15.254882812499998, 'y2': 33.75292968749999}, 'confidence': 0.01422882080078125, 'label': 'Up', 'original_image_path': 'results\\\\3483d55f-887a-4364-8d0b-6910faa6a585_36.png'}, 'image_id': '36', 'obstacle_id': 'unknown'}\n"
]
}
],
"source": [
"# 选项1: 优先检测较近的物体（默认行为，面积较大的物体）\n",
"import requests\n",
"\n",
"SERVER_URL = \"http://localhost:5000\"\n",
"image_file = \"Screenshot 2025-09-15 225930.png\"\n",
"\n",
"with open(image_file, 'rb') as f:\n",
" files = {'file': f}\n",
" data = {'prefer_close_objects': 'true'} # 优先近距离物体\n",
" response = requests.post(f\"{SERVER_URL}/image\", files=files, data=data)\n",
"\n",
"print(\"优先近距离物体:\")\n",
"print(response.status_code)\n",
"print(response.json())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "21f15172",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"优先远距离物体:\n",
"200\n",
"{'detection': {'annotated_image_path': 'results/annotated_image_Up-36_1757948327.jpg', 'bbox_coordinates': {'x1': 545.3320312499999, 'x2': 560.7070312499999, 'y1': 15.254882812499998, 'y2': 33.75292968749999}, 'confidence': 0.01422882080078125, 'label': 'Up', 'original_image_path': 'results\\\\e7dcd5cf-db24-4821-9fe6-5e16412ba51c_36.png'}, 'image_id': '36', 'obstacle_id': 'unknown'}\n"
]
}
],
"source": [
"# 选项2: 优先检测较远的物体（面积较小的物体）\n",
"import requests\n",
"\n",
"SERVER_URL = \"http://localhost:5000\"\n",
"image_file = \"b.png\"\n",
"\n",
"with open(image_file, 'rb') as f:\n",
" files = {'file': f}\n",
" data = {'prefer_close_objects': 'false'} # 优先远距离物体\n",
" response = requests.post(f\"{SERVER_URL}/image\", files=files, data=data)\n",
"\n",
"print(\"优先远距离物体:\")\n",
"print(response.status_code)\n",
"print(response.json())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6b29a73a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"默认行为（优先近距离物体）:\n",
"200\n",
"{'detection': {'annotated_image_path': 'results/annotated_image_Up-36_1757948317.jpg', 'bbox_coordinates': {'x1': 545.3320312499999, 'x2': 560.7070312499999, 'y1': 15.254882812499998, 'y2': 33.75292968749999}, 'confidence': 0.01422882080078125, 'label': 'Up', 'original_image_path': 'results\\\\d0387124-696d-4233-90db-fe511ed62828_36.png'}, 'image_id': '36', 'obstacle_id': 'unknown'}\n"
]
}
],
"source": [
"# 选项3: 不指定参数（使用默认行为，等同于 prefer_close_objects=true）\n",
"import requests\n",
"\n",
"SERVER_URL = \"http://localhost:5000\"\n",
"image_file = \"b.png\"\n",
"\n",
"with open(image_file, 'rb') as f:\n",
" files = {'file': f}\n",
" # 不添加 data 参数，使用默认行为（优先近距离物体）\n",
" response = requests.post(f\"{SERVER_URL}/image\", files=files)\n",
"\n",
"print(\"默认行为（优先近距离物体）:\")\n",
"print(response.status_code)\n",
"print(response.json())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "chatbot",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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},
"nbformat": 4,
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}
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