\" + response+\"
\"\n\n\nadmin_site.register(Resource, ResourceAdmin)\n\nclass End_PointAdmin(OSMGeoAdmin):\n pass\n\nadmin_site.register(End_Point, End_PointAdmin)\n\nclass PublisherAdmin(OSMGeoAdmin):\n search_fields = ('name',)\n pass\nadmin_site.register(Publisher, PublisherAdmin)\n\nclass Community_InputAdmin(OSMGeoAdmin):\n list_display = [\"resource\",\"date\",\"name\", \"email\"]\n raw_id_fields = (\"resource\",)\nadmin_site.register(Community_Input, Community_InputAdmin)\n\nclass Georeference_RequestAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Georeference_Request, Georeference_RequestAdmin)\n\n\nclass OwnerAdmin(OSMGeoAdmin):\n # enable a full_name overwrite when available\n list_display=[\"name\",\"full_name\"]\n search_fields = (\"name\",\"full_name\")\n\nadmin_site.register(Owner, OwnerAdmin)\n\nclass TypeAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Type, TypeAdmin)\n\nclass Geometry_TypeAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Geometry_Type, Geometry_TypeAdmin)\n\nclass FormatAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Format, FormatAdmin)\n\n\nclass Category_KeywordsInline(admin.StackedInline):\n model = Category_Keywords.category.through\n extra = 0\n\nclass Category_KeywordsAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Category_Keywords, Category_KeywordsAdmin)\n\nclass CategoryAdmin(OSMGeoAdmin):\n inlines = [\n Category_KeywordsInline\n ]\nadmin_site.register(Category, CategoryAdmin)\n\n\nclass TagAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Tag, TagAdmin)\n\nclass PlaceAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Place, PlaceAdmin)\n\nclass Named_PlaceAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Named_Place, Named_PlaceAdmin)\n\nclass URL_TypeAdmin(OSMGeoAdmin):\n list_display = ('name', 'ref', 'service', '_class', '_method')\n\nadmin_site.register(URL_Type,URL_TypeAdmin)\n\nclass URLAdmin(OSMGeoAdmin):\n list_filter = (\"url_type\",)\n\nadmin_site.register(URL,URLAdmin)\n\n", "repo_name": "GeospatialCentroid/geoportal-manager", "sub_path": "resources/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 15060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "61", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.admin.AdminSite", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 39, "usage_type": "name"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 47, "usage_type": "name"}, {"api_name": "models.URL", "line_number": 48, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 90, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 90, "usage_type": "name"}, {"api_name": "models.Status_Log", "line_number": 91, "usage_type": "name"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 94, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 94, "usage_type": "name"}, {"api_name": "models.Change_Log", "line_number": 95, "usage_type": "name"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 107, "usage_type": "name"}, {"api_name": "models.Resource.parent", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.Resource", "line_number": 108, "usage_type": "name"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 116, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 116, "usage_type": "name"}, {"api_name": "models.Resource.parent", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Resource", "line_number": 117, "usage_type": "name"}, {"api_name": "django.contrib.admin.SimpleListFilter", "line_number": 125, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 125, "usage_type": "name"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 144, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 178, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 178, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 197, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 202, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 205, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 233, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 238, "usage_type": "call"}, {"api_name": "models.Resource.objects.filter", "line_number": 243, "usage_type": "call"}, {"api_name": "models.Resource.objects", "line_number": 243, "usage_type": "attribute"}, {"api_name": "models.Resource", "line_number": 243, "usage_type": "name"}, {"api_name": "models.Resource.objects.filter", "line_number": 249, "usage_type": "call"}, {"api_name": "models.Resource.objects", "line_number": 249, "usage_type": "attribute"}, {"api_name": "models.Resource", "line_number": 249, "usage_type": "name"}, {"api_name": "resources.ingester.DB_ToGBL.DB_ToGBL", "line_number": 252, "usage_type": "call"}, {"api_name": "resources.ingester.DB_ToGBL", "line_number": 252, "usage_type": "name"}, {"api_name": "resources.ingester.Publish_ToGBL.Publish_ToGBL", "line_number": 258, "usage_type": "call"}, {"api_name": "resources.ingester.Publish_ToGBL", "line_number": 258, "usage_type": "name"}, {"api_name": "django.utils.translation.ngettext", "line_number": 264, "usage_type": "call"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 268, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 268, "usage_type": "name"}, {"api_name": "resources.ingester.Delete_From_Solr.Delete_From_Solr", "line_number": 273, "usage_type": "call"}, {"api_name": "resources.ingester.Delete_From_Solr", "line_number": 273, "usage_type": "name"}, {"api_name": "django.utils.translation.ngettext", "line_number": 281, "usage_type": "call"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 285, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 285, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 295, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 295, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 302, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 302, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 309, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 311, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.WKTWriter", "line_number": 323, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.GEOSGeometry", "line_number": 325, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 340, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 352, "usage_type": "call"}, {"api_name": "pygments.formatters.HtmlFormatter", "line_number": 355, "usage_type": "call"}, {"api_name": "pygments.highlight", "line_number": 357, "usage_type": "call"}, {"api_name": "pygments.lexers.JsonLexer", "line_number": 357, "usage_type": "call"}, {"api_name": "models.Resource", "line_number": 363, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 365, "usage_type": "name"}, {"api_name": "models.End_Point", "line_number": 368, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 370, "usage_type": "name"}, {"api_name": "models.Publisher", "line_number": 373, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 375, "usage_type": "name"}, {"api_name": "models.Community_Input", "line_number": 378, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 380, "usage_type": "name"}, {"api_name": "models.Georeference_Request", "line_number": 382, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 385, "usage_type": "name"}, {"api_name": "models.Owner", "line_number": 390, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 392, "usage_type": "name"}, {"api_name": "models.Type", "line_number": 394, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 396, "usage_type": "name"}, {"api_name": "models.Geometry_Type", "line_number": 398, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 400, "usage_type": "name"}, {"api_name": "models.Format", "line_number": 402, "usage_type": "argument"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 405, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 405, "usage_type": "name"}, {"api_name": "models.Category_Keywords.category", "line_number": 406, "usage_type": "attribute"}, {"api_name": "models.Category_Keywords", "line_number": 406, "usage_type": "name"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 409, "usage_type": "name"}, {"api_name": "models.Category_Keywords", "line_number": 411, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 413, "usage_type": "name"}, {"api_name": "models.Category", "line_number": 417, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 420, "usage_type": "name"}, {"api_name": "models.Tag", "line_number": 422, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 424, "usage_type": "name"}, {"api_name": "models.Place", "line_number": 426, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 428, "usage_type": "name"}, {"api_name": "models.Named_Place", "line_number": 430, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 432, "usage_type": "name"}, {"api_name": "models.URL_Type", "line_number": 435, "usage_type": "argument"}, {"api_name": "django.contrib.gis.admin.OSMGeoAdmin", "line_number": 437, "usage_type": "name"}, {"api_name": "models.URL", "line_number": 440, "usage_type": "argument"}]}
+{"seq_id": "42268205687", "text": "import numpy as np\nimport json\nimport pdb\nimport networkx as nx\nimport time\nimport matplotlib\n\nimport networkx as nx\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n\nimport grandalf\nfrom grandalf.layouts import SugiyamaLayout\n\nfrom .utils import SOURCE_NODE\nfrom evaluation.cost_model import update_subplan_costs\n\nCROSS_JOIN_CARD = 19329323\n\ndef _find_all_tables(plan):\n '''\n '''\n # find all the scan nodes under the current level, and return those\n table_names = extract_values(plan, \"Relation Name\")\n alias_names = extract_values(plan, \"Alias\")\n table_names.sort()\n alias_names.sort()\n\n return table_names, alias_names\n\ndef explain_to_nx(explain):\n '''\n '''\n base_table_nodes = []\n join_nodes = []\n\n def _get_node_name(tables):\n name = \"\"\n if len(tables) > 1:\n name = str(deterministic_hash(str(tables)))[0:5]\n join_nodes.append(name)\n else:\n name = tables[0]\n if len(name) >= 6:\n # no aliases, shorten it\n name = \"\".join([n[0] for n in name.split(\"_\")])\n if name in base_table_nodes:\n name = name + \"2\"\n base_table_nodes.append(name)\n return name\n\n def _add_node_stats(node, plan):\n # add stats for the join\n G.nodes[node][\"Plan Rows\"] = plan[\"Plan Rows\"]\n if \"Actual Rows\" in plan:\n G.nodes[node][\"Actual Rows\"] = plan[\"Actual Rows\"]\n else:\n G.nodes[node][\"Actual Rows\"] = -1.0\n\n if \"Node Type\" in plan:\n G.nodes[node][\"Node Type\"] = plan[\"Node Type\"]\n total_cost = plan[\"Total Cost\"]\n G.nodes[node][\"Total Cost\"] = total_cost\n aliases = G.nodes[node][\"aliases\"]\n if len(G.nodes[node][\"tables\"]) > 1:\n children_cost = plan[\"Plans\"][0][\"Total Cost\"] \\\n + plan[\"Plans\"][1][\"Total Cost\"]\n\n # +1 to avoid cases which are very close\n # if not total_cost+1 >= children_cost:\n # print(\"aliases: {} children cost: {}, total cost: {}\".format(\\\n # aliases, children_cost, total_cost))\n # pdb.set_trace()\n G.nodes[node][\"cur_cost\"] = total_cost - children_cost\n G.nodes[node][\"node_label\"] = plan[\"Node Type\"][0]\n G.nodes[node][\"scan_type\"] = \"\"\n else:\n # FIXME: debug\n G.nodes[node][\"cur_cost\"] = total_cost\n G.nodes[node][\"node_label\"] = node\n # what type of scan was this?\n node_types = extract_values(plan, \"Node Type\")\n for i, full_n in enumerate(node_types):\n shortn = \"\"\n for n in full_n.split(\" \"):\n shortn += n[0]\n node_types[i] = shortn\n\n scan_type = \"\\n\".join(node_types)\n G.nodes[node][\"scan_type\"] = scan_type\n\n def traverse(obj):\n if isinstance(obj, dict):\n if \"Plans\" in obj:\n if len(obj[\"Plans\"]) == 2:\n # these are all the joins\n left_tables, left_aliases = _find_all_tables(obj[\"Plans\"][0])\n right_tables, right_aliases = _find_all_tables(obj[\"Plans\"][1])\n if len(left_tables) == 0 or len(right_tables) == 0:\n return\n all_tables = left_tables + right_tables\n all_aliases = left_aliases + right_aliases\n all_aliases.sort()\n all_tables.sort()\n\n if len(left_aliases) > 0:\n node0 = _get_node_name(left_aliases)\n node1 = _get_node_name(right_aliases)\n node_new = _get_node_name(all_aliases)\n else:\n node0 = _get_node_name(left_tables)\n node1 = _get_node_name(right_tables)\n node_new = _get_node_name(all_tables)\n\n # update graph\n G.add_edge(node_new, node0)\n G.add_edge(node_new, node1)\n G.edges[(node_new, node0)][\"join_direction\"] = \"left\"\n G.edges[(node_new, node1)][\"join_direction\"] = \"right\"\n\n # add other parameters on the nodes\n G.nodes[node0][\"tables\"] = left_tables\n G.nodes[node1][\"tables\"] = right_tables\n G.nodes[node0][\"aliases\"] = left_aliases\n G.nodes[node1][\"aliases\"] = right_aliases\n G.nodes[node_new][\"tables\"] = all_tables\n G.nodes[node_new][\"aliases\"] = all_aliases\n\n # TODO: if either the left, or right were a scan, then add\n # scan stats\n _add_node_stats(node_new, obj)\n\n if len(left_tables) == 1:\n _add_node_stats(node0, obj[\"Plans\"][0])\n if len(right_tables) == 1:\n _add_node_stats(node1, obj[\"Plans\"][1])\n\n for k, v in obj.items():\n if isinstance(v, (dict, list)):\n traverse(v)\n\n elif isinstance(obj, list) or isinstance(obj,tuple):\n for item in obj:\n traverse(item)\n\n G = nx.DiGraph()\n traverse(explain)\n G.base_table_nodes = base_table_nodes\n G.join_nodes = join_nodes\n return G\n\nNODE_COLORS = {}\n# NODE_COLORS[\"Hash Join\"] = 'b'\n# NODE_COLORS[\"Merge Join\"] = 'r'\n# NODE_COLORS[\"Nested Loop\"] = 'c'\n\nNODE_COLORS[\"Index Scan\"] = 'k'\nNODE_COLORS[\"Seq Scan\"] = 'k'\nNODE_COLORS[\"Bitmap Heap Scan\"] = 'k'\n\nNODE_COLORS[\"Hash\"] = 'k'\nNODE_COLORS[\"Materialize\"] = 'k'\nNODE_COLORS[\"Sort\"] = 'k'\n\n# for signifying whether the join was a left join or right join\nEDGE_COLORS = {}\nEDGE_COLORS[\"left\"] = \"k\"\nEDGE_COLORS[\"right\"] = \"k\"\n\ndef _plot_join_order_graph(G, base_table_nodes, join_nodes, pdf, title,\n fn):\n\n def format_ints(num):\n # returns the number formatted to closest 1000 + K\n return str(round(num, -3)).replace(\"000\",\"\") + \"K\"\n\n def _plot_labels(xdiff, ydiff, key, font_color, font_size):\n labels = {}\n label_pos = {}\n for k, v in pos.items():\n label_pos[k] = (v[0]+xdiff, v[1]+ydiff)\n if key in G.nodes[k]:\n if is_float(G.nodes[k][key]):\n labels[k] = format_ints(G.nodes[k][key])\n else:\n labels[k] = G.nodes[k][key]\n else:\n est_labels[k] = -1\n\n nx.draw_networkx_labels(G, label_pos, labels,\n font_size=font_size, font_color=font_color, ax=ax)\n\n fig,ax = plt.subplots(figsize=(8,7))\n NODE_SIZE = 600\n\n # FUCK fucking graphviz\n # pos = graphviz_layout(G, prog='dot')\n # pos = graphviz_layout(G, prog='dot',\n # args='-Gnodesep=0.05')\n\n # graphviz is better, but its is a bitch to install, so grandalf is also ok otherwise\n # G = G.reverse(copy=True)\n\n g = grandalf.utils.convert_nextworkx_graph_to_grandalf(G) # undocumented function\n class defaultview(object):\n w, h = 10, 10\n for v in g.V(): v.view = defaultview()\n sug = SugiyamaLayout(g.C[0])\n sug.init_all() # roots=[V[0]])\n # sug.init_all(roots=[g.V[0]],inverted_edges=[g.V[4].e_to(g.V[0])])\n # This is a bit of a misnomer, as grandalf doesn't actually come with any\n # visualization methods. This method instead calculates positions\n sug.draw() # Extracts the positions\n pos = {v.data: (v.view.xy[0], v.view.xy[1]) for v in g.C[0].sV}\n\n # ugly hacks; want to draw the graph upside down than what grandalf gives\n # us (graphviz actually gave the correct layout...)\n ys = []\n levels = {}\n leveltoy = {}\n newlevels = {}\n for k,v in pos.items():\n ys.append(v[1])\n ys.sort()\n ys = np.unique(ys)\n level = 0\n for y in ys:\n levels[y] = level\n leveltoy[level] = y\n newlevels[y] = len(ys)-1-level\n level += 1\n pos2 = {}\n for k,v in pos.items():\n lv = newlevels[v[1]]\n newy = leveltoy[lv]\n pos2[k] = (v[0], newy)\n\n pos = pos2\n\n plt.title(title)\n color_intensity = [G.nodes[n][\"cur_cost\"] for n in G.nodes()]\n vmin = min(color_intensity)\n vmax = max(color_intensity)\n # cmap = 'viridis_r'\n cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n\n nx.draw_networkx_nodes(G, pos,\n node_size=NODE_SIZE,\n node_color = color_intensity,\n cmap = cmap,\n alpha=0.2,\n vmin=vmin, vmax=vmax,\n ax=ax)\n\n sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin = vmin,\n vmax=vmax))\n\n sm._A = []\n plt.colorbar(sm, alpha=0.2, fraction=0.1, pad=0.0,\n label=\"PostgreSQL Estimated Cost\")\n\n _plot_labels(0, -10, \"est_card\", \"b\", 8)\n _plot_labels(0, +10, \"true_card\", \"darkorange\", 8)\n _plot_labels(0, 0, \"node_label\", \"k\", 14)\n\n patch1 = mpatches.Patch(color='b', label='Estimated Cardinality')\n patch2 = mpatches.Patch(color='darkorange', label='True Cardinality')\n plt.legend(handles=[patch1,patch2])\n\n # TODO: shape of node based on scan types\n # _plot_labels(+25, +5, \"scan_type\", \"b\", 10)\n\n x_values, y_values = zip(*pos.values())\n x_max = max(x_values)\n x_min = min(x_values)\n x_margin = (x_max - x_min) * 0.10\n plt.xlim(x_min - x_margin, x_max + x_margin)\n\n edge_colors = []\n for edge in G.edges():\n edge_colors.append(EDGE_COLORS[G.edges[edge][\"join_direction\"]])\n\n nx.draw_networkx_edges(G, pos, width=1.0,\n alpha=1.0, arrows=False,\n edge_color=edge_colors, ax=ax)\n plt.tight_layout()\n\n if pdf is not None:\n pdf.savefig()\n elif fn is not None:\n plt.savefig(fn)\n else:\n plt.show()\n\n plt.close()\n\ndef plot_explain_join_order(explain, true_cardinalities,\n est_cardinalities, pdf, title, fn=None):\n '''\n @true_cardinalities: dict for this particular explain\n '''\n G = explain_to_nx(explain)\n for node in G.nodes():\n aliases = G.nodes[node][\"aliases\"]\n aliases.sort()\n card_key = \" \".join(aliases)\n if true_cardinalities is None:\n G.nodes[node][\"est_card\"] = G.nodes[node][\"Plan Rows\"]\n G.nodes[node][\"true_card\"] = G.nodes[node][\"Actual Rows\"]\n elif card_key in true_cardinalities:\n G.nodes[node][\"est_card\"] = est_cardinalities[card_key]\n G.nodes[node][\"true_card\"] = true_cardinalities[card_key]\n elif tuple(aliases) in true_cardinalities:\n G.nodes[node][\"est_card\"] = est_cardinalities[tuple(aliases)]\n G.nodes[node][\"true_card\"] = true_cardinalities[tuple(aliases)]\n else:\n # unknown, might be a cross-join?\n G.nodes[node][\"est_card\"] = CROSS_JOIN_CARD\n G.nodes[node][\"true_card\"] = CROSS_JOIN_CARD\n # pdb.set_trace()\n\n _plot_join_order_graph(G, G.base_table_nodes, G.join_nodes, pdf, title, fn)\n return G\n\ndef draw_plan_graph(subsetg, y, cost_model, ax=None,\n source_node=SOURCE_NODE, final_node=None, font_size=40,\n cbar_fontsize=24, cax=None, fig=None, width=None,\n edge_color=None,\n bold_opt_path=True, bold_path=None):\n\n for n in subsetg.nodes():\n joined = \" \\Join \".join(n)\n joined = \"$\" + joined + \"$\"\n subsetg.nodes()[n][\"label\"] = joined\n\n if y is not None and cost_model is not None:\n cost_key = \"tmp_cost\"\n subsetg = subsetg.reverse()\n tcost = update_subplan_costs(subsetg, cost_model,\n cost_key=cost_key, ests=y)\n\n # TODO: need to add the flow-loss computing module\n # flows, edges = get_flows(subsetg, cost_model+cost_key)\n # Flow-Loss specific widths\n # MIN: 2...6\n # MIN_WIDTH = 1.0\n # MAX_WIDTH = 30.0\n # NEW_RANGE = MAX_WIDTH - MIN_WIDTH\n # OLD_RANGE = max(flows) - min(flows)\n\n # edge_widths = {}\n # for i, x in enumerate(flows):\n # normx = (((x - min(flows))*NEW_RANGE) / OLD_RANGE) + MIN_WIDTH\n # edge_widths[edges[i]] = normx\n # widths = []\n # for edge in subsetg.edges():\n # key = tuple([edge[1], edge[0]])\n # widths.append(edge_widths[key])\n\n # reverse back\n subsetg = subsetg.reverse()\n widths = []\n for edge in subsetg.edges():\n key = tuple([edge[1], edge[0]])\n widths.append(1.0)\n\n edge_colors = []\n for edge in subsetg.edges(data=True):\n edge_colors.append(edge[2][cost_model+cost_key])\n\n vmin = min(edge_colors)\n vmax = max(edge_colors)\n\n # assert len(edge_colors) == len(flows)\n opt_labels_list = nx.shortest_path(subsetg, source_node,\n final_node, weight=cost_model+cost_key)\n opt_labels = {}\n for n in subsetg.nodes(data=True):\n if n[0] in opt_labels_list:\n opt_labels[n[0]] = n[1][\"label\"]\n\n cm = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"green\", \"yellow\", \"red\"])\n\n else:\n widths = []\n for edge in subsetg.edges():\n key = tuple([edge[1], edge[0]])\n widths.append(2.0)\n cm = None\n\n pos = nx.nx_pydot.pydot_layout(subsetg, prog=\"dot\")\n\n if ax is None:\n fig, ax = plt.subplots(1,1,figsize=(30,20))\n\n labels = nx.get_node_attributes(subsetg, 'label')\n\n nx.draw_networkx_labels(subsetg, pos=pos,\n labels=labels,\n ax=ax, font_size=font_size,\n bbox=dict(facecolor=\"w\", edgecolor='k', boxstyle='round,pad=0.1'))\n\n if bold_opt_path and cost_model is not None:\n nx.draw_networkx_labels(subsetg, pos=pos,\n labels=opt_labels, ax=ax,\n font_size=font_size,\n bbox=dict(facecolor=\"w\", edgecolor='k',\n lw=font_size/2, boxstyle='round,pad=0.5', fill=True))\n\n if bold_path and cost_model is not None:\n bold_labels = {}\n for n in subsetg.nodes(data=True):\n if n[0] in bold_path:\n bold_labels[n[0]] = n[1][\"label\"]\n nx.draw_networkx_labels(subsetg, pos=pos,\n labels=bold_labels, ax=ax,\n font_size=font_size,\n bbox=dict(facecolor=\"w\", edgecolor='k',\n lw=font_size/2, boxstyle='round,pad=0.5', fill=True))\n\n if edge_color is not None:\n edge_colors = edge_color\n\n edges = nx.draw_networkx_edges(subsetg, pos,\n edge_color=edge_colors,\n width=widths, ax = ax, edge_cmap=cm,\n arrows=True,\n arrowsize=font_size / 2,\n arrowstyle='simple',\n min_target_margin=5.0)\n\n if y is not None and cost_model is not None:\n plt.style.use(\"seaborn-white\")\n sm = plt.cm.ScalarMappable(cmap=cm,\n norm=plt.Normalize(vmin=vmin, vmax=vmax))\n sm.set_array([])\n if fig is None:\n cbar = plt.colorbar(sm, aspect=50,\n orientation=\"horizontal\", pad =\n 0.02)\n else:\n cbar = fig.colorbar(sm, ax=ax,\n pad = 0.02,\n aspect=50,\n orientation=\"horizontal\")\n\n cbar.ax.tick_params(labelsize=font_size)\n cbar.set_label(\"Cost\", fontsize=font_size)\n cbar.ax.xaxis.get_offset_text().set_fontsize(font_size)\n\n plt.tight_layout()\n", "repo_name": "learnedsystems/CEB", "sub_path": "query_representation/viz.py", "file_name": "viz.py", "file_ext": "py", "file_size_in_byte": 15652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "61", "api": [{"api_name": "networkx.DiGraph", "line_number": 148, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "grandalf.utils.convert_nextworkx_graph_to_grandalf", "line_number": 206, "usage_type": "call"}, {"api_name": "grandalf.utils", "line_number": 206, "usage_type": "attribute"}, {"api_name": "grandalf.layouts.SugiyamaLayout", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.colors.LinearSegmentedColormap.from_list", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 247, "usage_type": "attribute"}, {"api_name": "networkx.draw_networkx_nodes", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 257, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "networkx.draw_networkx_edges", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "utils.SOURCE_NODE", "line_number": 328, "usage_type": "name"}, {"api_name": "evaluation.cost_model.update_subplan_costs", "line_number": 341, "usage_type": "call"}, {"api_name": "networkx.shortest_path", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.colors.LinearSegmentedColormap.from_list", "line_number": 384, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 384, "usage_type": "attribute"}, {"api_name": "networkx.nx_pydot.pydot_layout", "line_number": 393, "usage_type": "call"}, {"api_name": "networkx.nx_pydot", "line_number": 393, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 396, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 396, "usage_type": "name"}, {"api_name": "networkx.get_node_attributes", "line_number": 398, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 400, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 406, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 417, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_edges", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 435, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 436, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 436, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 436, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 453, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 453, "usage_type": "name"}]}
+{"seq_id": "36805208294", "text": "from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom challenges.models import CommunityChallenge\n\ndef challenges(request):\n num_challenge_posts = CommunityChallenge.objects.all().count()\n posts = CommunityChallenge.objects.order_by('-published_date')\n\n context = {\n 'num_challenge_posts': num_challenge_posts,\n 'challenges': posts,\n }\n\n\n return render(request, 'challenges.html', context=context)", "repo_name": "katiehrenchir/you-go-girl", "sub_path": "challenges/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 450, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "61", "api": [{"api_name": "challenges.models.CommunityChallenge.objects.all", "line_number": 6, "usage_type": "call"}, {"api_name": "challenges.models.CommunityChallenge.objects", "line_number": 6, "usage_type": "attribute"}, {"api_name": "challenges.models.CommunityChallenge", "line_number": 6, "usage_type": "name"}, {"api_name": "challenges.models.CommunityChallenge.objects.order_by", "line_number": 7, "usage_type": "call"}, {"api_name": "challenges.models.CommunityChallenge.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "challenges.models.CommunityChallenge", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}]}
+{"seq_id": "34093717952", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"These functions calculate the similarity of two images of the same size.\"\"\"\n\n\nimport cv2\nfrom .utils import img_mat_rgb_2_gray\n\n\ndef cal_ccoeff_confidence(im_source, im_search):\n \"\"\"求取两张图片的可信度，使用TM_CCOEFF_NORMED方法.\"\"\"\n # 扩展置信度计算区域\n im_search = cv2.copyMakeBorder(im_search, 10,10,10,10,cv2.BORDER_REPLICATE)\n \n im_source, im_search = img_mat_rgb_2_gray(im_source), img_mat_rgb_2_gray(im_search)\n res = cv2.matchTemplate(im_source, im_search, cv2.TM_CCOEFF_NORMED)\n min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n\n return max_val\n\n\ndef cal_rgb_confidence(img_src_rgb, img_sch_rgb):\n \"\"\"同大小彩图计算相似度.\"\"\"\n # 扩展置信度计算区域\n img_sch_rgb = cv2.copyMakeBorder(img_sch_rgb, 10,10,10,10,cv2.BORDER_REPLICATE)\n # 转HSV强化颜色的影响\n img_src_rgb = cv2.cvtColor(img_src_rgb, cv2.COLOR_BGR2HSV)\n img_sch_rgb = cv2.cvtColor(img_sch_rgb, cv2.COLOR_BGR2HSV)\n src_bgr, sch_bgr = cv2.split(img_src_rgb), cv2.split(img_sch_rgb)\n\n # 计算BGR三通道的confidence，存入bgr_confidence:\n bgr_confidence = [0, 0, 0]\n # 加入取值范围干扰，防止算法过于放大微小差异\n src_bgr[0][0,0] = sch_bgr[0][0,0] = 0\n src_bgr[0][0,1] = sch_bgr[0][0,1] = 255\n for i in range(3):\n res_temp = cv2.matchTemplate(src_bgr[i], sch_bgr[i], cv2.TM_CCOEFF_NORMED)\n min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res_temp)\n bgr_confidence[i] = max_val\n\n return min(bgr_confidence)\n", "repo_name": "manito-666/air_uitest", "sub_path": "lib/python3.9/site-packages/airtest/aircv/cal_confidence.py", "file_name": "cal_confidence.py", "file_ext": "py", "file_size_in_byte": 1595, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "61", "api": [{"api_name": "cv2.copyMakeBorder", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.BORDER_REPLICATE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "utils.img_mat_rgb_2_gray", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.minMaxLoc", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.copyMakeBorder", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.BORDER_REPLICATE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.minMaxLoc", "line_number": 39, "usage_type": "call"}]}
+{"seq_id": "36448126999", "text": "import asyncio\nimport logging\nimport time\nimport six\nimport json\nimport os\nimport mpyq\nimport async_timeout\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\n\nfrom .client import Client\nfrom .data import CreateGameError, Result\nfrom .game_state import GameState\nfrom .player import Bot, Human\nfrom .portconfig import Portconfig\nfrom .protocol import ConnectionAlreadyClosed, ProtocolError\nfrom .sc2process import SC2Process\n\nlogger = logging.getLogger(__name__)\n\n\nclass SlidingTimeWindow:\n def __init__(self, size: int):\n assert size > 0\n\n self.window_size = size\n self.window = []\n\n def push(self, value: float):\n self.window = (self.window + [value])[-self.window_size :]\n\n def clear(self):\n self.window = []\n\n @property\n def sum(self) -> float:\n return sum(self.window)\n\n @property\n def available(self) -> float:\n return sum(self.window[1:])\n\n @property\n def available_fmt(self) -> float:\n return \",\".join(f\"{w:.2f}\" for w in self.window[1:])\n\n\nasync def _play_game_human(client, player_id, realtime, game_time_limit):\n while True:\n state = await client.observation()\n if client._game_result:\n return client._game_result[player_id]\n\n if game_time_limit and (state.observation.observation.game_loop * 0.725 * (1 / 16)) > game_time_limit:\n print(state.observation.game_loop, state.observation.game_loop * 0.14)\n return Result.Tie\n\n if not realtime:\n await client.step()\n\n\nasync def _play_game_ai(client, player_id, ai, realtime, step_time_limit, game_time_limit):\n if realtime:\n assert step_time_limit is None\n\n # step_time_limit works like this:\n # * If None, then step time is not limited\n # * If given integer or float, the bot will simpy resign if any step takes longer than that\n # * Otherwise step_time_limit must be an object, with following settings:\n #\n # Key | Value | Description\n # ------------|------------|-------------\n # penalty | None | No penalty, the bot can continue on next step\n # penalty | N: int | Cooldown penalty, BotAI.on_step will not be called for N steps\n # penalty | \"resign\" | Bot resigns when going over time limit\n # time_limit | int/float | Time limit for a single step\n # window_size | N: int | The time limit will be used for last N steps, instad of 1\n #\n # Cooldown is a harsh penalty. The both loses the ability to act, but even worse,\n # the observation data from skipped steps is also lost. It's like falling asleep in\n # a middle of the game.\n time_penalty_cooldown = 0\n if step_time_limit is None:\n time_limit = None\n time_window = None\n time_penalty = None\n elif isinstance(step_time_limit, (int, float)):\n time_limit = float(step_time_limit)\n time_window = SlidingTimeWindow(1)\n time_penalty = \"resign\"\n else:\n assert isinstance(step_time_limit, dict)\n time_penalty = step_time_limit.get(\"penalty\", None)\n time_window = SlidingTimeWindow(int(step_time_limit.get(\"window_size\", 1)))\n time_limit = float(step_time_limit.get(\"time_limit\", None))\n\n ai._initialize_variables()\n\n game_data = await client.get_game_data()\n game_info = await client.get_game_info()\n\n # This game_data will become self._game_data in botAI\n ai._prepare_start(client, player_id, game_info, game_data, realtime=realtime)\n state = await client.observation()\n # check game result every time we get the observation\n if client._game_result:\n await ai.on_end(client._game_result[player_id])\n return client._game_result[player_id]\n gs = GameState(state.observation)\n proto_game_info = await client._execute(game_info=sc_pb.RequestGameInfo())\n ai._prepare_step(gs, proto_game_info)\n await ai.on_before_start()\n ai._prepare_first_step()\n try:\n await ai.on_start()\n except Exception as e:\n logger.exception(f\"AI on_start threw an error\")\n logger.error(f\"resigning due to previous error\")\n await ai.on_end(Result.Defeat)\n return Result.Defeat\n\n iteration = 0\n while True:\n if iteration != 0:\n if realtime:\n # On realtime=True, might get an error here: sc2.protocol.ProtocolError: ['Not in a game']\n try:\n requested_step = gs.game_loop + client.game_step\n state = await client.observation(requested_step)\n # If the bot took too long in the previous observation, request another observation one frame after\n if state.observation.observation.game_loop > requested_step:\n # TODO Remove these 2 comments\n # t = state.observation.observation.game_loop\n state = await client.observation(state.observation.observation.game_loop + 1)\n # print(f\"Requested step: {requested_step}, received: {t}, new: {state.observation.observation.game_loop}\")\n except ProtocolError:\n pass\n else:\n state = await client.observation()\n # check game result every time we get the observation\n if client._game_result:\n try:\n await ai.on_end(client._game_result[player_id])\n except TypeError as error:\n # print(f\"caught type error {error}\")\n # print(f\"return {client._game_result[player_id]}\")\n return client._game_result[player_id]\n return client._game_result[player_id]\n gs = GameState(state.observation)\n logger.debug(f\"Score: {gs.score.score}\")\n\n if game_time_limit and (gs.game_loop * 0.725 * (1 / 16)) > game_time_limit:\n await ai.on_end(Result.Tie)\n return Result.Tie\n proto_game_info = await client._execute(game_info=sc_pb.RequestGameInfo())\n ai._prepare_step(gs, proto_game_info)\n\n logger.debug(f\"Running AI step, it={iteration} {gs.game_loop * 0.725 * (1 / 16):.2f}s\")\n\n try:\n if realtime:\n # Issue event like unit created or unit destroyed\n await ai.issue_events()\n await ai.on_step(iteration)\n await ai._after_step()\n else:\n if time_penalty_cooldown > 0:\n time_penalty_cooldown -= 1\n logger.warning(f\"Running AI step: penalty cooldown: {time_penalty_cooldown}\")\n iteration -= 1 # Do not increment the iteration on this round\n elif time_limit is None:\n # Issue event like unit created or unit destroyed\n await ai.issue_events()\n await ai.on_step(iteration)\n await ai._after_step()\n else:\n out_of_budget = False\n budget = time_limit - time_window.available\n\n # Tell the bot how much time it has left attribute\n ai.time_budget_available = budget\n\n if budget < 0:\n logger.warning(f\"Running AI step: out of budget before step\")\n step_time = 0.0\n out_of_budget = True\n else:\n step_start = time.monotonic()\n try:\n async with async_timeout.timeout(budget):\n await ai.issue_events()\n await ai.on_step(iteration)\n except asyncio.TimeoutError:\n step_time = time.monotonic() - step_start\n logger.warning(\n f\"Running AI step: out of budget; \"\n + f\"budget={budget:.2f}, steptime={step_time:.2f}, \"\n + f\"window={time_window.available_fmt}\"\n )\n out_of_budget = True\n step_time = time.monotonic() - step_start\n\n time_window.push(step_time)\n\n if out_of_budget and time_penalty is not None:\n if time_penalty == \"resign\":\n raise RuntimeError(\"Out of time\")\n else:\n time_penalty_cooldown = int(time_penalty)\n time_window.clear()\n\n await ai._after_step()\n except Exception as e:\n if isinstance(e, ProtocolError) and e.is_game_over_error:\n if realtime:\n return None\n result = client._game_result[player_id]\n if result is None:\n logger.error(\"Game over, but no results gathered\")\n raise\n await ai.on_end(result)\n return result\n # NOTE: this message is caught by pytest suite\n logger.exception(f\"AI step threw an error\") # DO NOT EDIT!\n logger.error(f\"Error: {e}\")\n logger.error(f\"Resigning due to previous error\")\n try:\n await ai.on_end(Result.Defeat)\n except TypeError as error:\n # print(f\"caught type error {error}\")\n # print(f\"return {Result.Defeat}\")\n return Result.Defeat\n return Result.Defeat\n\n logger.debug(f\"Running AI step: done\")\n\n if not realtime:\n if not client.in_game: # Client left (resigned) the game\n await ai.on_end(client._game_result[player_id])\n return client._game_result[player_id]\n\n await client.step()\n\n iteration += 1\n\n\nasync def _play_game(\n player, client, realtime, portconfig, step_time_limit=None, game_time_limit=None, rgb_render_config=None\n):\n assert isinstance(realtime, bool), repr(realtime)\n\n player_id = await client.join_game(\n player.name, player.race, portconfig=portconfig, rgb_render_config=rgb_render_config\n )\n logging.info(f\"Player {player_id} - {player.name if player.name else str(player)}\")\n\n if isinstance(player, Human):\n result = await _play_game_human(client, player_id, realtime, game_time_limit)\n else:\n result = await _play_game_ai(client, player_id, player.ai, realtime, step_time_limit, game_time_limit)\n\n logging.info(f\"Result for player {player_id} - {player.name if player.name else str(player)}: {result._name_}\")\n\n return result\n\n\nasync def _play_replay(client, ai, realtime=False, player_id=0):\n ai._initialize_variables()\n\n game_data = await client.get_game_data()\n game_info = await client.get_game_info()\n client.game_step = 1\n # This game_data will become self._game_data in botAI\n ai._prepare_start(client, player_id, game_info, game_data, realtime=realtime)\n state = await client.observation()\n # Check game result every time we get the observation\n if client._game_result:\n await ai.on_end(client._game_result[player_id])\n return client._game_result[player_id]\n gs = GameState(state.observation)\n proto_game_info = await client._execute(game_info=sc_pb.RequestGameInfo())\n ai._prepare_step(gs, proto_game_info)\n ai._prepare_first_step()\n try:\n await ai.on_start()\n except Exception as e:\n logger.exception(f\"AI on_start threw an error\")\n logger.error(f\"resigning due to previous error\")\n await ai.on_end(Result.Defeat)\n return Result.Defeat\n\n iteration = 0\n while True:\n if iteration != 0:\n if realtime:\n # TODO: check what happens if a bot takes too long to respond, so that the requested\n # game_loop might already be in the past\n state = await client.observation(gs.game_loop + client.game_step)\n else:\n state = await client.observation()\n # check game result every time we get the observation\n if client._game_result:\n try:\n await ai.on_end(client._game_result[player_id])\n except TypeError as error:\n # print(f\"caught type error {error}\")\n # print(f\"return {client._game_result[player_id]}\")\n return client._game_result[player_id]\n return client._game_result[player_id]\n gs = GameState(state.observation)\n logger.debug(f\"Score: {gs.score.score}\")\n\n proto_game_info = await client._execute(game_info=sc_pb.RequestGameInfo())\n ai._prepare_step(gs, proto_game_info)\n\n logger.debug(f\"Running AI step, it={iteration} {gs.game_loop * 0.725 * (1 / 16):.2f}s\")\n\n try:\n if realtime:\n # Issue event like unit created or unit destroyed\n await ai.issue_events()\n await ai.on_step(iteration)\n await ai._after_step()\n else:\n\n # Issue event like unit created or unit destroyed\n await ai.issue_events()\n await ai.on_step(iteration)\n await ai._after_step()\n\n except Exception as e:\n if isinstance(e, ProtocolError) and e.is_game_over_error:\n if realtime:\n return None\n # result = client._game_result[player_id]\n # if result is None:\n # logger.error(\"Game over, but no results gathered\")\n # raise\n await ai.on_end(Result.Victory)\n return None\n # NOTE: this message is caught by pytest suite\n logger.exception(f\"AI step threw an error\") # DO NOT EDIT!\n logger.error(f\"Error: {e}\")\n logger.error(f\"Resigning due to previous error\")\n try:\n await ai.on_end(Result.Defeat)\n except TypeError as error:\n # print(f\"caught type error {error}\")\n # print(f\"return {Result.Defeat}\")\n return Result.Defeat\n return Result.Defeat\n\n logger.debug(f\"Running AI step: done\")\n\n if not realtime:\n if not client.in_game: # Client left (resigned) the game\n await ai.on_end(Result.Victory)\n return Result.Victory\n\n await client.step() # unindent one line to work in realtime\n\n iteration += 1\n\n\nasync def _setup_host_game(server, map_settings, players, realtime, random_seed=None, disable_fog=None):\n r = await server.create_game(map_settings, players, realtime, random_seed, disable_fog)\n if r.create_game.HasField(\"error\"):\n err = f\"Could not create game: {CreateGameError(r.create_game.error)}\"\n if r.create_game.HasField(\"error_details\"):\n err += f\": {r.create_game.error_details}\"\n logger.critical(err)\n raise RuntimeError(err)\n\n return Client(server._ws)\n\n\nasync def _host_game(\n map_settings,\n players,\n realtime,\n portconfig=None,\n save_replay_as=None,\n step_time_limit=None,\n game_time_limit=None,\n rgb_render_config=None,\n random_seed=None,\n sc2_version=None,\n disable_fog=None,\n):\n\n assert players, \"Can't create a game without players\"\n\n assert any(isinstance(p, (Human, Bot)) for p in players)\n\n async with SC2Process(\n fullscreen=players[0].fullscreen, render=rgb_render_config is not None, sc2_version=sc2_version\n ) as server:\n await server.ping()\n\n client = await _setup_host_game(server, map_settings, players, realtime, random_seed, disable_fog)\n # Bot can decide if it wants to launch with 'raw_affects_selection=True'\n if not isinstance(players[0], Human) and getattr(players[0].ai, \"raw_affects_selection\", None) is not None:\n client.raw_affects_selection = players[0].ai.raw_affects_selection\n\n try:\n result = await _play_game(\n players[0], client, realtime, portconfig, step_time_limit, game_time_limit, rgb_render_config\n )\n if save_replay_as is not None:\n await client.save_replay(save_replay_as)\n await client.leave()\n await client.quit()\n except ConnectionAlreadyClosed:\n logging.error(f\"Connection was closed before the game ended\")\n return None\n\n return result\n\n\nasync def _host_game_aiter(\n map_settings, players, realtime, portconfig=None, save_replay_as=None, step_time_limit=None, game_time_limit=None,\n):\n assert players, \"Can't create a game without players\"\n\n assert any(isinstance(p, (Human, Bot)) for p in players)\n\n async with SC2Process() as server:\n while True:\n await server.ping()\n\n client = await _setup_host_game(server, map_settings, players, realtime)\n if not isinstance(players[0], Human) and getattr(players[0].ai, \"raw_affects_selection\", None) is not None:\n client.raw_affects_selection = players[0].ai.raw_affects_selection\n\n try:\n result = await _play_game(players[0], client, realtime, portconfig, step_time_limit, game_time_limit)\n\n if save_replay_as is not None:\n await client.save_replay(save_replay_as)\n await client.leave()\n except ConnectionAlreadyClosed:\n logging.error(f\"Connection was closed before the game ended\")\n return\n\n new_players = yield result\n if new_players is not None:\n players = new_players\n\n\ndef _host_game_iter(*args, **kwargs):\n game = _host_game_aiter(*args, **kwargs)\n new_playerconfig = None\n while True:\n new_playerconfig = yield asyncio.get_event_loop().run_until_complete(game.asend(new_playerconfig))\n\n\nasync def _join_game(\n players, realtime, portconfig, save_replay_as=None, step_time_limit=None, game_time_limit=None,\n):\n async with SC2Process(fullscreen=players[1].fullscreen) as server:\n await server.ping()\n\n client = Client(server._ws)\n # Bot can decide if it wants to launch with 'raw_affects_selection=True'\n if not isinstance(players[1], Human) and getattr(players[1].ai, \"raw_affects_selection\", None) is not None:\n client.raw_affects_selection = players[1].ai.raw_affects_selection\n\n try:\n result = await _play_game(players[1], client, realtime, portconfig, step_time_limit, game_time_limit)\n if save_replay_as is not None:\n await client.save_replay(save_replay_as)\n await client.leave()\n await client.quit()\n except ConnectionAlreadyClosed:\n logging.error(f\"Connection was closed before the game ended\")\n return None\n\n return result\n\n\nasync def _setup_replay(server, replay_path, realtime, observed_id):\n await server.start_replay(replay_path, realtime, observed_id)\n return Client(server._ws)\n\n\nasync def _host_replay(replay_path, ai, realtime, portconfig, base_build, data_version, observed_id):\n async with SC2Process(fullscreen=False, base_build=base_build, data_hash=data_version) as server:\n response = await server.ping()\n\n client = await _setup_replay(server, replay_path, realtime, observed_id)\n result = await _play_replay(client, ai, realtime)\n return result\n\n\ndef get_replay_version(replay_path):\n with open(replay_path, \"rb\") as f:\n replay_data = f.read()\n replay_io = six.BytesIO()\n replay_io.write(replay_data)\n replay_io.seek(0)\n archive = mpyq.MPQArchive(replay_io).extract()\n metadata = json.loads(archive[b\"replay.gamemetadata.json\"].decode(\"utf-8\"))\n return metadata[\"BaseBuild\"], metadata[\"DataVersion\"]\n\n\ndef run_game(map_settings, players, **kwargs):\n if sum(isinstance(p, (Human, Bot)) for p in players) > 1:\n host_only_args = [\"save_replay_as\", \"rgb_render_config\", \"random_seed\", \"sc2_version\", \"disable_fog\"]\n join_kwargs = {k: v for k, v in kwargs.items() if k not in host_only_args}\n\n portconfig = Portconfig()\n result = asyncio.get_event_loop().run_until_complete(\n asyncio.gather(\n _host_game(map_settings, players, **kwargs, portconfig=portconfig),\n _join_game(players, **join_kwargs, portconfig=portconfig),\n )\n )\n else:\n result = asyncio.get_event_loop().run_until_complete(_host_game(map_settings, players, **kwargs))\n return result\n\n\ndef run_replay(ai, replay_path, realtime=False, observed_id=0):\n portconfig = Portconfig()\n assert os.path.isfile(replay_path), f\"Replay does not exist at the given path: {replay_path}\"\n assert os.path.isabs(\n replay_path\n ), f'Replay path has to be an absolute path, e.g. \"C:/replays/my_replay.SC2Replay\" but given path was \"{replay_path}\"'\n base_build, data_version = get_replay_version(replay_path)\n result = asyncio.get_event_loop().run_until_complete(\n _host_replay(replay_path, ai, realtime, portconfig, base_build, data_version, observed_id)\n )\n return result\n", "repo_name": "mitchkoko/firstbot", "sub_path": "python-sc2/sc2/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 21385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "61", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "client.observation", "line_number": 50, "usage_type": "call"}, {"api_name": "client._game_result", "line_number": 51, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 52, "usage_type": "attribute"}, {"api_name": "data.Result.Tie", "line_number": 56, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 56, "usage_type": "name"}, {"api_name": "client.step", "line_number": 59, "usage_type": "call"}, {"api_name": "client.get_game_data", "line_number": 99, "usage_type": "call"}, {"api_name": "client.get_game_info", "line_number": 100, "usage_type": "call"}, {"api_name": "client.observation", "line_number": 104, "usage_type": "call"}, {"api_name": "client._game_result", "line_number": 106, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 107, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 108, "usage_type": "attribute"}, {"api_name": "game_state.GameState", "line_number": 109, "usage_type": "call"}, {"api_name": "client._execute", "line_number": 110, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2.RequestGameInfo", "line_number": 110, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2", "line_number": 110, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 119, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 119, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 120, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 120, "usage_type": "name"}, {"api_name": "client.game_step", "line_number": 128, "usage_type": "attribute"}, {"api_name": "client.observation", "line_number": 129, "usage_type": "call"}, {"api_name": "client.observation", "line_number": 134, "usage_type": "call"}, {"api_name": "protocol.ProtocolError", "line_number": 136, "usage_type": "name"}, {"api_name": "client.observation", "line_number": 139, "usage_type": "call"}, {"api_name": "client._game_result", "line_number": 141, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 143, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 147, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 148, "usage_type": "attribute"}, {"api_name": "game_state.GameState", "line_number": 149, "usage_type": "call"}, {"api_name": "data.Result.Tie", "line_number": 153, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 153, "usage_type": "name"}, {"api_name": "data.Result.Tie", "line_number": 154, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 154, "usage_type": "name"}, {"api_name": "client._execute", "line_number": 155, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2.RequestGameInfo", "line_number": 155, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2", "line_number": 155, "usage_type": "name"}, {"api_name": "time.monotonic", "line_number": 188, "usage_type": "call"}, {"api_name": "async_timeout.timeout", "line_number": 190, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 193, "usage_type": "attribute"}, {"api_name": "time.monotonic", "line_number": 194, "usage_type": "call"}, {"api_name": "time.monotonic", "line_number": 201, "usage_type": "call"}, {"api_name": "protocol.ProtocolError", "line_number": 214, "usage_type": "argument"}, {"api_name": "client._game_result", "line_number": 217, "usage_type": "attribute"}, {"api_name": "data.Result.Defeat", "line_number": 228, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 228, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 232, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 232, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 233, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 233, "usage_type": "name"}, {"api_name": "client.in_game", "line_number": 238, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 239, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 240, "usage_type": "attribute"}, {"api_name": "client.step", "line_number": 242, "usage_type": "call"}, {"api_name": "client.join_game", "line_number": 252, "usage_type": "call"}, {"api_name": "player.name", "line_number": 253, "usage_type": "attribute"}, {"api_name": "player.race", "line_number": 253, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 255, "usage_type": "call"}, {"api_name": "player.name", "line_number": 255, "usage_type": "attribute"}, {"api_name": "player.Human", "line_number": 257, "usage_type": "argument"}, {"api_name": "player.ai", "line_number": 260, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 262, "usage_type": "call"}, {"api_name": "player.name", "line_number": 262, "usage_type": "attribute"}, {"api_name": "client.get_game_data", "line_number": 270, "usage_type": "call"}, {"api_name": "client.get_game_info", "line_number": 271, "usage_type": "call"}, {"api_name": "client.game_step", "line_number": 272, "usage_type": "attribute"}, {"api_name": "client.observation", "line_number": 275, "usage_type": "call"}, {"api_name": "client._game_result", "line_number": 277, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 278, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 279, "usage_type": "attribute"}, {"api_name": "game_state.GameState", "line_number": 280, "usage_type": "call"}, {"api_name": "client._execute", "line_number": 281, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2.RequestGameInfo", "line_number": 281, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2", "line_number": 281, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 289, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 289, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 290, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 290, "usage_type": "name"}, {"api_name": "client.observation", "line_number": 298, "usage_type": "call"}, {"api_name": "client.game_step", "line_number": 298, "usage_type": "attribute"}, {"api_name": "client.observation", "line_number": 300, "usage_type": "call"}, {"api_name": "client._game_result", "line_number": 302, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 304, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 308, "usage_type": "attribute"}, {"api_name": "client._game_result", "line_number": 309, "usage_type": "attribute"}, {"api_name": "game_state.GameState", "line_number": 310, "usage_type": "call"}, {"api_name": "client._execute", "line_number": 313, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2.RequestGameInfo", "line_number": 313, "usage_type": "call"}, {"api_name": "s2clientprotocol.sc2api_pb2", "line_number": 313, "usage_type": "name"}, {"api_name": "protocol.ProtocolError", "line_number": 332, "usage_type": "argument"}, {"api_name": "data.Result.Victory", "line_number": 339, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 339, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 346, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 346, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 350, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 350, "usage_type": "name"}, {"api_name": "data.Result.Defeat", "line_number": 351, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 351, "usage_type": "name"}, {"api_name": "client.in_game", "line_number": 356, "usage_type": "attribute"}, {"api_name": "data.Result.Victory", "line_number": 357, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 357, "usage_type": "name"}, {"api_name": "data.Result.Victory", "line_number": 358, "usage_type": "attribute"}, {"api_name": "data.Result", "line_number": 358, "usage_type": "name"}, {"api_name": "client.step", "line_number": 360, "usage_type": "call"}, {"api_name": "data.CreateGameError", "line_number": 368, "usage_type": "call"}, {"api_name": "client.Client", "line_number": 374, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 393, "usage_type": "name"}, {"api_name": "player.Bot", "line_number": 393, "usage_type": "name"}, {"api_name": "sc2process.SC2Process", "line_number": 395, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 402, "usage_type": "argument"}, {"api_name": "client.raw_affects_selection", "line_number": 403, "usage_type": "attribute"}, {"api_name": "client.save_replay", "line_number": 410, "usage_type": "call"}, {"api_name": "client.leave", "line_number": 411, "usage_type": "call"}, {"api_name": "client.quit", "line_number": 412, "usage_type": "call"}, {"api_name": "protocol.ConnectionAlreadyClosed", "line_number": 413, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 414, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 425, "usage_type": "name"}, {"api_name": "player.Bot", "line_number": 425, "usage_type": "name"}, {"api_name": "sc2process.SC2Process", "line_number": 427, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 432, "usage_type": "argument"}, {"api_name": "client.raw_affects_selection", "line_number": 433, "usage_type": "attribute"}, {"api_name": "client.save_replay", "line_number": 439, "usage_type": "call"}, {"api_name": "client.leave", "line_number": 440, "usage_type": "call"}, {"api_name": "protocol.ConnectionAlreadyClosed", "line_number": 441, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 442, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 454, "usage_type": "call"}, {"api_name": "sc2process.SC2Process", "line_number": 460, "usage_type": "call"}, {"api_name": "client.Client", "line_number": 463, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 465, "usage_type": "argument"}, {"api_name": "client.raw_affects_selection", "line_number": 466, "usage_type": "attribute"}, {"api_name": "client.save_replay", "line_number": 471, "usage_type": "call"}, {"api_name": "client.leave", "line_number": 472, "usage_type": "call"}, {"api_name": "client.quit", "line_number": 473, "usage_type": "call"}, {"api_name": "protocol.ConnectionAlreadyClosed", "line_number": 474, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 475, "usage_type": "call"}, {"api_name": "client.Client", "line_number": 483, "usage_type": "call"}, {"api_name": "sc2process.SC2Process", "line_number": 487, "usage_type": "call"}, {"api_name": "six.BytesIO", "line_number": 498, "usage_type": "call"}, {"api_name": "mpyq.MPQArchive", "line_number": 501, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 502, "usage_type": "call"}, {"api_name": "player.Human", "line_number": 507, "usage_type": "name"}, {"api_name": "player.Bot", "line_number": 507, "usage_type": "name"}, {"api_name": "portconfig.Portconfig", "line_number": 511, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 512, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 513, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 519, "usage_type": "call"}, {"api_name": "portconfig.Portconfig", "line_number": 524, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path", "line_number": 525, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path", "line_number": 526, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 530, "usage_type": "call"}]}
+{"seq_id": "14211099431", "text": "import cv2\r\nimport numpy as np\r\nfrom csv_managment import comparate_with_database\r\nimport socket\r\n\r\nadress = '0.0.0.0'\r\nport = 8081\r\n\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nsock.bind((adress, port))\r\nsock.listen(1)\r\n\r\nconnections = []\r\nwithAndroid = False\r\n\r\nfinger_position_list = [[], []]\r\n\r\ndef string(vec):\r\n result = \"\"\r\n for i in vec:\r\n result += str(i) + \"!\"\r\n\r\n return result\r\n\r\ndef send(message):\r\n for connection in connections:\r\n connection.send(bytes(message + \"\\n\", 'utf-8'))\r\n\r\nif (withAndroid):\r\n print(\"Waiting for connections\")\r\n while True:\r\n client, a = sock.accept()\r\n connections.append(client)\r\n break\r\n\r\n print(\"Connected\")\r\n print(connections)\r\n\r\ncap = cv2.VideoCapture(0)\r\n_, img3 = cap.read()\r\n\r\nx1, y1, x2, y2 = 0,0,0,0\r\n\r\ncounter = 0\r\nsalidaFinal = \"\"\r\nisSend = False\r\nmensaje = \"\"\r\nwhile (cap.isOpened()):\r\n _, frame = cap.read()\r\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\r\n \r\n # define range of white color in HSV\r\n # change it according to your need !\r\n #lower_white = np.array([0, 0, 215])\r\n #upper_white = np.array([180, 15, 255])\r\n #lower_white = np.array([0, 0, 230])\r\n #upper_white = np.array([180, 25, 255])\r\n \r\n # Threshold the HSV image to get only white colors\r\n #mask = cv2.inRange(hsv, lower_white, upper_white)\r\n # Bitwise-AND mask and original image\r\n #res = cv2.bitwise_and(frame,frame, mask= mask)\r\n #umbral = cv2.threshold(mask, 25, 255, cv2.THRESH_BINARY)[1]\r\n #umbral = cv2.dilate(umbral, None, iterations=2)\r\n \r\n #contornosimg = umbral.copy()\r\n # Buscamos contorno en la imagen\r\n #im, contornos, hierarchy = cv2.findContours(contornosimg,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\r\n \r\n \r\n \r\n \"\"\"\r\n for c in contornos:\r\n # Eliminamos los contornos más pequeños\r\n if (cv2.contourArea(c) < 4000):\r\n continue\r\n # Obtenemos el bounds del contorno, el rectángulo mayor que engloba al contorno\r\n elif(cv2.contourArea(c) > 40000):\r\n continue\r\n else:\r\n \r\n (xa, ya, wa, ha) = cv2.boundingRect(c)\r\n # Dibujamos el rectángulo del bounds\r\n if(xa>40 and ya>40 and wa+80