def get(self, model_id, prediction_id): req_format = request.args.get('format', 'geojson') req_inferences = request.args.get('inferences', 'all') req_threshold = request.args.get('threshold', '0') req_threshold = float(req_threshold) stream = PredictionService.export(prediction_id) inferences = PredictionService.inferences(prediction_id) pred = PredictionService.get_prediction_by_id(prediction_id) first = False if req_inferences != 'all': inferences = [ req_inferences ] def generate_npz(): nonlocal req_threshold labels_dict ={} for row in stream: if req_inferences != 'all' and row[3].get(req_inferences) is None: continue if req_inferences != 'all' and row[3].get(req_inferences) <= req_threshold: continue if row[4]: i_lst = pred.inf_list.split(",") #convert raw predictions into 0 or 1 based on threshold raw_pred = [] for num, inference in enumerate(i_lst): raw_pred.append(row[3][inference]) if req_inferences == 'all': req_threshold = request.args.get('threshold', '0.5') req_threshold = float(req_threshold) l = [1 if score >= req_threshold else 0 for score in raw_pred] #convert quadkey to x-y-z t = '-'.join([str(i) for i in mercantile.quadkey_to_tile(row[1])]) # special case for binary if (pred.inf_binary) and (len(i_lst) != 2): return err(400, "binary models must have two catagories"), 400 if (len(i_lst) == 2) and (pred.inf_binary): if list(row[4].values())[0]: #validated and true, keep original labels_dict.update({t:l}) else: if l == [1, 0]: l = [0, 1] else: l = [1, 0] labels_dict.update({t:l}) else: # for multi-label for key in list(row[4].keys()): i = i_lst.index(key) if not row[4][key]: if l[i] == 0: l[i] = 1 else: l[i] = 0 labels_dict.update({t:l}) if not labels_dict: raise NoValid bytestream = io.BytesIO() np.savez(bytestream, **labels_dict) return bytestream.getvalue() def generate(): nonlocal first if req_format == "geojson": yield '{ "type": "FeatureCollection", "features": [' elif req_format == "csv": output = io.StringIO() rowdata = ["ID", "QuadKey", "QuadKeyGeom"] rowdata.extend(inferences) csv.writer(output, quoting=csv.QUOTE_NONNUMERIC).writerow(rowdata) yield output.getvalue() for row in stream: if req_inferences != 'all' and row[3].get(req_inferences) is None: continue if req_inferences != 'all' and row[3].get(req_inferences) <= req_threshold: continue if req_format == "geojson" or req_format == "geojsonld": properties_dict = {} if row[4]: properties_dict = row[3] valid_dict = {} valid_dict.update({'validity': row[4]}) properties_dict.update(valid_dict) else: properties_dict = row[3] feat = { "id": row[0], "quadkey": row[1], "type": "Feature", "properties": properties_dict, "geometry": json.loads(row[2]) } if req_format == "geojsonld": yield json.dumps(feat) + '\n' elif req_format == "geojson": if first == False: first = True yield '\n' + json.dumps(feat) else: yield ',\n' + json.dumps(feat) elif req_format == "csv": output = io.StringIO() rowdata = [ row[0], row[1], row[2]] for inf in inferences: rowdata.append(row[3].get(inf, 0.0)) csv.writer(output, quoting=csv.QUOTE_NONNUMERIC).writerow(rowdata) yield output.getvalue() else: return err(501, "not a valid export type, valid export types are: geojson, csv, and npz"), 501 if req_format == "geojson": yield ']}' if req_format == "csv": mime = "text/csv" elif req_format == "geojson": mime = "application/geo+json" elif req_format == "geojsonld": mime = "application/geo+json-seq" elif req_format == "npz": mime = "application/npz" if req_format == "npz": try: npz = generate_npz() return Response( response = generate_npz(), mimetype = mime, status = 200, headers = { "Content-Disposition": 'attachment; filename="export.' + req_format + '"' } ) except NoValid: return err(400, "Can only return npz if predictions are validated. Currently there are no valid predictions"), 400 else: return Response( generate(), mimetype = mime, status = 200, headers = { "Content-Disposition": 'attachment; filename="export.' + req_format + '"' } )
def payload(integration_id: int, payload: dict): integration = IntegrationService.get_secrets(integration_id) if integration is None: raise IntegrationNotFound("Integration Not Found") if integration.integration != "maproulette": raise Exception("Only MapRoulette Integrations supported") for ele in [ "prediction", "project", "project_desc", "challenge", "challenge_instr", "threshold", "inferences", ]: if payload.get(ele) is None: raise Exception("Missing " + ele + " key in body") auth = integration.auth if payload.get("auth") is not None: auth = payload.get("auth") parsed = urlparse(integration.url) config = maproulette.Configuration(api_key=auth, hostname=parsed.netloc, protocol=parsed.scheme) project_api = maproulette.Project(config) challenge_api = maproulette.Challenge(config) try: project = project_api.get_project_by_name( project_name=payload.get("project")) except Exception: project = project_api.create_project( data={ "name": payload.get("project"), "display_name": payload.get("project"), "description": payload.get("project_desc"), "enabled": True, }) try: challenge = challenge_api.create_challenge( data={ "name": payload.get("challenge"), "parent": project["data"]["id"], "instruction": payload.get("challenge_instr"), }) except Exception as e: raise e req_inferences = payload.get("inferences", "all") req_threshold = float(payload.get("threshold", "0")) stream = PredictionService.export(int(payload.get("prediction"))) feats = {"type": "FeatureCollection", "features": []} for row in stream: if req_inferences != "all" and row[3].get(req_inferences) is None: continue if req_inferences != "all" and row[3].get( req_inferences) <= req_threshold: continue properties_dict = row[3] if row[4]: valid_dict = {} valid_dict.update({"validity": row[4]}) properties_dict.update(valid_dict) properties_dict["mle:id"] = row[0] feat = { "quadkey": row[1], "type": "Feature", "properties": properties_dict, "geometry": json.loads(row[2]), } feats["features"].append(feat) challenge_api.add_tasks_to_challenge( challenge_id=challenge["data"]["id"], data=feats) return { "project": project["data"]["id"], "challenge": challenge["data"]["id"] }
def payload(integration_id: int, payload: dict): integration = IntegrationService.get_secrets(integration_id) if integration is None: raise IntegrationNotFound('Integration Not Found') if integration.integration != "maproulette": raise Exception("Only MapRoulette Integrations supported"); for ele in ['prediction', 'project', 'project_desc', 'challenge', 'challenge_instr', 'threshold', 'inferences']: if payload.get(ele) is None: raise Exception('Missing ' + ele + ' key in body') auth = integration.auth if payload.get('auth') is not None: auth = payload.get('auth') parsed = urlparse(integration.url) config = maproulette.Configuration( api_key=auth, hostname=parsed.netloc, protocol=parsed.scheme ) project_api = maproulette.Project(config) challenge_api = maproulette.Challenge(config) try: project = project_api.get_project_by_name( project_name=payload.get('project') ) except: project = project_api.create_project( data={ "name": payload.get('project'), "display_name": payload.get('project'), "description": payload.get('project_desc'), "enabled": True } ) try: challenge = challenge_api.create_challenge( data={ 'name': payload.get('challenge'), 'parent': project['data']['id'], 'instruction': payload.get('challenge_instr') } ) except Exception as e: raise e req_inferences = payload.get('inferences', 'all') req_threshold = float(payload.get('threshold', '0')) stream = PredictionService.export(int(payload.get('prediction'))) inferences = PredictionService.inferences(int(payload.get('prediction'))) pred = PredictionService.get_prediction_by_id(int(payload.get('prediction'))) if req_inferences != 'all': inferences = [ req_inferences ] fc = { 'type': 'FeatureCollection', 'features': [] } for row in stream: if req_inferences != 'all' and row[3].get(req_inferences) is None: continue if req_inferences != 'all' and row[3].get(req_inferences) <= req_threshold: continue properties_dict = {} if row[4]: properties_dict = row[3] valid_dict = {} valid_dict.update({'validity': row[4]}) properties_dict.update(valid_dict) feat = { "id": row[0], "quadkey": row[1], "type": "Feature", "properties": properties_dict, "geometry": json.loads(row[2]) } fc['features'].append(feat) challenge_api.add_tasks_to_challenge( challenge_id=challenge['data']['id'], data=fc ) return { "project": project['data']['id'], "challenge": challenge['data']['id'] }
def get(self, project_id, prediction_id): """ Export Geospatial Predictions --- parameters: - name: project_id in: path schema: type: integer minimum: 0 description: The ID of the Project - name: prediction_id in: path schema: type: integer minimum: 0 description: The ID of the Project - name: format in: query schema: type: string default: geojson enum: - geojson - geojsonseq - csv description: The format to provide records in - name: inferences in: query schema: type: string default: all description: Return all inferences or only a single inference - name: threshold in: query schema: type: integer default: 0 minimum: 0 maximum: 1 description: The confidence threshold to apply to exported inferences responses: 200: description: Exported Data """ req_format = request.args.get("format", "geojson") req_inferences = request.args.get("inferences", "all") req_threshold = request.args.get("threshold", "0") req_threshold = float(req_threshold) stream = PredictionService.export(prediction_id) inferences = PredictionService.inferences(prediction_id) pred = PredictionService.get_prediction_by_id(prediction_id) hint = pred.hint z = pred.tile_zoom i_info = ImageryService.get(pred.imagery_id) c_list = ImageryService.get(pred.imagery_id) first = False if req_inferences != "all": inferences = [req_inferences] def generate_npz(): nonlocal req_threshold nonlocal hint nonlocal z nonlocal i_info nonlocal c_list # get chip list csv as dataframe to match up chip-lst name + geometry with geometry in the predictions database labels_dict = {} for row in stream: if req_inferences != "all" and row[3].get(req_inferences) is None: continue if ( req_inferences != "all" and row[3].get(req_inferences) <= req_threshold ): continue # set labels.npz key to be x-y-z tile either from quadkey or wkt geometry if i_info["fmt"] == "wms": if row[1]: t = "-".join( [str(i) for i in mercantile.quadkey_to_tile(row[1])] ) else: s = shape(json.loads(row[2])).centroid t = "-".join([str(i) for i in mercantile.tile(s.x, s.y, z)]) if i_info["fmt"] == "list": r = requests.get(c_list["url"]) df = pd.read_csv(io.StringIO(r.text)) df["c"] = df["bounds"].apply( lambda x: box(*[float(n) for n in x.split(",")]) ) gdf = gpd.GeoDataFrame(df, crs="EPSG:4326", geometry=df["c"]) # get tile name that where chip-list geom and geom in prediction row match gdf_2 = gpd.GeoDataFrame( {"geometry": [shape(json.loads(row[2]))]}, crs="EPSG:4326" ) # To-DO account for no overlap case i = gpd.overlay(gdf, gdf_2, how="intersection") tiles_intersection = i["name"].tolist() # convert raw predictions into 0 or 1 based on threshold raw_pred = [] i_lst = pred.inf_list.split(",") for num, inference in enumerate(i_lst): raw_pred.append(row[3][inference]) if req_inferences == "all": req_threshold = request.args.get("threshold", "0.5") req_threshold = float(req_threshold) binary_pred_list = [1 if score >= req_threshold else 0 for score in raw_pred] # special case for training and not predictions if hint == "training": if i_info["fmt"] == "list": for chip_name in tiles_intersection: labels_dict.update({chip_name: binary_pred_list}) else: labels_dict.update({t: binary_pred_list}) elif row[4]: t = "-".join([str(i) for i in mercantile.quadkey_to_tile(row[1])]) # special case for binary if pred.inf_binary and len(i_lst) != 2: return err(400, "binary models must have two catagories"), 400 if len(i_lst) == 2 and pred.inf_binary: if list(row[4].values())[ 0 ]: # validated and true, keep original labels_dict.update({t: binary_pred_list}) else: if binary_pred_list == [1, 0]: binary_pred_list = [0, 1] else: binary_pred_list = [1, 0] labels_dict.update({t: binary_pred_list}) else: # for multi-label for key in list(row[4].keys()): i = i_lst.index(key) if not row[4][key]: if binary_pred_list[i] == 0: binary_pred_list[i] = 1 else: binary_pred_list[i] = 0 labels_dict.update({t: binary_pred_list}) if not labels_dict: raise NoValid bytestream = io.BytesIO() np.savez(bytestream, **labels_dict) return bytestream.getvalue() def generate(): nonlocal first if req_format == "geojson": yield '{ "type": "FeatureCollection", "features": [' elif req_format == "csv": output = io.StringIO() rowdata = ["ID", "QuadKey", "QuadKeyGeom"] rowdata.extend(inferences) csv.writer(output, quoting=csv.QUOTE_NONNUMERIC).writerow(rowdata) yield output.getvalue() for row in stream: if req_inferences != "all" and row[3].get(req_inferences) is None: continue if ( req_inferences != "all" and row[3].get(req_inferences) <= req_threshold ): continue if req_format == "geojson" or req_format == "geojsonld": properties_dict = {} if row[4]: properties_dict = row[3] valid_dict = {} valid_dict.update({"validity": row[4]}) properties_dict.update(valid_dict) else: properties_dict = row[3] feat = { "id": row[0], "quadkey": row[1], "type": "Feature", "properties": properties_dict, "geometry": json.loads(row[2]), } if req_format == "geojsonld": yield json.dumps(feat) + "\n" elif req_format == "geojson": if first is False: first = True yield "\n" + json.dumps(feat) else: yield ",\n" + json.dumps(feat) elif req_format == "csv": output = io.StringIO() rowdata = [row[0], row[1], row[2]] for inf in inferences: rowdata.append(row[3].get(inf, 0.0)) csv.writer(output, quoting=csv.QUOTE_NONNUMERIC).writerow(rowdata) yield output.getvalue() else: return ( err( 501, "not a valid export type, valid export types are: geojson, csv, and npz", ), 501, ) if req_format == "geojson": yield "]}" if req_format == "csv": mime = "text/csv" elif req_format == "geojson": mime = "application/geo+json" elif req_format == "geojsonld": mime = "application/geo+json-seq" elif req_format == "npz": mime = "application/npz" if req_format == "npz": try: return Response( response=generate_npz(), mimetype=mime, status=200, headers={ "Content-Disposition": 'attachment; filename="export.' + req_format + '"' }, ) except NoValid: return ( err( 400, "Can only return npz if predictions are validated. Currently there are no valid predictions", ), 400, ) else: return Response( generate(), mimetype=mime, status=200, headers={ "Content-Disposition": 'attachment; filename="export.' + req_format + '"' }, )