def __init__(self, frame, api_key, model_id): self.frame = frame self.api_key = api_key self.model_id = model_id self.authenticator = IAMAuthenticator(self.api_key) self.visual_recognition = VisualRecognitionV4( version='2019-02-11', authenticator=self.authenticator) self.visual_recognition.set_service_url( 'https://gateway.watsonplatform.net/visual-recognition/api/v4/analyze?' )
def get_ine(): apikey = 'i6Ze6gqvd1XrPj624Z-BGbejpQosiVKjnKaW-YVsM1cf' url = 'https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/a48f0673-5423-47cf-8295-d5b52a90921c' collection = '031fbbd1-cc79-4cf0-ac3c-e5ffd0c25cdc' authenticator = IAMAuthenticator(apikey) service = VisualRecognitionV4('2021-04-29', authenticator=authenticator) service.set_service_url(url) path = '././images/ine_ejemplo.jpg' with open(path, 'rb') as ine_img: analyze_images = service.analyze( collection_ids=[collection], features=[AnalyzeEnums.Features.OBJECTS.value], images_file=[FileWithMetadata(ine_img)]).get_result() print(analyze_images) response = json.dumps(analyze_images) return Response(response, mimetype='application/json')
from collections import Counter """ System parameters """ version_name = '2020-03-20' Watson_API_key = 'UNj_AQojzpzeg5q5FvmaMu2a3jathuqDB79_DjpAGJb_' # Watson Visual Recognition URL: VR_service_url = 'https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/de0d73f8-faaa-4318-9ed6-492a2822bf20' # Different ID for each Watson service: face_detection_CID = "0105d854-d33a-455a-989f-91ce7048982c" classifier_id = "FaceClassification_746624536" authenticator = IAMAuthenticator(Watson_API_key) service_v4 = VisualRecognitionV4(version_name, authenticator=authenticator) service_v4.set_service_url(VR_service_url) service_v3 = VisualRecognitionV3(version_name, authenticator=authenticator) def format_face_coords(ibm_analyze_result): """ Parse the face coords extracted from IBM service_v4. :param ibm_analyze_result: the json object directly returned from IBM face detection service_v4 see an example in "watson_experiment/sample_face_and_result/sample_output.json" :return: a list of location, each looks like { "left": 64, "top": 72, "width": 124,
import json import os from ibm_watson import VisualRecognitionV4 from ibm_watson.visual_recognition_v4 import FileWithMetadata, TrainingDataObject, Location, AnalyzeEnums from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator( 'YOUR APIKEY') service = VisualRecognitionV4( '2018-03-19', authenticator=authenticator) service.set_service_url('https://gateway.watsonplatform.net/visual-recognition/api') # create a classifier my_collection = service.create_collection( name='', description='testing for python' ).get_result() collection_id = my_collection.get('collection_id') # add images with open(os.path.join(os.path.dirname(__file__), '../resources/South_Africa_Luca_Galuzzi_2004.jpeg'), 'rb') as giraffe_info: add_images_result = service.add_images( collection_id, images_file=[FileWithMetadata(giraffe_info)], ).get_result() print(json.dumps(add_images_result, indent=2)) image_id = add_images_result.get('images')[0].get('image_id') # add image training data training_data = service.add_image_training_data(
import json from ibm_watson import VisualRecognitionV4 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator( '-L_ISbMRp4t_bFoGMrQIeTG2n9Qy3pdd1QmKQxGF3al2') visual_recognition = VisualRecognitionV4(version='2019-02-11', authenticator=authenticator) visual_recognition.set_service_url( 'https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/d9acfb96-cfe0-48b5-9859-e26d687fa469' ) result = visual_recognition.train( collection_id='d7169bfd-fa76-4d4f-b96a-05e56218631b').get_result() print(json.dumps(result, indent=2))
def camera(): authenticator = IAMAuthenticator( apikey="ZQBgX2TPs2CmC3wNLWGEr7X3xDmq42sDsqTZVCsjdke8") flag = False obj = VisualRecognitionV4(version='2021-03-25', authenticator=authenticator) obj.set_service_url( service_url= 'https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/2fa93f7c-36f0-48f9-abb1-15845fbe94e1' ) while True: success, img = cap.read() cv2.imwrite('4.jpg', img) with open('4.jpg', 'rb') as honda_file, open('4.jpg', 'rb') as dice_file: result = obj.analyze( collection_ids=[ "17951ae7-0169-4551-ac19-a3864c7eed65", '17951ae7-0169-4551-ac19-a3864c7eed65' ], features=[AnalyzeEnums.Features.OBJECTS.value], images_file=[ FileWithMetadata(honda_file), ], threshold=0.15).get_result() #print(json.dumps(result, indent=2)) img = cv2.imread('4.jpg') try: left = result['images'][0]['objects']['collections'][0]['objects'][ 0]['location']['left'] top = result['images'][0]['objects']['collections'][0]['objects'][ 0]['location']['top'] width = result['images'][0]['objects']['collections'][0][ 'objects'][0]['location']['width'] height = result['images'][0]['objects']['collections'][0][ 'objects'][0]['location']['height'] img = cv2.imread('4.jpg') cv2.rectangle(img, (left, top), (left + width, top + height), (255, 0, 0), 2) name = result['images'][0]['objects']['collections'][0]['objects'][ 0]['object'] with open('data.txt', 'r') as f: f.write(str(name)) cv2.putText(img, name, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2) cv2.imshow('gasm', img) k = cv2.waitKey(30) & 0xff if k == 27: # press 'ESC' to quit break cv2.show('gasm', img) if name == 'gun' and flag == False: flag = True SendMail() except: cv2.imshow('gasm', img) # pass # cv2.show('gasm',img) cap.release() # cv2.waitKey(0) cv2.destroyAllWindows()
# coding: utf-8 # In[8]: from ibm_watson import VisualRecognitionV4 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator import json from ibm_watson.visual_recognition_v4 import AnalyzeEnums, FileWithMetadata import details """Authentication step with IBM""" authenticator = IAMAuthenticator('{your_api_key}') visual_recognition = VisualRecognitionV4( version='2020/06/14', authenticator=authenticator ) visual_recognition.set_service_url('https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/ff7b2df2-cfd1-47a5-9bce-778b0661850f') """Using IBM Watson Studio to classify image and determine if there is injury""" """Threshold set to low since it's safer to assume""" with open('./frame.jpg', 'rb') as images_file: result = visual_recognition.analyze( collection_ids=["{collection_id}"], features=[AnalyzeEnums.Features.OBJECTS.value] images_file=[FileWithMetadata(images_file)], threshold='0.5',