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',
