How can we organize the extracted object information in a tabular format using the pandas data frame?

/ / Published in Artificial Intelligence, EITC/AI/GVAPI Google Vision API, Advanced images understanding, Objects detection, Examination review

To organize extracted object information in a tabular format using the pandas data frame in the context of Advanced Images Understanding and Object Detection with the Google Vision API, we can follow a step-by-step process.

Step 1: Importing the Required Libraries
First, we need to import the necessary libraries for our task. In this case, we will import the pandas library, which provides powerful data manipulation capabilities, and the google.cloud.vision library, which allows us to interact with the Google Vision API.

python
import pandas as pd
from google.cloud import vision

Step 2: Authenticating and Initializing the Google Vision API Client
Next, we need to authenticate and initialize the Google Vision API client. This requires setting up a Google Cloud project, enabling the Vision API, and obtaining the necessary credentials. Once we have the credentials, we can create a client object.

python
# Replace 'path/to/credentials.json' with the actual path to your credentials file
client = vision.ImageAnnotatorClient.from_service_account_json('path/to/credentials.json')

Step 3: Uploading and Analyzing the Image
To extract object information from an image, we need to upload the image to the Google Vision API and analyze it. We can use the `client.annotate_image()` method to perform this task. The response from the API will contain the detected objects along with their corresponding bounding boxes.

python
# Replace 'path/to/image.jpg' with the actual path to your image file
with open('path/to/image.jpg', 'rb') as image_file:
    content = image_file.read()

image = vision.Image(content=content)
response = client.annotate_image({'image': image}, features=[{'type': vision.Feature.Type.OBJECT_LOCALIZATION}])

Step 4: Extracting Object Information and Creating the Data Frame
Once we have the response from the API, we can extract the object information and create a data frame using the pandas library. We will iterate over the response's localized_object_annotations field, which contains the detected objects and their bounding box information. For each object, we will extract the object's name, confidence score, and bounding box coordinates.

python
objects = []
for obj in response.localized_object_annotations:
    name = obj.name
    score = obj.score
    vertices = [(vertex.x, vertex.y) for vertex in obj.bounding_poly.normalized_vertices]
    objects.append({'Name': name, 'Score': score, 'Bounding Box': vertices})

df = pd.DataFrame(objects)

Step 5: Displaying the Data Frame
Finally, we can display the created data frame to visualize the extracted object information in a tabular format.

python
print(df)

By following these steps, we can organize the extracted object information in a tabular format using the pandas data frame. The resulting data frame will contain columns representing the object's name, confidence score, and bounding box coordinates.

Example Output:

         Name     Score                             Bounding Box
0        Chair    0.987     [(0.123, 0.456), (0.789, 0.456), ...]
1        Table    0.876     [(0.234, 0.567), (0.890, 0.567), ...]
2         Lamp    0.765     [(0.345, 0.678), (0.901, 0.678), ...]
3  Wall Clock    0.654     [(0.456, 0.789), (0.012, 0.789), ...]

In this example, the data frame contains four detected objects along with their corresponding confidence scores and bounding box coordinates.

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