OpenAI + OpenCV

idea: describe an image processing problem to OpenAI → receive code → run

github/kamangir/openai//notebooks/completion.ipynb

openai.com/completion

function-calling, not relevant.

How_to_build_a_tool-using_agent_with_Langchain

langchain.com

Write a python function that inputs an image and returns a darker version of it.

import numpy as np
from PIL import Image

# Function takes a image as input and returns a darker version of it
def make_darker(input_image):
    
    #convert the image to a numpy array
    arr = np.array(input_image)
    
    #find the shape of the array
    shape = arr.shape
    
    #create an empty array of the same size
    darker_arr = np.empty(shape, dtype='uint8')
    
    # loop through and subtract 15 from each element in order to make the image darker
    for row in range(shape[0]):
        for col in range(shape[1]):
            darker_arr[row][col] = arr[row][col] - 15
           
    # return the darker image
    return Image.fromarray(darker_arr)

prompt = """"Write a python function named runme_001 
that inputs an image as a numpy array
and uses opencv vector functions 
to return a darker version of the input image
without running a for loop on the pixels.
"""

import cv2
import numpy as np

def runme_001(img):
    # Create a new image with lower intensity
    dark_img = cv2.add(img, np.array([-50.0]))

    # re-scale the pixel
    dark_img = cv2.normalize(dark_img, None, 0, 255, cv2.NORM_MINMAX)

    return dark_img

import cv2
import numpy as np

def runme_001(src):
    # Create a darkening mask
    mask = np.ones(src.shape, dtype="uint8") * 75 

    # Apply the mask with the multiply function from opencv 
    res = cv2.multiply(src, mask) 
    return res

def runme_001(image):
    
    #import opencv and numpy libraries
    import cv2, numpy as np

    #apply the addWeight function to make it darker
    result = cv2.addWeighted(image, 0.75, np.zeros(image.shape, image.dtype), 0, 0)
    
    #return the result (darker version of the input image)
    return result

input: an image as a numpy array
uses: opencv vector functions 
return: darker version of the input image
conditions: without running a for loop on the pixels.

Next: refactor as a class + bash interface.