Can anyone help giving this code in Flutter

[md-rifatkhan]

Can anyone help giving this code in Flutter.

Shape: NHWC (Number, 512 (Height), 512 (Width), 3 (RGB Channel)
Python:

import cv2
import numpy as np
import tensorflow as tf
import time

# Load the TFLite model
interpreter = tf.lite.Interpreter(model_path="FF/realesr-general-x4v3_float32.tflite")
interpreter.allocate_tensors()

# Get input and output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Load the input image
img = cv2.imread('FF/tnn.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Resize image to match model's expected sizing
img = cv2.resize(img, (input_details[0]['shape'][2], input_details[0]['shape'][1]))
img = np.expand_dims(img, axis=0)  # Add batch dimension

# Normalize the image
img = img.astype(np.float32) / 255

# Start time for measuring inference time
start_time = time.time()

# Set the tensor to point to the input data to be inferred
interpreter.set_tensor(input_details[0]['index'], img)

# Run inference
interpreter.invoke()

# Retrieve the model output
out_mat = interpreter.get_tensor(output_details[0]['index'])

# Measure and print elapsed time
elapsed_time = time.time() - start_time
print('Inference time: ', elapsed_time)

# Post-process and save output (as needed)
# Example: Convert output to desired format, visualize, etc.

# Example of saving the output
# Assuming the output is an image-like array
out_mat = (out_mat.squeeze() * 255).clip(0, 255).astype(np.uint8)
output_img = cv2.cvtColor(out_mat, cv2.COLOR_RGB2BGR)
cv2.imwrite('output.jpg', output_img)
print("Output image saved.")

[md-rifatkhan]

Model link: drive

[md-rifatkhan]

I can pay 2$ for coffee (usdt), if someone want :)