server.py

from fastapi import FastAPI, File, UploadFile, Form, Body
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from typing import List
import io
from PIL import Image, ImageOps
import numpy as np
import compColors
import dominantColors
import recolorReinhardAlgo
import recolorOTAlgo
import recolorTransferAlgo
import recolorLumaConverterAlgo
import recolorPaletteBasedTransfer
import recolorReinhardV2Algo
import recolorLinearColorTransfer
import recolorStrictTransfer
import matchCollection
import ColorReplacer
import ColorMask
from typing import Optional

import random

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
@app.post("/dominantColor")
async def dominant_color(file: UploadFile = File(...), num_colors: int = Form(...), ordered: bool = Form(False)):
    """
    Receive an image file and an integer and return the dominant color(s).
    """
    print('num_colors: ', num_colors)
    file_content = await file.read()
    image_bytes = io.BytesIO(file_content)
    im = Image.open(image_bytes)
    dominantColorsRGB = dominantColors.findDominantColors(image_bytes, num_colors, False)
    dominantColorsHex = [dominantColors.rgb_to_hex(color) for color in dominantColorsRGB]
    return {"dominantColors": dominantColorsHex}


@app.post("/ColorPalettes/")
async def color_palettes(colors: str = Form(...)):
    """
    Receive an array of strings representing colors and return a color palette based on these colors.
    """
    #maybe this isn't necessary. converting the string to an array of strings
    colors = [color.strip() for color in colors.split(',')]
    #generate the first pallete, which is the complementary colors of the given colors
    complementaryColors = []
    for color in colors:
        complementaryColors.append(compColors.complementary_colors(color))

    #generate the second palette using the adjacent colors algorithm:
    adjacentColors = []
    for color in colors:
        _adjcolors = compColors.adjacent_colors(color)
        for _color in _adjcolors:
            if _color not in adjacentColors:
                adjacentColors.append(_color)
    #generate the third palette using the gradient colors algorithm:
    gradientColors = []
    for i in range(len(colors)-1):
        gradientColors.append(compColors.gradient_colors(colors[i], colors[i+1]))

    #Fixing size of palletes to 5 colors:
    complementaryColors = [complementaryColors[i:i + 5] for i in range(0, len(complementaryColors), 5)]
    adjacentColors = [adjacentColors[i:i + 5] for i in range(0, len(adjacentColors), 5)]
    colors = [colors[i:i + 5] for i in range(0, len(colors), 5)]
    
    return {"inputColor": colors, "complementaryColors": complementaryColors, "adjacentColors": adjacentColors, "gradientColors": gradientColors}

@app.post("/recolor/")
async def recolor(file: UploadFile = File(...), colors: str = Form(...), new_colors: Optional[str] = Form(None), random_colors: bool = Form(False),  model: str = Form(...), mask: Optional[UploadFile] = File(None)):
    """
    Receive an image file and an array of strings representing colors of a selected pallete and recolor an image.
    """
    method = model
    invertColors = False
    colors = [color.strip() for color in colors.split(',')]
    file_content = await file.read()
    image_bytes = io.BytesIO(file_content)
    image = Image.open(image_bytes)
    if invertColors:
        image = ImageOps.invert(image)
    image_np = np.array(image)

    if new_colors is not None:
        new_colors = [new_color.strip() for new_color in new_colors.split(',')]

    if method == "CCA":
        print('CCA generated')
        #Characteristic Color Analysis
        recolorReinhardAlgo.recolor(image_np, colors)

    elif method == "OTA":
        print('OTA generated')
        #Optimal Transport Algorithm transfer
        recolorOTAlgo.recolor(image_np, colors)

    elif method =="KMEANS":
        print('KMEANS generated')
        #K-means clustering transfer
        recolorTransferAlgo.recolor(image_np, colors)

    elif method == "LUMA":
        print('Luma generated')
        #Luma converter transfer
        recolorLumaConverterAlgo.remap_image_colors(image_np, colors)

    elif method == "palette":
        #palette transfer
        print('palette generated')
        recolorPaletteBasedTransfer.recolor(image_np, colors)

    elif method == "Reinhardv2":
        print('Reinhardv2 generated')
        recolorReinhardV2Algo.recolor(image_np, colors)

    elif method == "LinearColorTransfer":
        print('LinearColorTransfer generated')
        recolorLinearColorTransfer.recolor(image_np, colors)

    elif method.startswith("StrictTransfer"):
        print('StrictTransfer started', colors, new_colors)
        if random_colors:
            random.shuffle(colors)            
            random.shuffle(new_colors)            
            print('StrictTransfer random', colors, new_colors)
        recolorStrictTransfer.recolor(image_np, colors, new_colors)        
    
    elif method == "ColorReplacer":
        print('ColorReplacer started')
        ColorReplacer.recolor_selected_area(image_np, colors[0], colors[1])
        
    elif method == "ColorMask":
        print('ColorMask started')
        ColorMask.create_mask(image_np, colors[0])

    #mask image:
    if mask is not None:
        mask_content = await mask.read()
        mask_image = Image.open(io.BytesIO(mask_content))
        # Ensure mask_image is the same size as result_image
        result_image = Image.open('./result.jpg')
        result_np = np.array(result_image)
        print('result_np', result_np.size)
        print('image_np', image_np.size)
        print('mask_image', mask_image.size)
        if mask_image.size != result_image.size:
            mask_image = mask_image.resize(result_image.size)

        mask_image = mask_image.convert('RGB')
        mask_np = np.array(mask_image)

        # Create a new image array based on the mask
        new_image_np = np.where(mask_np == 0, result_np, image_np)

        # Save the new image
        new_image = Image.fromarray(new_image_np)
        new_image.save('./result.jpg')
    
    img_file = open("./result.jpg", "rb")
    return StreamingResponse(img_file, media_type="image/jpeg")


# @app.post("/collection/")
# async def create_collection(collection: str = Body(...), colors: List[str] = Body(...)):
#     """
#     Endpoint to create a collection with items.
#     """
#     result = matchCollection.predict_palette(collection, colors[0])
#     print(result)
#     #preparar o dado pra ser respondido
#     return {"collection": result}

@app.post("/collection/")
async def create_collection(collection: str = Body(...), colors: List[str] = Body(...)):
    """
    Endpoint to create a collection with items.
    """
    palettes = [matchCollection.predict_palette(collection, color) for color in colors]
    return {"collection": collection, "palettes": palettes}

@app.get("/test/")
async def test():
    """
    Test endpoint to check if the server is running.
    """
    return {"message": "Server is running!"}





if __name__ == "__main__":
    import uvicorn
    print("Server is running")
    uvicorn.run(app, host="0.0.0.0", port=7860)




#how to run:
#source env/bin/activate     
#uvicorn server:app --reload

#curl -X POST http://0.0.0.0:4201/collection/ \ -H "Content-Type: application/json" \ -d '{"collection": "FLORAL", "colors": ["#1f3b4a", "#597375", "#7f623e", "#5c453c"]}'