Ccd1 / main.py
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Create main.py
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from fastapi import FastAPI, File, UploadFile
from fastapi.responses import StreamingResponse
import numpy as np
import cv2
import io
from PIL import Image
# Load the models
print("Loading models...")
net = cv2.dnn.readNetFromCaffe('colorization_deploy_v2.prototxt', 'colorization_release_v2.caffemodel')
pts = np.load('pts_in_hull.npy')
class8 = net.getLayerId("class8_ab")
conv8 = net.getLayerId("conv8_313_rh")
pts = pts.transpose().reshape(2, 313, 1, 1)
net.getLayer(class8).blobs = [pts.astype("float32")]
net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype='float32')]
# Initialize FastAPI app
app = FastAPI()
def read_image(file):
# Open the image with Pillow
pil_image = Image.open(file)
# Convert the Pillow image to a NumPy array
image_array = np.array(pil_image)
return image_array
def colorize_image(image):
# Convert the PIL image to OpenCV format
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
scaled = image.astype("float32") / 255.0
lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)
resized = cv2.resize(lab, (224, 224))
L = cv2.split(resized)[0]
L -= 50
net.setInput(cv2.dnn.blobFromImage(L))
ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
ab = cv2.resize(ab, (image.shape[1], image.shape[0]))
L = cv2.split(lab)[0]
colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2RGB)
colorized = np.clip(colorized, 0, 1)
colorized = (255 * colorized).astype("uint8")
return colorized
@app.post("/upload/")
async def upload(file: UploadFile = File(...)):
# Read the image using Pillow and convert to NumPy array
image_array = read_image(file.file)
# Process the image
colorized_image = colorize_image(image_array)
# Convert the colorized image to bytes
pil_image = Image.fromarray(colorized_image)
buf = io.BytesIO()
pil_image.save(buf, format="PNG")
buf.seek(0)
return StreamingResponse(buf, media_type="image/png")