Stuff

.gitignore

@@ -1,2 +1,6 @@
 *.jpeg
 *.jpg
+*.png
+imageserver/images/
+imageserver/models
+__pycache__

imageserver/.env

@@ -0,0 +1 @@
+TOKEN="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"

imageserver/Dockerfile

@@ -0,0 +1,15 @@
+FROM nvidia/cuda:11.6.0-base-ubuntu20.04
+RUN apt-get update && apt-get install python3 python3-pip -y
+
+RUN pip3 install --upgrade diffusers transformers scipy python-dotenv cuda-python fastapi uvicorn httplib2 && \
+    pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    pip3 install torch==1.11.0+cu115 torchvision==0.12.0+cu115 torchaudio==0.11.0+cu115 -f https://download.pytorch.org/whl/torch_stable.html && \
+    pip3 install python-multipart accelerate
+
+WORKDIR /app
+
+COPY main.py /app/main.py
+VOLUME /root/.cache/huggingface/diffusers/
+
+
+CMD [ "uvicorn", "main:app", "--host", "0.0.0.0", "--reload" ]

imageserver/client.py

@@ -0,0 +1,40 @@
+# import aiohttp
+# import aiofiles
+import asyncio
+import requests
+from io import BytesIO
+from PIL import Image
+import shutil
+from random import randint
+
+def main():
+    print("Starting")
+
+    img = Image.new('RGB', (25, 25), color = (randint(0, 255), randint(0, 255), randint(0, 255)))
+    img = Image.open("/home/jimmy/image.png")
+    byte_io = BytesIO()
+    img.save(byte_io, 'png')
+    byte_io.seek(0)
+
+    r = requests.post(url='http://localhost:8000?text=cartoon',
+        files={
+            'my_file': (
+                '1.png',
+                byte_io,
+                'image/png'
+            ),
+        },
+        stream=True
+    )
+    print(r.status_code)
+
+    if r.status_code == 200:
+        byte_io = BytesIO(r.content)
+        img = Image.open(byte_io)
+        img.show()
+
+if __name__ == '__main__':
+    main()
+
+
+

imageserver/docker-compose.yaml

@@ -0,0 +1,16 @@
+version: '3.9'
+
+services:
+  imageserver:
+    image: imageserver
+    build: ./
+    runtime: nvidia
+    ports:
+      - 8000:8000
+    env_file:
+      - .env
+    volumes:
+      - ./models:/root/.cache/huggingface/diffusers/
+      - ./main.py:/app/main.py
+      - ./images:/images
+    restart: unless-stopped

imageserver/main.py

@@ -0,0 +1,68 @@
+from multiprocessing import context
+from httplib2 import Response
+import torch
+import uuid
+import os
+from diffusers import StableDiffusionImg2ImgPipeline
+from dotenv import load_dotenv
+from os import getenv
+from fastapi import FastAPI, Response, HTTPException, File, UploadFile
+from pydantic import BaseModel
+import io
+from PIL.PngImagePlugin import PngInfo
+from PIL import Image
+
+load_dotenv()
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", num_inference_steps=100, revision="fp16", torch_dtype=torch.float16, use_auth_token=getenv("TOKEN"))
+pipe.to("cuda")
+
+class Text(BaseModel):
+    text: str
+
+
+
+app = FastAPI()
+
+@app.post("/",
+    responses = {
+        200: {
+            "content": {"image/png": {}}
+        }
+    },
+    response_class=Response
+)
+async def root(text: str, my_file: UploadFile = File(...)):
+    prompt = text.replace('+', ' ')
+    print(prompt)
+    request_object_content = await my_file.read()
+    img = Image.open(io.BytesIO(request_object_content))
+
+    height_orig = img.height
+    width_orig = img.width
+    aspect_ratio = width_orig / height_orig 
+    width_new = 512
+    height_new = int(width_new / aspect_ratio)
+    img = img.resize((width_new, height_new), 0)
+    try:  
+        resp = pipe(prompt, image=img)
+        print(resp)
+        image = resp.images[0]
+    except RuntimeError as e:
+        print(e)
+        raise HTTPException(status_code=202, detail="Busy")
+    except Exception as e:
+        raise HTTPException(status_code=504, detail=str(e)) 
+    if resp["nsfw_content_detected"] == [True]:
+        raise HTTPException(status_code=418, detail="NSFW")
+    
+    imgByteArr = io.BytesIO()
+    image.save(imgByteArr, format="PNG")
+    imgByteArr = imgByteArr.getvalue()
+    running = False
+    return Response(content=imgByteArr, media_type="image/png")
+    
+
+
+
+

pi.py

@@ -4,19 +4,33 @@ import cv2
 from gpiozero import LED, Button
 from picamera2 import Picamera2
 from time import sleep
+import asyncio
+import requests
+from io import BytesIO
+from PIL import Image
+import shutil
+from random import randint
+import numpy as np
+import sys
+import pyautogui
 
 red = LED(19)
 blue = LED(26)
 green = LED(13)
 button = Button(5)
+button2 = Button(2)
+button3 = Button(3)
+
 
 # Grab images as numpy arrays and leave everything else to OpenCV.
-x = 1920
+x = 512
-y = 1080
+y = 512
+
+
+print(pyautogui.size())
+
+x, y = pyautogui.size()
 
-cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
-#cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
-cv2.resizeWindow("window", x, y)
 picam2 = Picamera2()
 
 capture_config = picam2.create_preview_configuration(main={"size": (x, y),
@@ -24,25 +38,69 @@ capture_config = picam2.create_preview_configuration(main={"size": (x, y),
 picam2.configure(capture_config)
 picam2.start()
 
+cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
+# cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
 while True:
+    
+    # cv2.resizeWindow("window", x, y)
     while True:
         im = picam2.capture_array()
 
 
         cv2.imshow("window", im)
-        if cv2.waitKey(40) & 0xFF == ord('a') or button.value:
+        if cv2.waitKey(40) & 0xFF == ord('q'):
+            sys.exit()
+        if button2.value: 
             break
 
     while button.value:
         pass
 
-    while not button.value:
+    color_converted = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-        sleep(0.1)
+    img=Image.fromarray(color_converted)
 
-    while not button.value:
+    byte_io = BytesIO()
-        pass
+    img.save(byte_io, 'png')
+    byte_io.seek(0)
+
+    print("-------------------Generating Image----------------------")
+    r = requests.post(url='http://192.168.1.100:8000?text=into cyborg robot ',
+        files={
+            'my_file': (
+                '1.png',
+                byte_io,
+                'image/png'
+            ),
+        },
+        stream=True
+    )
+    print(r.status_code)
+
+    if r.status_code == 200:
+        byte_io = BytesIO(r.content)
+        img = Image.open(byte_io)
+   
+        # img.show()
+        height_orig = img.height
+        width_orig = img.width
+        aspect_ratio = width_orig / height_orig 
+        width_new = x
+        height_new = int(width_new / aspect_ratio)
+        img = img.resize((width_new, height_new), 0)
+
+        numpy_image=np.array(img) 
+        opencv_image=cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR) 
+      
+        cv2.imshow("window", opencv_image)
+        print("---------------------------------Image Generated-----------------------------")
+
+ 
+    
+    
+    print("--------------------Sleeping-------------------------")
+    sleep(10)
+    print("--------------------Slept----------------------------")
 
-    sleep(1)