208 lines
8.2 KiB
Python
208 lines
8.2 KiB
Python
import base64
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import hashlib
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import json
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import random
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import uuid
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import cv2
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from flask import Flask, request, jsonify
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import sys
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import os
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from PIL import Image
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import io
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import numpy as np
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import websocket
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import openpose_gen as opg
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from comfy_socket import get_images
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from postprocessing import expo_shuffle_image_steps, expo_add_to_background_image, expo_postprocess_main
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import skeleton_lib as skel
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sys.path.append('./')
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app = Flask(__name__)
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info = json.load(open('info.json'))
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comfyui_address = info['comfyui_address']
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expo_raw_sd_dir = info['expo_raw_sd_dir']
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expo_openpose_dir = info['expo_openpose_dir']
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expo_postprocessed_dir = info['expo_postprocessed_dir']
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expo_postprocess_temp_dir = info['expo_postprocess_temp_dir']
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on_postprocessing = False
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on_testing = False
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@app.route('/expo_fencing_pose', methods=['POST'])
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def expo_fencing_pose():
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if on_postprocessing:
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return jsonify({"status": "error", "message": "Postprocessing in progress"}), 503
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if request.is_json:
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data = request.get_json()
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coordinates = data['coordinates']
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canvas_size = data['canvas_size']
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batch = data['batch']
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step = data['step']
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if coordinates is None or canvas_size is None or 'batch' not in data or 'step' not in data:
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return jsonify({"status": "error", "message": "Missing data"}), 422
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right_fencer_coordinates = get_predicted_coordinates(coordinates)
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left_fencer_dir = os.path.join(expo_openpose_dir, 'left_fencer')
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os.makedirs(left_fencer_dir, exist_ok=True)
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right_fencer_dir = os.path.join(expo_openpose_dir, 'right_fencer')
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os.makedirs(right_fencer_dir, exist_ok=True)
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left_openpose_image_path = opg.expo_save_bodypose(canvas_size[0], canvas_size[1], coordinates, batch, step, left_fencer_dir, skel.coco_limbSeq, skel.coco_colors)
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right_openpose_image_path = opg.expo_save_bodypose(canvas_size[0], canvas_size[1], right_fencer_coordinates, batch, step, right_fencer_dir, skel.coco_limbSeq, skel.coco_colors)
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left_fencer_raw_image_dir = os.path.join(expo_raw_sd_dir, 'left_fencer')
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os.makedirs(left_fencer_raw_image_dir, exist_ok=True)
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right_fencer_raw_image_dir = os.path.join(expo_raw_sd_dir, 'right_fencer')
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os.makedirs(right_fencer_raw_image_dir, exist_ok=True)
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expo_fencer_prompt(left_openpose_image_path, left_fencer_raw_image_dir, batch, step)
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expo_fencer_prompt(right_openpose_image_path, right_fencer_raw_image_dir, batch, step)
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return jsonify({"status": "success", "message": "Data received"}), 201
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else:
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return jsonify({"status": "error", "message": "Request must be JSON"}), 415
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def get_predicted_coordinates(coordinates: list, width: int, height: int) -> list:
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# TODO implement the model to predict the right fencer's coordinates
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# coordinates = [x1, y1, c1, x2, y2, c2, ...],
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# where x, y are the coordinates and c is the confidence score
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# there should be 18 keypoints from 0 to 17
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# they are not normalized, they are by the size of the width and height
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# the the limbSeq and colors of points need to convert from and to skel.coco_limbSeq, skel.coco_colors
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# those are in skeleton_lib.py
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# when testing, can visualize with the method expo_save_bodypose in openpose_gen.py
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return mirror_coordinates(coordinates, width)
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def mirror_coordinates(coordinates: list, width: int) -> list:
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for i in range(0, len(coordinates), 3):
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coordinates[i] = width - coordinates[i]
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return coordinates
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def expo_fencer_prompt(openpose_image_path, save_dir, batch, step):
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prompt = json.loads(open("./prompts/fencer_03.json", "r", encoding="utf-8").read())
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openpose_image_name = opg.upload_image(openpose_image_path)
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opg.upload_image("./images/ref_black.png", "ref_black.png")
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print(openpose_image_name)
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prompt["3"]["inputs"]["seed"] = random.randint(0, 10000000000)
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prompt["29"]["inputs"]['image'] = "ref_black.png"
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prompt["17"]["inputs"]['image'] = openpose_image_name
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client_id = hashlib.sha256(str(random.getrandbits(256)).encode('utf-8')).hexdigest()
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ws = websocket.WebSocket()
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ws.connect("ws://{}/ws?clientId={}".format(comfyui_address, client_id))
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images = get_images(ws, prompt, client_id)
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for node_id in images:
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for idx, image_data in enumerate(images[node_id]):
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image = Image.open(io.BytesIO(image_data))
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image_path = os.path.join(save_dir, f"{batch}_{step}.png")
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image.save(image_path)
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def expo_clear_images():
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if on_testing:
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return
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for root, dirs, files in os.walk(expo_openpose_dir):
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for file in files:
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os.remove(os.path.join(root, file))
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for root, dirs, files in os.walk(expo_raw_sd_dir):
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for file in files:
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os.remove(os.path.join(root, file))
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for root, dirs, files in os.walk(expo_postprocess_temp_dir):
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for file in files:
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os.remove(os.path.join(root, file))
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@app.route('/expo_postprocess', methods=['POST'])
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def expo_postprocess():
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if on_postprocessing:
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return jsonify({"status": "error", "message": "Postprocessing in progress"}), 503
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print("Postprocessing")
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on_postprocessing = True
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expo_postprocess_main()
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expo_clear_images()
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on_postprocessing = False
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@app.route('/gen_image', methods=['POST'])
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def gen_image():
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if request.is_json:
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data = request.get_json()
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coordinates = data['coordinates']
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canvas_size = data['canvas_size']
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pid = data['pid']
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if not coordinates or not canvas_size:
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return jsonify({"status": "error", "message": "Missing data"}), 422
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openpose_image_path = opg.save_bodypose(canvas_size[0], canvas_size[1], coordinates, pid)
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# gen_fencer_prompt(openpose_image_path, pid, comfyui_address)
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return jsonify({"status": "success", "message": "Data received"}), 201
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else:
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return jsonify({"status": "error", "message": "Request must be JSON"}), 415
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@app.route('/gen_group_pic', methods=['POST'])
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def gen_group_pic():
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if request.is_json:
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data = request.get_json()
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coordinates_list = data['coordinates_list']
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canvas_size = data['canvas_size']
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pid = data['pid']
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base_image = base64.b64decode(data['base_image'])
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if not coordinates_list or not canvas_size or not base_image or not pid:
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return jsonify({"status": "error", "message": "Missing data"}), 422
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for i in range(len(coordinates_list)):
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coordinates_list[i] = coordinates_list[i]['coordinates']
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openpose_image_path = opg.save_bodypose_mulit(canvas_size[0], canvas_size[1], coordinates_list, pid)
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gen_group_pic_prompt(openpose_image_path, base_image, pid, comfyui_address)
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return jsonify({"status": "success", "message": "Data received"}), 201
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else:
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return jsonify({"status": "error", "message": "Request must be JSON"}), 415
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def gen_fencer_prompt(openpose_image_path, pid, comfyUI_address):
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with open("./prompts/fencerAPI.json", "r") as f:
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prompt_json = f.read()
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prompt = json.loads(prompt_json)
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openpose_image_name = opg.upload_image_circular_queue(openpose_image_path, 20, pid, comfyUI_address)
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opg.upload_image("./images/ref_black.png", "ref_black.png")
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prompt["3"]["inputs"]["seed"] = random.randint(0, 10000000000)
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prompt["29"]["inputs"]['image'] = "./images/ref_black.png"
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prompt["17"]["inputs"]['image'] = openpose_image_name
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opg.queue_prompt(prompt, comfyUI_address)
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def gen_group_pic_prompt(openpose_image_path, base_image, pid, comfyUI_address):
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with open("./prompts/group_pic.json", "r") as f:
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prompt_json = f.read()
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prompt = json.loads(prompt_json)
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openpose_image_name = opg.upload_image_circular_queue(openpose_image_path, 30, pid, comfyUI_address)
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base_image_name = opg.upload_image_circular_queue(base_image, 30, pid, comfyUI_address)
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prompt["3"]["inputs"]["seed"] = random.randint(0, 10000000000)
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prompt["10"]["inputs"]['image'] = openpose_image_name
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prompt["14"]["inputs"]['image'] = base_image_name
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opg.queue_prompt(prompt, comfyUI_address)
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if __name__ == '__main__':
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expo_postprocess()
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# app.run(debug=True) |