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| # Codeformer enchance plugin | |
| # author: Vladislav Janvarev | |
| # CountFloyd 20230717, extended to blend original/destination images | |
| from chain_img_processor import ChainImgProcessor, ChainImgPlugin | |
| import os | |
| from PIL import Image | |
| from numpy import asarray | |
| modname = os.path.basename(__file__)[:-3] # calculating modname | |
| # start function | |
| def start(core:ChainImgProcessor): | |
| manifest = { # plugin settings | |
| "name": "Codeformer", # name | |
| "version": "3.0", # version | |
| "default_options": { | |
| "background_enhance": True, # | |
| "face_upsample": True, # | |
| "upscale": 2, # | |
| "codeformer_fidelity": 0.8, | |
| "skip_if_no_face":False, | |
| }, | |
| "img_processor": { | |
| "codeformer": PluginCodeformer # 1 function - init, 2 - process | |
| } | |
| } | |
| return manifest | |
| def start_with_options(core:ChainImgProcessor, manifest:dict): | |
| pass | |
| class PluginCodeformer(ChainImgPlugin): | |
| def init_plugin(self): | |
| import plugins.codeformer_app_cv2 | |
| pass | |
| def process(self, img, params:dict): | |
| import copy | |
| # params can be used to transfer some img info to next processors | |
| from plugins.codeformer_app_cv2 import inference_app | |
| options = self.core.plugin_options(modname) | |
| if "face_detected" in params: | |
| if not params["face_detected"]: | |
| return img | |
| # don't touch original | |
| temp_frame = copy.copy(img) | |
| if "processed_faces" in params: | |
| for face in params["processed_faces"]: | |
| start_x, start_y, end_x, end_y = map(int, face['bbox']) | |
| padding_x = int((end_x - start_x) * 0.5) | |
| padding_y = int((end_y - start_y) * 0.5) | |
| start_x = max(0, start_x - padding_x) | |
| start_y = max(0, start_y - padding_y) | |
| end_x = max(0, end_x + padding_x) | |
| end_y = max(0, end_y + padding_y) | |
| temp_face = temp_frame[start_y:end_y, start_x:end_x] | |
| if temp_face.size: | |
| temp_face = inference_app(temp_face, options.get("background_enhance"), options.get("face_upsample"), | |
| options.get("upscale"), options.get("codeformer_fidelity"), | |
| options.get("skip_if_no_face")) | |
| temp_frame[start_y:end_y, start_x:end_x] = temp_face | |
| else: | |
| temp_frame = inference_app(temp_frame, options.get("background_enhance"), options.get("face_upsample"), | |
| options.get("upscale"), options.get("codeformer_fidelity"), | |
| options.get("skip_if_no_face")) | |
| if not "blend_ratio" in params: | |
| return temp_frame | |
| temp_frame = Image.blend(Image.fromarray(img), Image.fromarray(temp_frame), params["blend_ratio"]) | |
| return asarray(temp_frame) | |