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import cv2 |
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import json |
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import logging |
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import mediapipe as mp |
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import numpy as np |
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import os |
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import sys |
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import torch |
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from mediapipe.framework.formats import landmark_pb2 |
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from mediapipe.python.solutions.drawing_utils import _normalized_to_pixel_coordinates |
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from PIL import Image, ImageDraw |
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from transformers import DPTFeatureExtractor, DPTForDepthEstimation |
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from typing import List, Mapping, Optional, Tuple, Union, Dict, Type |
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from utils import colorize |
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from quads import QUADS |
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mp_face_mesh = mp.solutions.face_mesh |
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mp_drawing = mp.solutions.drawing_utils |
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mp_drawing_styles = mp.solutions.drawing_styles |
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NumpyImage = Type[np.ndarray] |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' |
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HF_HACK = True |
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class Point3: |
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def __init__(self, values:List[float]=3*[0] ): |
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self.values = values |
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@property |
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def x(self): |
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return self.values[0] |
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@property |
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def y(self): |
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return self.values[1] |
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@property |
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def z(self): |
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return self.values[2] |
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def to_dict(self): |
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return {'x':self.x,'y':self.y,'z':self.z} |
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class TextureCoordinate: |
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def __init__(self, values:List[float]=2*[0] ): |
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self.values = values |
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@property |
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def u(self): |
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return self.values[0] |
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@property |
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def v(self): |
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return self.values[1] |
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def to_dict(self): |
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return {'u':self.u,'v':self.v} |
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class PixelCoordinate: |
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def __init__(self, values:List[int]=2*[0] ): |
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self.values = values |
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@property |
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def x(self): |
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return self.values[0] |
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@property |
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def y(self): |
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return self.values[1] |
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def to_dict(self): |
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return {'x':self.x,'y':self.y} |
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class DepthMap: |
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MEDIA_PIPE = 'mediapipe' |
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def __init__(self, values:Dict[str,float]={'og':0} ): |
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self.values = values |
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def to_dict(self): |
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return self.values |
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class DepthMapping: |
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def __init__(self, weight:float=1, lo:float=+np.inf, hi:float=-np.inf, toLo:float=0, toHi:float=1): |
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self.weight = weight |
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self.lo = lo |
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self.hi = hi |
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self.toLo = toLo |
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self.toHi = toHi |
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self.diff = 1 |
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self.toDiff = 1 |
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self.update() |
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def reset(self): |
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self.lo = +np.inf |
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self.hi = -np.inf |
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def track(self,value): |
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self.lo = min(self.lo,value) |
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self.hi = max(self.hi,value) |
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def update(self): |
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self.diff = self.hi - self.lo |
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self.toDiff = self.toHi - self.toLo |
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return self |
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def translate(self,value): |
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if not self.diff == 0: |
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value = ( value - self.lo ) / self.diff |
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value = self.toLo + value * self.toDiff |
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value = value * self.weight |
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return value |
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def to_dict(self): |
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return { |
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'weight' : self.weight, |
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'lo' : self.lo, |
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'hi' : self.hi, |
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'toLo' : self.toLo, |
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'toHi' : self.toHi, |
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'diff' : self.diff, |
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'toDiff' : self.toDiff, |
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} |
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class WeightMap: |
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def __init__(self, values:Dict[str,DepthMapping]=None): |
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if values is None: |
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self.values = {DepthMap.MEDIA_PIPE:DepthMapping()} |
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else: |
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self.values = values |
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def set(self,key:str,depthMapping:DepthMapping): |
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self.values[key] = depthMapping |
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def totally(self,name:str): |
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if not name in self.values: |
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raise Exception( f'no weight for {k} in {self.to_dict()}' ) |
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for depthMapping in self.values.values(): |
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depthMapping.weight = 0 |
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self.values[ name ].weight = 1 |
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def saveWeights(self)->Dict[str,float]: |
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return {k:v.weight for k,v in self.values.items()} |
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def loadWeights(self,weights:Dict[str,float]): |
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for k,weight in weights.items(): |
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if k in self.values: |
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self.values[ k ].weight = weight |
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else: |
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raise Exception( f'no weight for {k} in {self.to_dict()}' ) |
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def to_dict(self): |
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return {k:dm.to_dict() for k,dm in self.values.items()} |
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return self.values |
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class MeshPoint: |
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def __init__(self, |
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position:Point3 = Point3(), |
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color:Point3 = Point3(), |
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textureCoordinate:TextureCoordinate = TextureCoordinate(), |
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pixelCoordinate:PixelCoordinate = PixelCoordinate(), |
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depthMap:DepthMap = None, |
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): |
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self.position = position |
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self.color = color |
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self.textureCoordinate = textureCoordinate |
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self.pixelCoordinate = pixelCoordinate |
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if depthMap is None: |
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self.depthMap = DepthMap({DepthMap.MEDIA_PIPE:position.values[2]}) |
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else: |
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self.depthMap = depthMap |
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def to_dict(self): |
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derp = { |
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'position' : self.position.to_dict(), |
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'color' : self.color.to_dict(), |
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'textureCoordinate' : self.textureCoordinate.to_dict(), |
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'pixelCoordinate' : self.pixelCoordinate.to_dict(), |
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} |
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if not self.depthMap is None: |
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derp[ 'depthMap' ] = self.depthMap.to_dict() |
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return derp |
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def weighDepth(self, weightMap:WeightMap = WeightMap()): |
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total_sum = sum([dm.weight for dm in weightMap.values.values()]) |
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tmp = 0 |
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for key, depthMapping in weightMap.values.items(): |
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if key in self.depthMap.values: |
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tmp = tmp + depthMapping.translate( self.depthMap.values[ key ] ) |
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else: |
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raise Exception(f'{key} from weightMap not in depthMap') |
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tmp = tmp / total_sum |
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self.position.values[2] = tmp |
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def mapLandMark(self, mediaMesh:'MediaMesh', landmark: landmark_pb2.NormalizedLandmark) -> 'MeshPoint': |
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x, y = _normalized_to_pixel_coordinates(landmark.x,landmark.y,mediaMesh.width,mediaMesh.height) |
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position = [v * mediaMesh.scale[i] for i,v in enumerate([landmark.x, landmark.y, landmark.z])] |
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self.position = Point3(position) |
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self.color = Point3([value / 255 for value in mediaMesh.image[y,x]]) |
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self.textureCoordinate = TextureCoordinate([x/mediaMesh.width,1-y/mediaMesh.height] ) |
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self.pixelCoordinate = PixelCoordinate([x,y]) |
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self.depthMap = DepthMap({DepthMap.MEDIA_PIPE:self.position.z}) |
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return self |
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def toObj(self, lines:List[str], hf_hack:bool=HF_HACK): |
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lines.append( "v " + " ".join(map(str, self.position.values + self.color.values)) ) |
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lines.append( "vt " + " ".join(map(str, self.textureCoordinate.values ) ) ) |
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class MeshFace: |
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def __init__(self,indices:List[int]=None,normal:Point3=Point3()): |
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self.indices = indices |
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self.normal = normal |
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def calculateNormal(self,meshPoints:List[MeshPoint]): |
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if self.indices is None: |
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raise Exception('indices is junk') |
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if meshPoints is None: |
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raise Exception('meshPoints is junk') |
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if len(self.indices)<3: |
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raise Exception('need at least 3 points') |
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points = [meshPoints[index-1] for index in self.indices[:3]] |
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npz = [np.array(point.position.values) for point in points] |
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v1 = npz[1] - npz[0] |
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v2 = npz[2] - npz[0] |
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normal = np.cross(v1, v2) |
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normal = normal / np.linalg.norm(normal) |
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self.normal = Point3( normal.tolist() ) |
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def toObj(self, lines:List[str], index:int, hf_hack:bool=HF_HACK): |
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lines.append( "vn " + " ".join([str(value) for value in self.normal.values]) ) |
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face_uv = "f " + " ".join([f'{vertex}/{vertex}/{index}' for vertex in self.indices]) |
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face_un = "f " + " ".join([str(vertex) for vertex in self.indices]) |
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if hf_hack: |
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lines.append( f'#{face_uv}' ) |
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lines.append( f'{face_un}' ) |
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else: |
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lines.append( face_uv ) |
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class DepthSource: |
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def __init__(self, name:str=None): |
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self.name = name |
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self.mediaMesh = None |
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self.depth:NumpyImage = None |
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self.gray:NumpyImage = None |
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def mapDepth(self, mediaMesh:'MediaMesh', depthMapping:DepthMapping=None) -> 'DepthSource': |
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return self |
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def _addDepth(self, mediaMesh:'MediaMesh', depthMapping:DepthMapping=None) -> 'DepthSource': |
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self.gray = colorize(self.depth, cmap='gray_r') |
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self.mediaMesh = mediaMesh |
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for meshPoint in mediaMesh.points: |
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depth = self.depth[meshPoint.pixelCoordinate.y,meshPoint.pixelCoordinate.x] |
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meshPoint.depthMap.values[ self.name ] = float( depth ) |
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mediaMesh.weightMap.set( self.name, self.createDepthMapping(depthMapping) ) |
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self.gray = mediaMesh.drawGrayMesh(self.name,True) |
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return self |
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def createDepthMapping(self,depthMapping:DepthMapping=None) -> DepthMapping: |
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if depthMapping is None: |
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depthMapping = DepthMapping() |
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depthMapping.reset() |
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if not self.depth is None: |
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for meshPoint in self.mediaMesh.points: |
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depth = self.depth[meshPoint.pixelCoordinate.y,meshPoint.pixelCoordinate.x] |
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depthMapping.track(float(depth)) |
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return depthMapping.update() |
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class ZoeDepthSource( DepthSource ): |
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NAME = 'zoe' |
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def __init__(self): |
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super().__init__(ZoeDepthSource.NAME) |
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self.model = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval() |
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def mapDepth(self, mediaMesh:'MediaMesh', depthMapping:DepthMapping=None) -> 'DepthSource': |
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self.depth = 1.-self.model.infer_pil(mediaMesh.image) |
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return self._addDepth(mediaMesh, depthMapping) |
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class MidasDepthSource( DepthSource ): |
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NAME = 'midas' |
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def __init__(self): |
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super().__init__(MidasDepthSource.NAME) |
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self.feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") |
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self.model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") |
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def mapDepth(self, mediaMesh:'MediaMesh', depthMapping:DepthMapping=None) -> 'DepthSource': |
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img = Image.fromarray(mediaMesh.image) |
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encoding = self.feature_extractor(img, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = self.model(**encoding) |
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predicted_depth = outputs.predicted_depth |
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prediction = torch.nn.functional.interpolate( |
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predicted_depth.unsqueeze(1), |
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size=img.size[::-1], |
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mode="bicubic", |
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align_corners=False, |
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).squeeze() |
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self.depth = prediction.cpu().numpy() |
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return self._addDepth(mediaMesh, depthMapping) |
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class MediaMesh: |
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LOG = logging.getLogger(__name__) |
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COMBINED = 'combined' |
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def __init__(self, scale:List[int]=[-1,-1,-1], weightMap:WeightMap = None, image:NumpyImage = None, annotated:NumpyImage = None, points:List[MeshPoint] = None): |
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self.scale = scale |
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if weightMap is None: |
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self.weightMap = WeightMap() |
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else: |
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self.weightMap = weightMap |
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self.image = image |
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self.annotated = annotated |
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self.points = points |
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self.meshes = {} |
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self.depthSources = {} |
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def detect(self, image:NumpyImage, min_detection_confidence:float = .5) -> 'MediaMesh': |
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self.image = image |
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self.annotated = image.copy() |
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self.points = None |
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self.width = image.shape[1] |
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self.height = image.shape[0] |
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self.ratio = self.width / self.height |
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self.scale[0] = self.ratio |
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first = True |
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with mp_face_mesh.FaceMesh( |
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static_image_mode=True, |
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max_num_faces=1, |
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min_detection_confidence=min_detection_confidence) as face_mesh: |
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results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) |
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if not results.multi_face_landmarks: |
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raise Exception( 'no faces found' ) |
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for landmarks in results.multi_face_landmarks: |
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if first: |
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self.points = self.mapLandMarks(landmarks) |
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first = False |
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self.drawLandMarks(self.annotated, landmarks) |
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self.gray = self.drawGrayMesh() |
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self.weightMap.set( DepthMap.MEDIA_PIPE, self.createDepthMapping() ) |
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return self |
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def drawLandMarks(self, image:NumpyImage, landmarks: landmark_pb2.NormalizedLandmarkList): |
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1) |
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mp_drawing.draw_landmarks( |
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image=image, |
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landmark_list=landmarks, |
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connections=mp_face_mesh.FACEMESH_TESSELATION, |
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landmark_drawing_spec=None, |
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connection_drawing_spec=mp_drawing_styles |
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.get_default_face_mesh_tesselation_style()) |
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mp_drawing.draw_landmarks( |
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image=image, |
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landmark_list=landmarks, |
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connections=mp_face_mesh.FACEMESH_CONTOURS, |
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landmark_drawing_spec=None, |
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connection_drawing_spec=mp_drawing_styles |
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.get_default_face_mesh_contours_style()) |
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def mapLandMarks(self, landmarks: landmark_pb2.NormalizedLandmarkList) -> List[MeshPoint]: |
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points = [] |
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for landmark in landmarks.landmark: |
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point = MeshPoint().mapLandMark(self, landmark) |
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points.append( point ) |
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return self.centerPoints(points) |
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def centerPoints(self,points:List[MeshPoint]=None) -> List[MeshPoint]: |
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if points is None: |
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points = self.points |
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mins = [+np.inf] * 3 |
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maxs = [-np.inf] * 3 |
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for point in points: |
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for dimension,value in enumerate( point.position.values ): |
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mins[dimension] = min(mins[dimension],value) |
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maxs[dimension] = max(maxs[dimension],value) |
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mids = [(min_val + max_val) / 2 for min_val, max_val in zip(mins, maxs)] |
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for point in points: |
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point.position.values = [(val-mid) for val, mid in zip(point.position.values,mids)] |
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print( f'mins: {mins}' ) |
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print( f'mids: {mids}' ) |
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print( f'maxs: {maxs}' ) |
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return points |
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def createDepthMapping(self,depthMapping:DepthMapping=None) -> DepthMapping: |
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if depthMapping is None: |
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depthMapping = DepthMapping() |
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for point in self.points: |
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depthMapping.track(point.position.z) |
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return depthMapping.update() |
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def drawGrayMesh(self, source:str=DepthMap.MEDIA_PIPE, invert:bool=False): |
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image = Image.new("RGB", (self.width, self.height), (88,13,33)) |
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draw = ImageDraw.Draw(image) |
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minZ = np.inf |
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maxZ = -np.inf |
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depths = [] |
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for point in self.points: |
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depth = point.depthMap.values[source] |
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depths.append( depth ) |
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minZ = min( minZ, depth ) |
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maxZ = max( maxZ, depth ) |
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difZ = maxZ - minZ |
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if 0 == difZ: |
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difZ = 1 |
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depths = [(depth-minZ)/difZ for depth in depths] |
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for quad in QUADS: |
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points = [tuple(self.points[index-1].pixelCoordinate.values) for index in quad] |
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colors = [tuple(3*[int(255*depths[index-1])]) for index in quad] |
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color = int(np.average(colors)) |
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if invert: |
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color = 255 - color |
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draw.polygon(points, fill=tuple(3*[color])) |
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return np.asarray(image) |
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def toObj(self, name:str='sweet', hf_hack:bool=HF_HACK): |
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print( '-----------------------------------------------------------------------------' ) |
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obj = [f'o {name}Mesh'] |
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mtl = f'newmtl {name}Material\nmap_Kd {name}.png\n' |
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c = '#' if hf_hack else '' |
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obj.append( f'{c}mtllib {name}.mtl' ) |
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obj.append( f'##################################################################' ) |
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obj.append( f'# to bring into blender with uvs:' ) |
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obj.append( f'# put the following 2 lines into {name}.mtl uncommented' ) |
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obj.append( f'#newmtl {name}Material' ) |
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obj.append( f'#map_Kd {name}.png' ) |
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obj.append( f'# remove lines from this file starting with "f "' ) |
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obj.append( f'# uncomment the lines that start with "#f "' ) |
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obj.append( f'##################################################################' ) |
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for key, depthMapping in self.weightMap.values.items(): |
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depthMapping.update() |
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print( f'{name}.{key} -> {depthMapping.to_dict()}' ) |
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for point in self.points: |
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point.weighDepth(self.weightMap) |
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self.centerPoints() |
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for point in self.points: |
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point.toObj(obj,hf_hack) |
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obj.append( f'usemtl {name}Material' ) |
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index = 0 |
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for quad in QUADS: |
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index = 1 + index |
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face = MeshFace(quad) |
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face.calculateNormal(self.points) |
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face.toObj(obj, index, hf_hack) |
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obj.append( f'##################################################################' ) |
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obj.append( f'# EOF' ) |
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obj.append( f'##################################################################' ) |
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print( '-----------------------------------------------------------------------------' ) |
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return obj,mtl |
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def to_dict(self): |
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return { |
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'width' : self.width, |
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'height' : self.height, |
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'ratio' : self.ratio, |
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'weightMap' : {key: value.to_dict() for key, value in self.weightMap.values.items()}, |
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'points' : [point.to_dict() for point in self.points] |
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} |
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def singleSourceMesh(self,name:str, hf_hack:bool=HF_HACK): |
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before = self.weightMap.saveWeights() |
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self.weightMap.totally(name) |
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obj,mtl = self.toObj(name) |
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self.weightMap.loadWeights( before ) |
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return obj,mtl |
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def meshmerizing(self,hf_hack:bool=HF_HACK): |
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for depthSource in self.depthSources: |
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depthSource.mapDepth(self,self.weightMap.values[depthSource.name]) |
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obj,mtl = self.toObj(MediaMesh.COMBINED) |
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self.meshes = {MediaMesh.COMBINED:(obj,mtl)} |
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|
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for source in self.depthSources: |
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self.meshes[ source.name ] = (self.singleSourceMesh(source.name)) |
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self.meshes[DepthMap.MEDIA_PIPE] = (self.singleSourceMesh(DepthMap.MEDIA_PIPE)) |
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return self.meshes |
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|
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def demoSetup(self) -> 'MediaMesh': |
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self.depthSources = [ ZoeDepthSource(), MidasDepthSource() ] |
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|
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for depthSource in self.depthSources: |
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self.weightMap.set( depthSource.name, depthSource.createDepthMapping() ) |
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|
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self.weightMap.values[ ZoeDepthSource.NAME ].toHi = 1.77 |
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self.weightMap.values[ MidasDepthSource.NAME ].toHi = 2.55 |
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self.weightMap.values[ ZoeDepthSource.NAME ].weight = 1.00 |
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self.weightMap.values[ MidasDepthSource.NAME ].weight = 0.22 |
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return self |
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def main(self): |
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if not 2 == len(sys.argv): |
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raise Exception( 'usage: MediaMesh.py <image filename>' ) |
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mediaMesh = MediaMesh().demoSetup() |
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mediaMesh.detect(cv2.imread( sys.argv[1] ) ) |
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for name,mesh in mediaMesh.meshmerizing().items(): |
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obj = mesh[0] |
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mtl = mesh[1] |
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with open(f"{name}.obj", "w") as file: |
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file.write( '\n'.join(obj) ) |
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with open(f"{name}.mtl", "w") as file: |
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file.write( mtl ) |
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cv2.imwrite( 'mesh.png', mediaMesh.annotated ) |
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cv2.imwrite( 'mpg.png', mediaMesh.gray ) |
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for source in mediaMesh.depthSources: |
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cv2.imwrite( f'{source.name}.png', source.gray ) |
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with open("mesh.json", "w") as file: |
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json.dump(mediaMesh.to_dict(), file, indent=4) |
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if __name__ == "__main__": |
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MediaMesh().main() |
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