Duplicate from verkaDerkaDerk/face-image-to-face-obj

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Face Image to Face Quad Mesh
+emoji: 🐢
+colorFrom: red
+colorTo: pink
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+duplicated_from: verkaDerkaDerk/face-image-to-face-obj
+---
+
+Uses MediaPipe to detect a face in an image and convert it to a (mostly) quad mesh.
+Currently saves to OBJ, hopefully glb at some point with color data.
+The 3d viewer has Y pointing the opposite direction from Blender, so ya hafta spin it.
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+########################################################################################
+import gradio as gr
+
+import cv2
+import matplotlib
+import matplotlib.cm
+import mediapipe as mp
+import numpy as np
+import os
+import struct
+import tempfile
+import torch
+
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.python.solutions.drawing_utils import _normalized_to_pixel_coordinates
+from PIL import Image
+from quads import QUADS
+from typing import List, Mapping, Optional, Tuple, Union
+from utils import colorize, get_most_recent_subdirectory
+
+class face_image_to_face_mesh:
+    def __init__(self):
+        self.zoe_me = True
+        self.uvwrap = not True
+
+    def demo(self):
+        if self.zoe_me:
+            DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+            self.zoe = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval()
+
+        demo = gr.Blocks(css=self.css(), cache_examples=True)
+        with demo:
+            gr.Markdown(self.header())
+
+            with gr.Row():
+                with gr.Column():
+                    upload_image = gr.Image(label="Input image", type="numpy", source="upload")
+                    self.temp_dir = get_most_recent_subdirectory( upload_image.DEFAULT_TEMP_DIR )
+                    print( f'The temp_dir is {self.temp_dir}' )
+
+                    gr.Examples( examples=[
+                        'examples/blonde-00081-399357008.png',
+                        'examples/dude-00110-1227390728.png',
+                        'examples/granny-00056-1867315302.png',
+                        'examples/tuffie-00039-499759385.png',
+                        'examples/character.png',
+                    ], inputs=[upload_image] )
+                    upload_image_btn = gr.Button(value="Detect faces")
+                    if self.zoe_me:
+                        with gr.Group():
+                            zoe_scale = gr.Slider(label="Mix the ZoeDepth with the MediaPipe Depth", value=1, minimum=0, maximum=1, step=.01)
+                            flat_scale = gr.Slider(label="Depth scale, smaller is flatter and possibly more flattering", value=1, minimum=0, maximum=1, step=.01)
+                            min_detection_confidence = gr.Slider(label="Mininum face detection confidence", value=.5, minimum=0, maximum=1.0, step=0.01)
+                    else:
+                        use_zoe = False
+                        zoe_scale = 0
+                    with gr.Group():
+                        gr.Markdown(self.footer())
+
+                with gr.Column():
+                    with gr.Group():
+                        output_mesh = gr.Model3D(clear_color=3*[0],  label="3D Model",elem_id='mesh-display-output')
+                        output_image = gr.Image(label="Output image",elem_id='img-display-output')
+                        depth_image = gr.Image(label="Depth image",elem_id='img-display-output')
+                        num_faces_detected = gr.Number(label="Number of faces detected", value=0)
+
+            upload_image_btn.click(
+                fn=self.detect, 
+                inputs=[upload_image, min_detection_confidence,zoe_scale,flat_scale], 
+                outputs=[output_mesh, output_image, depth_image, num_faces_detected]
+            )
+        demo.launch()
+
+
+    def detect(self, image, min_detection_confidence, zoe_scale, flat_scale):
+        width  = image.shape[1]
+        height = image.shape[0]
+        ratio  = width / height
+
+        mp_drawing = mp.solutions.drawing_utils
+        mp_drawing_styles = mp.solutions.drawing_styles
+        mp_face_mesh = mp.solutions.face_mesh
+            
+        mesh = "examples/converted/in-granny.obj"
+
+        if self.zoe_me and 0 < zoe_scale:
+            depth = self.zoe.infer_pil(image)
+            idepth = colorize(depth, cmap='gray_r')
+        else:
+            depth = None
+            idepth = image
+
+        drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+        with mp_face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=1,
+            min_detection_confidence=min_detection_confidence) as face_mesh:
+            results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+            if not results.multi_face_landmarks:
+                return mesh, image, idepth, 0
+
+            annotated_image = image.copy()
+            for face_landmarks in results.multi_face_landmarks:
+                (mesh,mtl,png) = self.toObj(image=image, width=width, height=height, ratio=ratio, landmark_list=face_landmarks, depth=depth, zoe_scale=zoe_scale, flat_scale=flat_scale)
+
+                mp_drawing.draw_landmarks(
+                    image=annotated_image,
+                    landmark_list=face_landmarks,
+                    connections=mp_face_mesh.FACEMESH_TESSELATION,
+                    landmark_drawing_spec=None,
+                    connection_drawing_spec=mp_drawing_styles
+                    .get_default_face_mesh_tesselation_style())
+                mp_drawing.draw_landmarks(
+                    image=annotated_image,
+                    landmark_list=face_landmarks,
+                    connections=mp_face_mesh.FACEMESH_CONTOURS,
+                    landmark_drawing_spec=None,
+                    connection_drawing_spec=mp_drawing_styles
+                    .get_default_face_mesh_contours_style())
+
+            return mesh, annotated_image, idepth, 1
+
+    def toObj( self, image: np.ndarray, width:int, height:int, ratio: float, landmark_list: landmark_pb2.NormalizedLandmarkList, depth: np.ndarray, zoe_scale: float, flat_scale: float):
+        print( f'you have such pretty hair', self.temp_dir )
+
+        hf_hack = True
+        if hf_hack:
+            obj_file = tempfile.NamedTemporaryFile(suffix='.obj', delete=False)
+            mtl_file = tempfile.NamedTemporaryFile(suffix='.mtl', delete=False)
+            png_file = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        else:
+            obj_file = tempfile.NamedTemporaryFile(suffix='.obj', dir=self.temp_dir, delete=False)
+            mtl_file = tempfile.NamedTemporaryFile(suffix='.mtl', dir=self.temp_dir, delete=False)
+            png_file = tempfile.NamedTemporaryFile(suffix='.png', dir=self.temp_dir, delete=False)
+
+        ############################################
+        (points,coordinates,colors) = self.landmarksToPoints( image, width, height, ratio, landmark_list, depth, zoe_scale, flat_scale )
+        ############################################
+
+        lines = []
+
+        lines.append( f'o MyMesh' )
+
+        if hf_hack:
+            # the 'file=' is a gradio hack
+            lines.append( f'#mtllib file={mtl_file.name}' )
+        else:
+            # the 'file=' is a gradio hack
+            lines.append( f'mtllib file={mtl_file.name}' )
+
+        for index, point in enumerate(points):
+            color = colors[index]
+            scaled_color = [value / 255 for value in color]  # Scale colors down to 0-1 range
+            flipped = [-value for value in point]
+            flipped[ 0 ] = -flipped[ 0 ]
+            lines.append( "v " + " ".join(map(str, flipped + color)) )
+
+        for coordinate in coordinates:
+            lines.append( "vt " + " ".join([str(value) for value in coordinate]) )
+
+        for quad in QUADS:
+            #quad = list(reversed(quad))
+            normal = self.totallyNormal( points[ quad[ 0 ] -1 ], points[ quad[ 1 ] -1 ], points[ quad[ 2 ] -1 ] )
+            lines.append( "vn " + " ".join([str(value) for value in normal]) )
+
+        lines.append( 'usemtl MyMaterial' )
+
+        quadIndex = 0
+        for quad in QUADS:
+            quadIndex = 1 + quadIndex
+            face_uv = "f " + " ".join([f'{vertex}/{vertex}/{quadIndex}' for vertex in quad])
+            face_un = "f " + " ".join([str(vertex) for vertex in quad])
+            if self.uvwrap:
+                lines.append( face_uv )
+            else:
+                lines.append( f'#{face_uv}' )
+                lines.append( f'{face_un}' )
+                #"f " + " ".join([str(vertex) for vertex in quad]) )
+
+        out = open( obj_file.name, 'w' )
+        out.write( '\n'.join( lines ) + '\n' )
+        out.close()
+
+        ############################################
+
+        lines = []
+        lines.append( 'newmtl MyMaterial' )
+        lines.append( f'map_Kd file={png_file.name}' ) # the 'file=' is a gradio hack
+
+        out = open( mtl_file.name, 'w' )
+        out.write( '\n'.join( lines ) + '\n' )
+        out.close()
+
+        ############################################
+
+        cv2.imwrite(png_file.name, cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+        ############################################
+
+        print( f'I know it is special to you so I saved it to {obj_file.name} since we are friends' )
+        return (obj_file.name,mtl_file.name,png_file.name)
+
+    def landmarksToPoints( self, image:np.ndarray, width: int, height: int, ratio: float, landmark_list: landmark_pb2.NormalizedLandmarkList, depth: np.ndarray, zoe_scale: float, flat_scale: float ):
+        points      = [] # 3d vertices
+        coordinates = [] # 2d texture coordinates
+        colors      = [] # 3d rgb info
+
+        mins = [+np.inf] * 3
+        maxs = [-np.inf] * 3
+
+        mp_scale = 1 - zoe_scale
+        print( f'zoe_scale:{zoe_scale}, mp_scale:{mp_scale}' )
+
+        for idx, landmark in enumerate(landmark_list.landmark):
+            x, y = _normalized_to_pixel_coordinates(landmark.x,landmark.y,width,height)
+            color = image[y,x]
+            colors.append( [value / 255 for value in color ] )
+            coordinates.append( [x/width,1-y/height] )
+
+            if depth is not None:
+                landmark.z = depth[y, x] * zoe_scale + mp_scale * landmark.z
+
+            landmark.z = landmark.z * flat_scale
+
+            point = [landmark.x * ratio, landmark.y, landmark.z];
+            for pidx,value in enumerate( point ):
+                mins[pidx] = min(mins[pidx],value)
+                maxs[pidx] = max(maxs[pidx],value)
+            points.append( point )
+
+        mids = [(min_val + max_val) / 2 for min_val, max_val in zip(mins, maxs)]
+        for idx,point in enumerate( points ):
+            points[idx] = [(val-mid) for val, mid in zip(point,mids)]
+
+        print( f'mins: {mins}' )
+        print( f'mids: {mids}' )
+        print( f'maxs: {maxs}' )
+        return (points,coordinates,colors)
+
+
+    def totallyNormal(self, p0, p1, p2):
+        v1 = np.array(p1) - np.array(p0)
+        v2 = np.array(p2) - np.array(p0)
+        normal = np.cross(v1, v2)
+        normal = normal / np.linalg.norm(normal)
+        return normal.tolist()    
+
+
+    def header(self):
+        return ("""
+                # Image to Quad Mesh
+
+                Uses MediaPipe to detect a face in an image and convert it to a quad mesh.
+                Saves to OBJ since gltf does not support quad faces.  The 3d viewer has Y pointing the opposite direction from Blender, so ya hafta spin it.
+
+                The face depth with Zoe can be a bit much and without it is a bit generic. In blender you can fix this just by snapping to the high poly model. For photos turning it down to .4 helps, but may still need cleanup...
+
+                Highly recommend running it locally. The 3D model has uv values in the faces, but you will have to either use the script or do some manually tomfoolery.
+
+                Quick import result in examples/converted/movie-gallery.mp4 under files
+        """)
+
+
+    def footer(self):
+        return ( """
+            # Using the Textured Mesh in Blender
+            
+            There a couple of annoying steps atm after you download the obj from the 3d viewer. 
+            
+            You can use the script meshin-around.sh in the files section to do the conversion or manually:
+            
+            1. edit the file and change the mtllib line to use fun.mtl
+            2. replace / delete all lines that start with 'f', eg :%s,^f.*,,
+            3. uncomment all the lines that start with '#f', eg: :%s,^#f,f,
+            4. save and exit
+            5. create fun.mtl to point to the texture like:
+            
+            ```
+            newmtl MyMaterial
+            map_Kd fun.png
+            ```
+            
+            Make sure the obj, mtl and png are all in the same directory
+            
+            Now the import will have the texture data: File -> Import -> Wavefront (obj) -> fun.obj
+            
+            This is all a work around for a weird hf+gradios+babylonjs bug which seems to be related to the version
+            of babylonjs being used... It works fine in a local babylonjs sandbox...
+            
+            # Suggested Workflows
+            
+            Here are some workflow ideas.
+            
+            ## retopologize high poly face mesh
+            
+            1. sculpt high poly mesh in blender
+            2. snapshot the face
+            3. generate the mesh using the mediapipe stuff
+            4. import the low poly mediapipe face
+            5. snap the mesh to the high poly model
+            6. model the rest of the low poly model
+            7. bake the normal / etc maps to the low poly face model
+            8. it's just that easy 😛
+            
+            Ideally it would be a plugin...
+            
+            ## stable diffusion integration
+            
+            1. generate a face in sd
+            2. generate the mesh
+            3. repose it and use it for further generation
+            
+            May need to expanded the generated mesh to cover more, maybe with
+            <a href="https://github.com/shunsukesaito/PIFu" target="_blank">PIFu model</a>.
+            
+        """)
+
+
+    def css(self):
+       return ("""
+            #mesh-display-output {
+                max-height: 44vh;
+                max-width:  44vh;
+                width:auto;
+                height:auto
+                }
+            #img-display-output {
+                max-height: 28vh;
+                max-width:  28vh;
+                width:auto;
+                height:auto
+                }
+        """)
+
+
+face_image_to_face_mesh().demo()
+
+# EOF
+########################################################################################

examples/converted/README.md

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+
+1. downloaded all the obj files
+2. for i in in-*obj ; do o=$( echo ${i} | cut -f2- -d- ) ; ../../meshin-around.sh ${i} ${o} ; done 
+3. for i in ../*png ; do o=$(basename ${i} | sed 's,-[^.]*\.,.,' ) ; cp -i ${i} ${o} ; done
+

examples/converted/blonde.mtl

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+newmtl MyMaterial
+illum 2
+map_Kd blonde.png

examples/converted/character.mtl

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+newmtl MyMaterial
+illum 2
+map_Kd character.png

examples/converted/converted.blend.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8886653880134a39d43a39bd5b7a2b2d7554fed15a749d98962477272dc5cef9
+size 310421

examples/converted/dude.mtl

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+newmtl MyMaterial
+illum 2
+map_Kd dude.png

examples/converted/granny.mtl

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+newmtl MyMaterial
+illum 2
+map_Kd granny.png

examples/converted/tuffie.mtl

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+newmtl MyMaterial
+illum 2
+map_Kd tuffie.png

meshin-around.sh

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+#!/bin/bash	
+
+_meshin_around_main() {
+	local mesh="${1}" ; shift
+	local mash="${1-fun.obj}" ; shift
+
+	if [ "" = "${mesh}" ] ; then
+		echo "usage: meshin-around.sh <download.obj>"
+		return 1
+	fi
+
+	local name=$( basename ${mash} | sed 's,\.obj$,,' )
+	local mtl="${name}.mtl"
+	local png="${name}.png"
+
+	if [ -f ${mash} ] ; then
+		echo "${mash} already exists"
+	else
+		echo "creating ${mash} for ${mesh}"
+		sed "s,^f.*,,;s,#f,f,;s,.*mtllib.*,mtllib ${mtl}," ${mesh} > ${mash} || exit ${?}
+	fi
+
+	if [ -f "${mtl}" ] ; then 
+		echo "${mtl} already exists"
+	else
+		echo "creating ${mtl} for ${mash}"
+		echo -e "newmtl MyMaterial\nmap_Kd ${png}" > ${mtl} || exit ${?}
+	fi
+
+	if [ -f "${png}" ] ; then
+		echo "${png} looks good"
+	else
+		echo "be sure your texture is in pwd and named ${png} or edit ${mtl}"
+	fi
+}
+
+_meshin_around_main ${*}

quads.py

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+# This was created by importing a MediaPipe tesselated mesh and manually converting it in blender
+
+QUADS = [
+	[300, 334, 333, 298] , [  1,  12, 303, 268] , [234, 233, 122, 129] , [270, 304, 305, 271] , [246, 129, 115, 189] ,
+	[112, 118, 229,  32] , [104,  55,  69, 105] , [228,  35, 128, 235] , [120, 102, 101, 121] , [ 74,  73,  38,  40] ,
+	[ 71,  47,  54,  64] , [135, 132, 116, 221] , [335, 294, 299, 334] , [ 73,  12,   1,  38] , [ 42,  43,  81,  82] ,
+	[166,  93,  41,  40] , [122, 233, 232, 121] , [215, 213, 217, 208] , [183,  84,  85, 182] , [376, 308, 321, 322] ,
+	[ 30, 161, 160,  28] , [ 57,  29, 159, 158] , [ 84, 202, 201,  19] , [117, 144,  35, 228] , [204, 207,  93, 166] ,
+	[139, 216,  59, 173] , [276, 282,   6,   5] , [ 25, 145, 164, 111] , [292, 307, 308, 376] , [143, 127,  48, 101] ,
+	[419, 422, 429, 263] , [147,  44, 107,  92] , [ 17,  86,  85,  18] , [ 78,  77,  62, 147] , [127, 210, 199, 218] ,
+	[397, 378, 401, 370] , [166,  40,  38, 168] , [245, 234, 129, 246] , [ 31, 248, 247, 162] , [ 34, 247, 248, 131] ,
+	[175, 218, 199, 237] , [418, 352, 413, 466] , [125, 114, 226,  47] , [225, 224,  53,  54] , [ 99,  65, 103, 130] ,
+	[193, 215, 208, 188] , [219,  80, 240, 238] , [134, 156, 113, 244] , [345, 361, 364, 441] , [141, 171, 150, 177] ,
+	[400, 413, 352, 420] , [119, 230, 229, 118] , [282, 276, 441, 364] , [ 71,  64,  69,  72] , [315, 314, 407, 406] ,
+	[222, 190, 194,  56] , [114, 248,  31, 226] , [106,  53,  66,  67] , [236,  60, 167, 220] , [108,  56,   9,  10] ,
+	[ 67,  66,  56, 108] , [ 69,  64, 106, 105] , [120, 119,  51, 102] , [242, 126,  45, 238] , [  6, 196,   4,  52] ,
+	[143, 130, 210, 127] , [ 34, 131,  26,   8] , [323, 271, 410, 411] , [ 33, 195, 205, 212] , [ 37, 102,  51, 206] ,
+	[195, 202,  84, 183] , [238, 240, 239, 242] , [ 26, 111, 164,   8] , [225,  54,  47, 226] , [154, 146,  24,  23] ,
+	[211, 203, 213, 215] , [246, 194, 190, 245] , [425, 336, 407, 419] , [318, 317, 404, 403] , [ 33, 212, 171, 141] ,
+	[ 12,  73,  39,  13] , [208, 217, 207, 206] , [238, 221, 116, 219] , [ 46, 221, 238,  45] , [184,  43,  75, 185] ,
+	[209, 202, 195,  33] , [269, 272, 304, 303] , [214, 148, 178, 216] , [235,  94, 138, 228] , [ 67, 108, 109,  70] ,
+	[  7, 352, 418, 169] , [193, 188, 148, 214] , [ 97,  63,  77,  78] , [125,  47,  71, 157] , [317,  16,  17, 316] ,
+	[180,  87,  88, 179] , [106,  64,  54,  53] , [119, 118, 124,  51] , [146, 145,  25,  24] , [325, 319, 320, 326] ,
+	[123, 189, 175, 197] , [293, 309, 325, 326] , [150, 171, 170, 151] , [178, 138,  94, 133] , [328, 295, 456, 461] ,
+	[361, 421, 457, 364] , [336, 274, 376, 322] , [396, 395, 431, 432] , [ 13,  39,  83,  14] , [278, 330, 350, 351] ,
+	[191,  57, 158, 174] , [117, 112,  36, 144] , [224, 223,  66,  53] , [140,  72,  22, 163] , [163, 128,  35, 140] ,
+	[366, 365, 395, 380] , [219, 116,  49, 220] , [430, 359, 372, 356] , [157, 144,  36, 125] , [377, 353, 281, 412] ,
+	[125,  36, 227, 114] , [355,  20,  95, 371] , [120, 231, 230, 119] , [249, 457, 400, 420] , [162, 161,  30,  31] ,
+	[ 46,  45,   2,   5] , [141, 172, 209,  33] , [394, 392, 328, 327] , [ 32,  26, 131, 227] , [300, 298, 339, 338] ,
+	[395, 396, 379, 380] , [102,  37, 143, 101] , [217, 213,  58, 187] , [327,   3, 165, 394] , [242, 239,  21, 243] ,
+	[186,  41,  93, 187] , [269, 303,  12,  13] , [192,  81,  43, 184] , [140,  35, 144, 157] , [223, 222,  56,  66] ,
+	[189, 115, 218, 175] , [323, 427, 424, 392] , [ 37, 204, 130, 143] , [280, 430, 421, 361] , [  2, 275, 276,   5] ,
+	[134, 244, 191, 174] , [241,  76,  60, 236] , [108,  10, 152, 109] , [ 27, 155, 154,  23] , [211, 215, 136, 170] ,
+	[355, 275,   2,  20] , [ 90,  89,  96,  97] , [321, 320, 404, 405] , [316, 315, 406, 405] , [107,  44, 203, 205] ,
+	[201, 422, 314,  19] , [153, 176, 172, 149] , [376, 274, 288, 292] , [292, 288, 411, 410] , [130, 204, 166,  99] ,
+	[115,  48, 127, 218] , [327, 328, 461, 329] , [105, 106,  67,  70] , [236,  65,  99, 241] , [200, 201, 202, 209] ,
+	[332, 295, 328, 359] , [100,  61,  76, 241] , [243, 142, 126, 242] , [329, 463, 371, 327] , [220, 167,  80, 219] ,
+	[233,  27,  23, 232] , [190, 222,  57, 191] , [223,  29,  57, 222] , [244, 113, 234, 245] , [ 32, 229, 111,  26] ,
+	[226,  31,  30, 225] , [232,  23,  24, 231] , [225,  30,  28, 224] , [114, 227, 131, 248] , [ 32, 227,  36, 112] ,
+	[234, 113,  27, 233] , [230,  25, 111, 229] , [224,  28,  29, 223] , [ 95,  20, 126, 142] , [239, 240,  80,  21] ,
+	[243,  21,  61, 100] , [157,  71,  72, 140] , [ 76,  61, 167,  60] , [189, 123, 194, 246] , [231,  24,  25, 230] ,
+	[232, 231, 120, 121] , [121, 101,  48, 122] , [208, 206,  51, 188] , [332, 280, 279, 295] , [196, 249, 420, 198] ,
+	[199, 210,  50, 132] , [177, 149, 172, 141] , [117, 124, 118, 112] , [ 28, 160, 159,  29] , [245, 190, 191, 244] ,
+	[379, 396, 370, 401] , [268, 303, 304, 270] , [351, 453, 454, 358] , [ 75,  74,  40,  41] , [169, 418, 286,   9] ,
+	[283, 444, 445, 284] , [397, 176, 153, 378] , [110,  68,  70, 109] , [301, 277, 354, 384] , [186,  62,  77, 185] ,
+	[299, 294, 301, 302] , [ 50,  49, 116, 132] , [422, 201, 200, 429] , [304, 272, 273, 305] , [271, 323, 392, 270] ,
+	[296, 443, 444, 283] , [427, 437, 428, 426] , [336, 322, 406, 407] , [ 19, 314, 315,  18] , [387, 388, 260, 258] ,
+	[255, 374, 375, 254] , [314, 422, 419, 407] , [297, 335, 334, 300] , [313, 312, 272, 269] , [ 55,  22,  72,  69] ,
+	[221,  46,  52, 135] , [391, 374, 255, 340] , [315, 316,  17,  18] , [372, 267, 331, 330] , [423, 274, 336, 425] ,
+	[ 58,  44, 147,  62] , [ 91,  78, 147,  92] , [182,  85,  86, 181] , [423, 425, 432, 431] , [357, 265, 448, 455] ,
+	[268, 270, 392, 394] , [358, 454, 465, 466] , [264, 360, 468, 467] , [264, 250, 256, 360] , [421, 430, 356, 438] ,
+	[194, 123,   7, 169] , [449, 450, 348, 347] , [277, 284, 445, 446] , [241,  99,  98, 100] , [281, 331, 267, 426] ,
+	[307, 292, 410, 409] , [260, 388, 389, 261] , [364, 457, 249, 282] , [338, 339,  11, 152] , [438, 344, 413, 400] ,
+	[349, 451, 452, 350] , [345, 279, 280, 361] , [402, 377, 434, 436] , [367, 324, 455, 448] , [182,  92, 107, 183] ,
+	[418, 414, 442, 286] , [360, 256, 262, 447] , [284, 277, 301, 294] , [291, 251, 463, 329] , [344, 358, 466, 413] ,
+	[179,  89,  90, 180] , [266, 341, 346, 373] , [429, 397, 370, 263] , [296, 283, 335, 297] , [275, 355, 462, 458] ,
+	[  4, 237, 135,  52] , [359, 424, 267, 372] , [386, 387, 258, 259] , [394, 165,   1, 268] , [207, 217, 187,  93] ,
+	[278, 356, 372, 330] , [ 44,  58, 213, 203] , [459, 460, 458, 462] , [381, 382, 257, 253] , [266, 447, 262, 341] ,
+	[399, 385, 287, 415] , [437, 433, 435, 428] , [447, 266, 354, 343] , [183, 107, 205, 195] , [ 43,  42,  74,  75] ,
+	[302, 301, 384, 369] , [425, 419, 263, 432] , [295, 279, 440, 456] , [ 49,  50, 103,  65] , [ 74,  42,  39,  73] ,
+	[433, 423, 431, 435] , [311, 273, 272, 312] , [353, 367, 448, 346] , [252, 302, 369, 390] , [209, 172, 176, 200] ,
+	[ 56, 194, 169,   9] , [377, 412, 417, 434] , [ 90,  97,  78,  91] , [330, 331, 349, 350] , [180,  90,  91, 181] ,
+	[281, 348, 349, 331] , [265, 373, 346, 448] , [324, 367, 402, 362] , [308, 326, 320, 321] , [ 16,  15,  88,  87] ,
+	[266, 373, 384, 354] , [353, 347, 348, 281] , [363, 399, 415, 464] , [318,  15,  16, 317] , [356, 278, 344, 438] ,
+	[ 96,  79,  63,  97] , [ 11, 110, 109, 152] , [398, 368, 365, 366] , [  2,  45, 126,  20] , [313, 269,  13,  14] ,
+	[237, 199, 132, 135] , [187,  58,  62, 186] , [152,  10, 337, 338] , [ 42,  82,  83,  39] , [414, 418, 466, 465] ,
+	[467, 468, 261, 389] , [  9, 286, 337,  10] , [446, 343, 354, 277] , [265, 357, 390, 369] , [436, 434, 417, 368] ,
+	[170, 136, 137, 151] , [458, 441, 276, 275] , [212, 205, 203, 211] , [347, 353, 346, 341] , [284, 294, 335, 283] ,
+	[452, 453, 351, 350] , [ 95,   3, 327, 371] , [450, 451, 349, 348] , [197,   4, 196, 198] , [254, 375, 381, 253] ,
+	[345, 441, 458, 439] , [367, 353, 377, 402] , [449, 347, 341, 262] , [360, 447, 343, 468] , [136, 139, 173, 137] ,
+	[289, 436, 368, 398] , [281, 426, 428, 412] , [288, 433, 437, 411] , [ 99, 166, 168,  98] , [142, 243, 100,  98] ,
+	[175, 237,   4, 197] , [185,  75,  41, 186] , [307, 293, 326, 308] , [396, 432, 263, 370] , [286, 442, 443, 296] ,
+	[428, 435, 417, 412] , [411, 437, 427, 323] , [421, 438, 400, 457] , [165,   3,  98, 168] , [279, 345, 439, 440] ,
+	[391, 340, 256, 250] , [306, 291, 329, 461] , [373, 265, 369, 384] , [386, 259, 287, 385] , [435, 365, 368, 417] ,
+	[251, 459, 462, 463] , [320, 319, 403, 404] , [ 17,  16,  87,  86] , [322, 321, 405, 406] , [ 85,  84,  19,  18] ,
+	[433, 288, 274, 423] , [362, 402, 436, 289] , [185,  77,  63, 184] , [293, 307, 409, 408] , [392, 424, 359, 328] ,
+	[352,   7, 198, 420] , [228, 138, 124, 117] , [393, 290, 456, 440] , [176, 397, 429, 200] , [220,  49,  65, 236] ,
+	[424, 427, 426, 267] , [332, 359, 430, 280] , [365, 435, 431, 395] , [310, 251, 291, 393] , [355, 371, 463, 462] ,
+	[ 98,   3,  95, 142] , [255, 254, 451, 450] , [415, 414, 465, 464] , [254, 253, 452, 451] , [261, 468, 343, 446] ,
+	[260, 261, 446, 445] , [258, 260, 445, 444] , [454, 342, 464, 465] , [198,   7, 123, 197] , [259, 258, 444, 443] ,
+	[287, 442, 414, 415] , [340, 449, 262, 256] , [340, 255, 450, 449] , [257, 342, 454, 453] , [ 61,  21,  80, 167] ,
+	[310, 393, 440, 439] , [338, 337, 297, 300] , [310, 460, 459, 251] , [ 51, 124, 148, 188] , [253, 257, 453, 452] ,
+	[215, 193, 139, 136] , [351, 358, 344, 278] , [113, 156, 155,  27] , [  6,  52,  46,   5] , [206, 207, 204,  37] ,
+	[249, 196,   6, 282] , [216, 178, 133,  59] , [286, 296, 297, 337] , [382, 383, 342, 257] , [287, 259, 443, 442] ,
+	[211, 170, 171, 212] , [306, 461, 456, 290] , [104, 105,  70,  68] , [271, 305, 409, 410] , [460, 310, 439, 458] ,
+	[214, 216, 139, 193] , [317, 316, 405, 404] , [181,  91,  92, 182] , [  1, 165, 168,  38] , [363, 464, 342, 383] ,
+	[210, 130, 103,  50] , [305, 273, 408, 409] , [311, 416, 408, 273] , [309, 293, 408, 416] , [184,  63,  79, 192] ,
+	[115, 129, 122,  48] , [148, 124, 138, 178] , [181,  86,  87, 180] , [290, 393, 291, 306] , [252, 285, 299, 302] ,
+    [285, 333, 334, 299]
+]

requirements.txt

@@ -0,0 +1,25 @@
+########################################################################################
+# :-D
+
+############################################
+# mediapipe up on this!
+
+opencv-python>=4.7.0.72
+mediapipe>=0.10.1
+
+#
+############################################
+
+############################################
+# zoe depth requirements
+
+#--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+torchvision>=0.11.2
+timm==0.6.11
+
+#
+############################################
+
+# EOF
+########################################################################################

utils.py

@@ -0,0 +1,128 @@
+# from https://huggingface.co/spaces/shariqfarooq/ZoeDepth/raw/main/utils.py
+
+# MIT License
+
+# Copyright (c) 2022 Intelligent Systems Lab Org
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# File author: Shariq Farooq Bhat
+
+import matplotlib
+import matplotlib.cm
+import numpy as np
+import torch
+
+def colorize(value, vmin=None, vmax=None, cmap='magma_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
+    """Converts a depth map to a color image.
+
+    Args:
+        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
+        vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
+        vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
+        cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
+        invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
+        invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
+        background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
+        gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
+        value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
+
+    Returns:
+        numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
+    """
+    if isinstance(value, torch.Tensor):
+        value = value.detach().cpu().numpy()
+
+    value = value.squeeze()
+    if invalid_mask is None:
+        invalid_mask = value == invalid_val
+    mask = np.logical_not(invalid_mask)
+
+    # normalize
+    vmin = np.percentile(value[mask],2) if vmin is None else vmin
+    vmax = np.percentile(value[mask],85) if vmax is None else vmax
+    if vmin != vmax:
+        value = (value - vmin) / (vmax - vmin)  # vmin..vmax
+    else:
+        # Avoid 0-division
+        value = value * 0.
+
+    # squeeze last dim if it exists
+    # grey out the invalid values
+
+    value[invalid_mask] = np.nan
+    cmapper = matplotlib.cm.get_cmap(cmap)
+    if value_transform:
+        value = value_transform(value)
+        # value = value / value.max()
+    value = cmapper(value, bytes=True)  # (nxmx4)
+
+    # img = value[:, :, :]
+    img = value[...]
+    img[invalid_mask] = background_color
+
+    #     return img.transpose((2, 0, 1))
+    if gamma_corrected:
+        # gamma correction
+        img = img / 255
+        img = np.power(img, 2.2)
+        img = img * 255
+        img = img.astype(np.uint8)
+    return img
+
+
+import os
+
+# bard...
+def find_most_recently_created_directory(temp_dir):
+  """Finds the most recently created directory in a directory.
+
+  Args:
+    temp_dir: The directory to search.
+
+  Returns:
+    The path to the most recently created directory.
+  """
+
+  directories = os.listdir(temp_dir)
+  most_recently_created_directory = None
+  for directory in directories:
+    path = os.path.join(temp_dir, directory)
+    st = os.stat(path)
+    if most_recently_created_directory is None or st.mtime > most_recently_created_directory.mtime:
+      most_recently_created_directory = path
+
+  if most_recently_created_directory is None:
+    most_recently_created_directory = temp_dir
+
+  return most_recently_created_directory
+
+
+#chatgpt
+def get_most_recent_subdirectory(path):
+    if not os.path.isdir(path):
+        return path
+
+    subdirectories = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
+    if not subdirectories:
+        return path
+
+    most_recent_subdirectory = max(subdirectories, key=lambda d: os.path.getctime(os.path.join(path, d)))
+    return os.path.join(path, most_recent_subdirectory)
+