Merge pull request #5 from TRI3D-LC/aravind-3

Aravind 3

__init__.py

@@ -1440,14 +1440,85 @@ class TRI3DFaceRecognise:
         face2 = app.get(image2)
 
         embedding1 = face1[0]['embedding']
-        embedding1 /= math.sqrt(embedding1.dot(embedding1))
         embedding2 = face2[0]['embedding']
+
+        embedding1 /= math.sqrt(embedding1.dot(embedding1))
         embedding2 /= math.sqrt(embedding2.dot(embedding2))
 
         s = embedding1.dot(embedding2)
         return ({"overlap (float)": s}, )
 
 
+class FloatToImage:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "value": ("FLOAT", {
+                    "default": 0.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.01
+                })
+            }
+        }
+
+    CATEGORY = "TRI3D"
+    RETURN_TYPES = ("IMAGE", )
+    FUNCTION = "load_image"
+
+    def load_image(self, value):
+
+        def render_float(float_input):
+            latex_expression = '$' + str(float_input) + '$'
+            import matplotlib.pyplot as plt
+
+            fig = plt.figure(
+                figsize=(10, 4))  # Dimensions of figsize are in inches
+
+            text = fig.text(
+                x=0.5,  # x-coordinate to place the text
+                y=0.5,  # y-coordinate to place the text
+                s=latex_expression,
+                horizontalalignment="center",
+                verticalalignment="center",
+                fontsize=32,
+            )
+
+            import tempfile
+            path_file_image_output = tempfile.NamedTemporaryFile(
+            ).name + '.png'
+
+            plt.savefig(path_file_image_output)
+
+            import cv2
+            image = cv2.imread(path_file_image_output, cv2.IMREAD_COLOR)
+
+            import os
+            # os.unlink(path_file_image_output)
+
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            return image
+
+        def to_torch_image(image):
+
+            import numpy as np
+            import torch
+            image = image.astype(dtype=np.float32)
+            image /= 255.0
+            image = torch.from_numpy(image)[
+                None,
+            ]
+            image = image.unsqueeze(0)
+            return image
+
+        image = render_float(float_input=value)
+        image = to_torch_image(image)
+
+        return image
+
+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {
@@ -1463,12 +1534,14 @@ NODE_CLASS_MAPPINGS = {
     "tri3d-pose-adaption": TRI3DPoseAdaption,
     "tri3d-load-pose-json": TRI3DLoadPoseJson,
     "tri3d-face-recognise": TRI3DFaceRecognise,
+    "tri3d-float-to-image": FloatToImage,
+
 }
 
 
 
 
-VERSION = "1.6.0"
+VERSION = "1.7.0"
 # A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
     "tri3d-atr-parse-batch": "ATR Parse Batch" + " v" + VERSION,
@@ -1485,4 +1558,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "tri3d-pose-adaption": "Pose Adaption" + " v" + VERSION,
     "tri3d-load-pose-json": "Load Pose Json" + " v" + VERSION,
     "tri3d-face-recognise": "Recognise face" + " v" + VERSION,
+    "tri3d-float-to-image": "Render float" + " v" + VERSION,
 }