first push

app.py

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+from nest2D import Point, Box, Item, nest, SVGWriter
+import plotly
+import numpy as np
+import plotly.graph_objects as go
+import cv2
+import gradio as gr
+
+def transform(point:list[float], x:float, y:float, rotation:float):
+    if point is None:
+        return None
+
+    point = np.array([point[0], point[1], 1])
+    matrix = np.array([[np.cos(rotation), -np.sin(rotation), x],
+                        [np.sin(rotation), np.cos(rotation), y],
+                        [0,0,1]])
+
+    return (matrix@point)[:2]
+
+class BinPacking:
+
+    def __init__(self, width:int, height:int, image:np.ndarray, imageScale:float=1) -> None:
+        self.width = width
+        self.height = height
+        self.pgrp = None
+        print(image.shape)
+        self.image = cv2.resize(image, (int(image.shape[1]*imageScale), int(image.shape[0]*imageScale)))
+        if self.image.shape[2] == 4:
+            x,y = np.where(self.image[:,:,3]==0)
+            self.image[x,y] = np.array([255,255,255,0])
+            self.imgray = cv2.cvtColor(self.image, cv2.COLOR_BGRA2GRAY)
+        else:
+            self.imgray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
+        self.imgray = cv2.bitwise_not(self.imgray)
+
+    @property
+    def box(self):    
+        return Box(self.width, self.height)
+
+    def pack(self):
+        # make margin
+        imagem = cv2.dilate(self.imgray, np.ones((3,3), np.uint8), iterations=10)
+        _, thresh = cv2.threshold(imagem, 127, 255, 0)
+
+        contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        contourList = []
+        for contour in contours:
+            contourList.extend(contour)
+            
+        hull = cv2.convexHull(np.array(contourList).reshape(-1,2))
+        hull = np.append(hull, [hull[0]], axis=0).reshape(-1,2)
+        item = Item(
+            [
+                Point(point[0], point[1])
+                for point in np.flip(hull, 0)
+            ]
+        )
+
+        max_item = int(self.width*self.height/item.area)
+        self.pgrp = nest([item,]*max_item, self.box)
+
+        return self
+
+    def visualize(self):
+        if self.pgrp == None:
+            return None
+
+        _, thresh = cv2.threshold(self.imgray, 127, 255, 0)
+        contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        transformedPoints = []
+        for item in self.pgrp[0]:
+            for contour in contours:
+                for point in contour:
+                    transformedPoints.append(transform(point[0], item.translation.x, item.translation.y, item.rotation))
+                transformedPoints.append([None,None])
+            transformedPoints.append([None,None])
+
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(
+            x=[0, self.width, self.width, 0, 0],
+            y=[0, 0, self.height, self.height, 0],
+            fill="toself",
+            mode="lines",
+            textposition="bottom right",
+        )) 
+        fig.add_trace(go.Scatter(
+            x=[point[0] for point in transformedPoints],
+            y=[point[1] for point in transformedPoints],
+            mode="lines",
+            textposition="bottom right",
+        )) 
+        fig.update_layout(
+            autosize=False,
+            width=500,
+            height=500,
+            margin=dict(
+                l=50,
+                r=50,
+                b=100,
+                t=100,
+                pad=4
+            ),
+            paper_bgcolor="LightSteelBlue",
+        )
+        return fig
+
+
+def process(width:int, height:int, scale:float, image: np.ndarray):
+
+    packer = BinPacking(width, height, image, scale)
+
+    packer.pack()
+    figure = packer.visualize()
+    return figure
+
+
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # Image fitting
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(image_mode="RGBA")
+                with gr.Row():
+                    width = gr.Number(1500,precision=0)
+                    height = gr.Number(1500,precision=0)
+                    scale = gr.Number(1.0)
+                fit = gr.Button("Submit")
+                
+            plot_output = gr.Plot()
+
+        fit.click(fn=process, inputs=[width, height, scale, image], outputs=plot_output)
+
+    demo.launch()

requirements.txt

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+plotly
+nest2D