Update deploying_3_3.py

deploying_3_3.py

@@ -1013,68 +1013,69 @@ def mainFunctionDrawImgPdf(datadoc,dxfpath, dxfratio,pdfpath=0,pdfname=0):
         
 
         imgslice = WhiteImgFinal[y:y+h, x:x+w]
-        flippedSlice=flip(imgslice)
-        
-
-    # Convert flippedSlice to PIL for color extraction
-        flippedSlice_pil = Image.fromarray(flippedSlice)
-
-        # Define patch size for color sampling (e.g., 10x10 pixels)
-        patch_size = 60
-        patch_colors = []
-
-        # Loop through patches in the image
-        for i in range(0, flippedSlice_pil.width, patch_size):
-            for j in range(0, flippedSlice_pil.height, patch_size):
-                # Crop a patch from the original image
-                patch = flippedSlice_pil.crop((i, j, i + patch_size, j + patch_size))
-                patch_colors += patch.getcolors(patch_size * patch_size)
-
-        # Calculate the dominant color from all patches
-        max_count = 0
-        dominant_color = None
-        tolerance = 5
-        black_threshold = 30  # Max RGB value for a color to be considered "black"
-        white_threshold = 225  # Min RGB value for a color to be considered "white"
-
-        for count, color in patch_colors:
-            # Exclude colors within the black and white ranges
-            if not (all(c <= black_threshold for c in color) or all(c >= white_threshold for c in color)):
-                # Update if the current color has a higher count than previous max
-                if count > max_count:
-                    max_count = count
-                    dominant_color = color
-
-        
-
-        # Append dominant color to ColorCheck and update NewColors
-        if dominant_color is not None:
-            ColorCheck.append(dominant_color)
-
-            NewColors = None
-
-            for color in ColorCheck:
-              # Check if the current color is within the tolerance
-              print("color = ",color)
-              print("dominant_color = ",dominant_color)
-              if (abs(color[0] - dominant_color[0]) < 20 and
-                  abs(color[1] - dominant_color[1]) < 20 and
-                  abs(color[2] - dominant_color[2]) < 20):
-                  NewColors = (color[2], color[1], color[0])  # Set the new color
-                  break
-              else:
-                  # If no color in ColorCheck meets the tolerance, use the dominant color
-                  NewColors = (dominant_color[2], dominant_color[1], dominant_color[0])
-
-            if NewColors not in ColorCheck:
-              ColorCheck.append(NewColors)
-
+        if(imgslice.shape[0] != 0 and imgslice.shape[1] != 0):
+            flippedSlice=flip(imgslice)
             
-            if flagcolor == 1:
-                SimilarAreaDictionary.at[index, 'Color'] = NewColors
-                # print(f"Updated Color at index {index} with {NewColors}.")
-            elif flagcolor == 2:
-                    SimilarAreaDictionary.at[i, 'Color'] = NewColors
+    
+        # Convert flippedSlice to PIL for color extraction
+            flippedSlice_pil = Image.fromarray(flippedSlice)
+    
+            # Define patch size for color sampling (e.g., 10x10 pixels)
+            patch_size = 100
+            patch_colors = []
+    
+            # Loop through patches in the image
+            for i in range(0, flippedSlice_pil.width, patch_size):
+                for j in range(0, flippedSlice_pil.height, patch_size):
+                    # Crop a patch from the original image
+                    patch = flippedSlice_pil.crop((i, j, i + patch_size, j + patch_size))
+                    patch_colors += patch.getcolors(patch_size * patch_size)
+    
+            # Calculate the dominant color from all patches
+            max_count = 0
+            dominant_color = None
+            tolerance = 5
+            black_threshold = 30  # Max RGB value for a color to be considered "black"
+            white_threshold = 225  # Min RGB value for a color to be considered "white"
+    
+            for count, color in patch_colors:
+                # Exclude colors within the black and white ranges
+                if not (all(c <= black_threshold for c in color) or all(c >= white_threshold for c in color)):
+                    # Update if the current color has a higher count than previous max
+                    if count > max_count:
+                        max_count = count
+                        dominant_color = color
+    
+            
+    
+            # Append dominant color to ColorCheck and update NewColors
+            if dominant_color is not None:
+                ColorCheck.append(dominant_color)
+    
+                NewColors = None
+    
+                for color in ColorCheck:
+                  # Check if the current color is within the tolerance
+                  print("color = ",color)
+                  print("dominant_color = ",dominant_color)
+                  if (abs(color[0] - dominant_color[0]) < 20 and
+                      abs(color[1] - dominant_color[1]) < 20 and
+                      abs(color[2] - dominant_color[2]) < 20):
+                      NewColors = (color[2], color[1], color[0])  # Set the new color
+                      break
+                  else:
+                      # If no color in ColorCheck meets the tolerance, use the dominant color
+                      NewColors = (dominant_color[2], dominant_color[1], dominant_color[0])
+    
+                if NewColors not in ColorCheck:
+                  ColorCheck.append(NewColors)
+    
+                
+                if flagcolor == 1:
+                    SimilarAreaDictionary.at[index, 'Color'] = NewColors
+                    # print(f"Updated Color at index {index} with {NewColors}.")
+                elif flagcolor == 2:
+                        SimilarAreaDictionary.at[i, 'Color'] = NewColors
 
             
       cv2.drawContours(imgg, [np.array(cntPoints)], -1, ([NewColors[2],NewColors[1],NewColors[0]]), thickness=-1)