diff --git a/README.md b/README.md
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-# Color-Detection-
-color detection is an excellent data analytics project and an interactive application that will accurately identify the color in an image
diff --git a/color detection.py b/color detection.py
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+# Color-Detection-
+color detection is an excellent data analytics project and an interactive application that will accurately identify the color in an image
+import cv2
+import numpy as np
+
+def get_dominant_color(image_path):
+    """Get the average RGB color of the image."""
+    image = cv2.imread(image_path)
+    image = cv2.resize(image, (300, 300))
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    pixels = image_rgb.reshape(-1, 3)
+    average_color = np.mean(pixels, axis=0)
+    return average_color
+def get_color_name(rgb):
+    """Find the closest color name based on the RGB values."""
+    color_names = {
+        "Red": [255, 0, 0],
+        "Green": [0, 255, 0],
+        "Blue": [0, 0, 255],
+        "Yellow": [255, 255, 0],
+        "Cyan": [0, 255, 255],
+        "Magenta": [255, 0, 255],
+        "Black": [0, 0, 0],
+        "White": [255, 255, 255],
+        "Gray": [169, 169, 169],
+    }
+    closest_color = None
+    min_distance = float("inf")
+    for color_name, color_rgb in color_names.items():
+        distance = np.linalg.norm(np.array(rgb) - np.array(color_rgb))
+        if distance < min_distance:
+            min_distance = distance
+            closest_color = color_name
+    return closest_color
+def main():
+    image_path = 'images/sample_image.jpg'
+    avg_color = get_dominant_color(image_path)
+    color_name = get_color_name(avg_color)
+    print(f"Average RGB Color: {avg_color}")
+    print(f"Detected Color Name: {color_name}")
+if __name__ == "__main__":
+    main()