Update Image.py

Add support for difference-aware hash(dHash) calculation. DHash value can be used to quickly determine the similarity of two pictures.

src/PIL/Image.py

@@ -2692,6 +2692,78 @@ class Image:
         if not ImageQt.qt_is_installed:
             raise ImportError("Qt bindings are not installed")
         return ImageQt.toqpixmap(self)
+    
+    def dhash(self) -> str:
+    """
+    Calculate the dHash value of the picture. DHash value can be used to quickly determine the similarity of two pictures.
+    """
+    difference = self._difference(self)
+    decimal_value = 0
+    hash_string = ""
+    for index, value in enumerate(difference):
+        if value:
+            decimal_value += value * (2 ** (index % 8))
+        if index % 8 == 7:
+            hash_string += str(hex(decimal_value)[2:].rjust(2, "0"))
+            decimal_value = 0
+    return hash_string
+
+def is_similar_to(self, image, hamming_distance: int = 3) -> bool:
+    """
+
+    :param image: PIL.Image / dHash(str)
+    :param hamming_distance:
+    :return: bool
+    """
+    assert 0 <= hamming_distance <= 64
+    dis = self.hamming_distance(image)
+    return True if dis <= hamming_distance else False
+
+def hamming_distance(self, image) -> int:
+    """
+    Calculate the Hamming distance between two pictures (based on dHash algorithm)
+    :param image: PIL.Image / dHash(str)
+    :return: hamming distance. The larger the value, the greater the difference between the two pictures;
+    otherwise, the more similar they are.
+    """
+    if isinstance(image, str):
+        return self._hamming_distance_with_hash(image)
+
+    image1_diff = self._difference(self)
+    image2_diff = self._difference(image)
+
+    return sum(image1_diff ^ image2_diff)
+
+@staticmethod
+def _difference(image):
+    """
+    *Private method*
+    Calculate difference sequence
+    :param image: PIL.Image
+    :return: bool list
+    """
+    from numpy import array
+
+    resize_width = 9
+    resize_height = 8
+    difference = []  # 64-bit boolean sequence
+
+    # convert to 9*8 grayscale image
+    data = array(image.resize((resize_width, resize_height)).convert('L'))
+    for row in range(resize_height):
+        for col in range(resize_width - 1):
+            difference.append(data[row, col] > data[row, col + 1])
+
+    return array(difference)
+
+def _hamming_distance_with_hash(self, dhash: str) -> int:
+    """
+    *Private method*
+    calculate hamming distance with perceptual hash
+    :return: hamming_distance(int)
+    """
+    difference = (int(self.dhash(), 16)) ^ (int(dhash, 16))
+    return bin(difference).count("1")
 
 
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