[Telemetry] Migrate bitmap.py from bitmaptools.cc to numpy

tools/telemetry/telemetry/image_processing/image_util_numpy_impl.py

Index: tools/telemetry/telemetry/image_processing/image_util_numpy_impl.py
diff --git a/tools/telemetry/telemetry/image_processing/image_util_numpy_impl.py b/tools/telemetry/telemetry/image_processing/image_util_numpy_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bb50fe9e7c1edd146f507682c006a8d9492387d
--- /dev/null
+++ b/tools/telemetry/telemetry/image_processing/image_util_numpy_impl.py
@@ -0,0 +1,183 @@
+# Copyright 2014 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+from __future__ import division
+
+from telemetry.core import util
+from telemetry.image_processing import histogram
+from telemetry.image_processing import rgba_color
+from telemetry.util import external_modules
+
+util.AddDirToPythonPath(util.GetTelemetryDir(), 'third_party', 'png')
+import png  # pylint: disable=F0401
+
+cv2 = external_modules.ImportOptionalModule('cv2')
+np = external_modules.ImportRequiredModule('numpy')
+
+
+def Channels(image):
+  return image.shape[2]
+
+def Width(image):
+  return image.shape[1]
+
+def Height(image):
+  return image.shape[0]
+
+def Pixels(image):
+  return bytearray(np.uint8(image[:, :, ::-1]).flat)  # Convert from bgr to rgb.
+
+def GetPixelColor(image, x, y):
+  bgr = image[y][x]
+  return rgba_color.RgbaColor(bgr[2], bgr[1], bgr[0])
+
+def WritePngFile(image, path):
+  assert(path.endswith('png'))
+  if cv2 is not None:
+    cv2.imwrite(path, image)
+  else:
+    with open(path, "wb") as f:
+      metadata = {}
+      metadata['size'] = (Width(image), Height(image))
+      metadata['alpha'] = False
+      metadata['bitdepth'] = 8
+      img = image[:, :, ::-1]
+      pixels = img.reshape(-1).tolist()
+      png.Writer(**metadata).write_array(f, pixels)
+
+def FromRGBPixels(width, height, pixels, bpp):
+  img = np.array(pixels, order='F', dtype=np.uint8)
+  img.resize((height, width, bpp))
+  if bpp == 4:
+    img = img[:, :, :3]  # Drop alpha.
+  return img[:, :, ::-1]  # Convert from rgb to bgr.
+
+def FromPngFile(path):
+  if cv2 is not None:
+    img = cv2.imread(path, cv2.CV_LOAD_IMAGE_COLOR)
+    if img is None:
+      raise ValueError('Image at path {0} could not be read'.format(path))
+    return img
+  else:
+    with open(path, "rb") as f:
+      return FromPng(f.read())
+
+def FromPng(png_data):
+  if cv2 is not None:
+    file_bytes = np.asarray(bytearray(png_data), dtype=np.uint8)
+    return cv2.imdecode(file_bytes, cv2.CV_LOAD_IMAGE_COLOR)
+  else:
+    width, height, pixels, meta = png.Reader(bytes=png_data).read_flat()
+    return FromRGBPixels(width, height, pixels, 4 if meta['alpha'] else 3)
+
+def _SimpleDiff(image1, image2):
+  if cv2 is not None:
+    return cv2.absdiff(image1, image2)
+  else:
+    amax = np.maximum(image1, image2)
+    amin = np.minimum(image1, image2)
+    return amax - amin
+
+def AreEqual(image1, image2, tolerance, likely_equal):
+  if image1.shape != image2.shape:
+    return False
+  self_image = image1
+  other_image = image2
+  if tolerance:
+    if likely_equal:
+      return np.amax(_SimpleDiff(image1, image2)) <= tolerance
+    else:
+      for row in xrange(Height(image1)):
+        if np.amax(_SimpleDiff(image1[row], image2[row])) > tolerance:
+          return False
+      return True
+  else:
+    if likely_equal:
+      return (self_image == other_image).all()
+    else:
+      for row in xrange(Height(image1)):
+        if not (self_image[row] == other_image[row]).all():
+          return False
+      return True
+
+def Diff(image1, image2):
+  self_image = image1
+  other_image = image2
+  if image1.shape[2] != image2.shape[2]:
+    raise ValueError('Cannot diff images of differing bit depth')
+  if image1.shape[:2] != image2.shape[:2]:
+    width = max(Width(image1), Width(image2))
+    height = max(Height(image1), Height(image2))
+    self_image = np.zeros((width, height, image1.shape[2]), np.uint8)
+    other_image = np.zeros((width, height, image1.shape[2]), np.uint8)
+    self_image[0:Height(image1), 0:Width(image1)] = image1
+    other_image[0:Height(image2), 0:Width(image2)] = image2
+  return _SimpleDiff(self_image, other_image)
+
+def GetBoundingBox(image, color, tolerance):
+  if cv2 is not None:
+    color = np.array([color.b, color.g, color.r])
+    img = cv2.inRange(image, np.subtract(color[0:3], tolerance),
+                      np.add(color[0:3], tolerance))
+    count = cv2.countNonZero(img)
+    if count == 0:
+      return None, 0
+    contours, _ = cv2.findContours(img, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
+    contour = np.concatenate(contours)
+    return cv2.boundingRect(contour), count
+  else:
+    if tolerance:
+      color = np.array([color.b, color.g, color.r])
+      colorm = color - tolerance
+      colorp = color + tolerance
+      b = image[:, :, 0]
+      g = image[:, :, 1]
+      r = image[:, :, 2]
+      w = np.where(((b >= colorm[0]) & (b <= colorp[0]) &
+                    (g >= colorm[1]) & (g <= colorp[1]) &
+                    (r >= colorm[2]) & (r <= colorp[2])))
+    else:
+      w = np.where((image[:, :, 0] == color.b) &
+                   (image[:, :, 1] == color.g) &
+                   (image[:, :, 2] == color.r))
+    if len(w[0]) == 0:
+      return None, 0
+    return (w[1][0], w[0][0], w[1][-1] - w[1][0] + 1, w[0][-1] - w[0][0] + 1), \
+        len(w[0])
+
+def Crop(image, left, top, width, height):
+  img_height, img_width = image.shape[:2]
+  if (left < 0 or top < 0 or
+      (left + width) > img_width or
+      (top + height) > img_height):
+    raise ValueError('Invalid dimensions')
+  return image[top:top + height, left:left + width]
+
+def GetColorHistogram(image, ignore_color, tolerance):
+  if cv2 is not None:
+    mask = None
+    if ignore_color is not None:
+      color = np.array([ignore_color.b, ignore_color.g, ignore_color.r])
+      mask = ~cv2.inRange(image, np.subtract(color, tolerance),
+                          np.add(color, tolerance))
+
+    flatten = np.ndarray.flatten
+    hist_b = flatten(cv2.calcHist([image], [0], mask, [256], [0, 256]))
+    hist_g = flatten(cv2.calcHist([image], [1], mask, [256], [0, 256]))
+    hist_r = flatten(cv2.calcHist([image], [2], mask, [256], [0, 256]))
+  else:
+    filtered = image.reshape(-1, 3)
+    if ignore_color is not None:
+      color = np.array([ignore_color.b, ignore_color.g, ignore_color.r])
+      colorm = np.array(color) - tolerance
+      colorp = np.array(color) + tolerance
+      in_range = ((filtered[:, 0] < colorm[0]) | (filtered[:, 0] > colorp[0]) |
+                  (filtered[:, 1] < colorm[1]) | (filtered[:, 1] > colorp[1]) |
+                  (filtered[:, 2] < colorm[2]) | (filtered[:, 2] > colorp[2]))
+      filtered = np.compress(in_range, filtered, axis = 0)
+    hist_b = np.bincount(filtered[:, 0], minlength=256)
+    hist_g = np.bincount(filtered[:, 1], minlength=256)
+    hist_r = np.bincount(filtered[:, 2], minlength=256)
+
+  return histogram.ColorHistogram(hist_r, hist_g, hist_b, ignore_color)