Delete tools/telemetry.

tools/telemetry/telemetry/internal/image_processing/image_util_numpy_impl.py

Index: tools/telemetry/telemetry/internal/image_processing/image_util_numpy_impl.py
diff --git a/tools/telemetry/telemetry/internal/image_processing/image_util_numpy_impl.py b/tools/telemetry/telemetry/internal/image_processing/image_util_numpy_impl.py
deleted file mode 100644
index d0da21d22a54df25cb5da4f8c7059bc869591915..0000000000000000000000000000000000000000
--- a/tools/telemetry/telemetry/internal/image_processing/image_util_numpy_impl.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# 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.internal.util import external_modules
-from telemetry.util import color_histogram
-from telemetry.util import rgba_color
-import png
-
-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):
-  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)
-    if len(filtered[:, 0]) == 0:
-      return color_histogram.ColorHistogram(np.zeros((256)), np.zeros((256)),
-                                      np.zeros((256)), ignore_color)
-    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 color_histogram.ColorHistogram(hist_r, hist_g, hist_b, ignore_color)