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plot_helpers.py
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plot_helpers.py
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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# ==============================================================================
from __future__ import print_function
from builtins import str
import sys, os, time
import numpy as np
from easydict import EasyDict
from builtins import range
import copy, textwrap
from PIL import Image, ImageFont, ImageDraw
from PIL.ExifTags import TAGS
# this is important when deploying to headless server environment (non-GUI)
###################################################
import matplotlib
# force headless backend, or set 'backend' to 'Agg'
# in your ~/.matplotlib/matplotlibrc
matplotlib.use('Agg')
import matplotlib.pyplot
# force non-interactive mode, or set 'interactive' to False
# in your ~/.matplotlib/matplotlibrc
from matplotlib.pyplot import imsave
matplotlib.pyplot.ioff()
###################################################
import cntk
from cntk import input_variable, Axis
from utils.nms.nms_wrapper import apply_nms_to_single_image_results
from cntk_helpers import regress_rois
import cv2 # pip install opencv-python
available_font = "arial.ttf"
try:
dummy = ImageFont.truetype(available_font, 16)
except:
available_font = "FreeMono.ttf"
####################################
# Visualize results
####################################
def visualizeResultsFaster(imgPath, roiLabels, roiScores, roiRelCoords, padWidth, padHeight, classes,
nmsKeepIndices = None, boDrawNegativeRois = True, decisionThreshold = 0.0):
# read and resize image
imgWidth, imgHeight = imWidthHeight(imgPath)
scale = 800.0 / max(imgWidth, imgHeight)
imgHeight = int(imgHeight * scale)
imgWidth = int(imgWidth * scale)
if imgWidth > imgHeight:
h_border = 0
v_border = int((imgWidth - imgHeight)/2)
else:
h_border = int((imgHeight - imgWidth)/2)
v_border = 0
PAD_COLOR = [103, 116, 123] # [114, 114, 114]
cv_img = cv2.imread(imgPath)
rgb_img = cv2.cvtColor(cv_img,cv2.COLOR_BGR2RGB)
resized = cv2.resize(rgb_img, (imgWidth, imgHeight), interpolation=cv2.INTER_NEAREST)
imgDebug = cv2.copyMakeBorder(resized,v_border,v_border,h_border,h_border,cv2.BORDER_CONSTANT,value=PAD_COLOR)
rect_scale = 800 / padWidth
assert(len(roiLabels) == len(roiRelCoords))
if roiScores:
assert(len(roiLabels) == len(roiScores))
minScore = min(roiScores)
print("roiScores min: {}, max: {}, threshold: {}".format(minScore, max(roiScores), decisionThreshold))
if minScore > decisionThreshold:
decisionThreshold = minScore * 0.5
print("reset decision threshold to: {}".format(decisionThreshold))
# draw multiple times to avoid occlusions
for iter in range(0,3):
for roiIndex in range(len(roiRelCoords)):
label = roiLabels[roiIndex]
if roiScores:
score = roiScores[roiIndex]
if decisionThreshold and score < decisionThreshold:
label = 0
# init drawing parameters
thickness = 1
if label == 0:
color = (255, 0, 0)
else:
color = getColorsPalette()[label]
rect = [(rect_scale * i) for i in roiRelCoords[roiIndex]]
rect[0] = int(max(0, min(padWidth, rect[0])))
rect[1] = int(max(0, min(padHeight, rect[1])))
rect[2] = int(max(0, min(padWidth, rect[2])))
rect[3] = int(max(0, min(padHeight, rect[3])))
# draw in higher iterations only the detections
if iter == 0 and boDrawNegativeRois:
drawRectangles(imgDebug, [rect], color=color, thickness=thickness)
elif iter==1 and label > 0:
if not nmsKeepIndices or (roiIndex in nmsKeepIndices):
thickness = 4
drawRectangles(imgDebug, [rect], color=color, thickness=thickness)
elif iter == 2 and label > 0:
if not nmsKeepIndices or (roiIndex in nmsKeepIndices):
font = ImageFont.truetype(available_font, 18)
text = classes[label]
if roiScores:
text += "(" + str(round(score, 2)) + ")"
imgDebug = drawText(imgDebug, (rect[0],rect[1]), text, color = (255,255,255), font = font, colorBackground=color)
return imgDebug
def load_resize_and_pad(image_path, width, height, pad_value=114):
if "@" in image_path:
print("WARNING: zipped image archives are not supported for visualizing results.")
exit(0)
img = cv2.imread(image_path)
img_width = len(img[0])
img_height = len(img)
scale_w = img_width > img_height
target_w = width
target_h = height
if scale_w:
target_h = int(np.round(img_height * float(width) / float(img_width)))
else:
target_w = int(np.round(img_width * float(height) / float(img_height)))
resized = cv2.resize(img, (target_w, target_h), 0, 0, interpolation=cv2.INTER_NEAREST)
top = int(max(0, np.round((height - target_h) / 2)))
left = int(max(0, np.round((width - target_w) / 2)))
bottom = height - top - target_h
right = width - left - target_w
resized_with_pad = cv2.copyMakeBorder(resized, top, bottom, left, right,
cv2.BORDER_CONSTANT, value=[pad_value, pad_value, pad_value])
# transpose(2,0,1) converts the image to the HWC format which CNTK accepts
model_arg_rep = np.ascontiguousarray(np.array(resized_with_pad, dtype=np.float32).transpose(2, 0, 1))
dims = (width, height, target_w, target_h, img_width, img_height)
return resized_with_pad, model_arg_rep, dims
# Tests a Faster R-CNN model and plots images with detected boxes
def eval_and_plot_faster_rcnn(eval_model, num_images_to_plot, test_map_file, img_shape,
results_base_path, feature_node_name, classes,
drawUnregressedRois=False, drawNegativeRois=False,
nmsThreshold=0.5, nmsConfThreshold=0.0, bgrPlotThreshold = 0.8):
# get image paths
with open(test_map_file) as f:
content = f.readlines()
img_base_path = os.path.dirname(os.path.abspath(test_map_file))
img_file_names = [os.path.join(img_base_path, x.split('\t')[1]) for x in content]
# prepare model
image_input = input_variable(img_shape, dynamic_axes=[Axis.default_batch_axis()], name=feature_node_name)
dims_input = input_variable((1,6), dynamic_axes=[Axis.default_batch_axis()], name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
#dims_input_const = cntk.constant([image_width, image_height, image_width, image_height, image_width, image_height], (1, 6))
print("Plotting results from Faster R-CNN model for %s images." % num_images_to_plot)
for i in range(0, num_images_to_plot):
imgPath = img_file_names[i]
# evaluate single image
_, cntk_img_input, dims = load_resize_and_pad(imgPath, img_shape[2], img_shape[1])
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1,) + dims_input.shape
output = frcn_eval.eval({frcn_eval.arguments[0]: [cntk_img_input], frcn_eval.arguments[1]: dims_input})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
if drawUnregressedRois:
# plot results without final regression
imgDebug = visualizeResultsFaster(imgPath, labels, scores, out_rpn_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=None, boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_{}".format(results_base_path, i, os.path.basename(imgPath)), imgDebug)
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels, dims)
nmsKeepIndices = apply_nms_to_single_image_results(regressed_rois, labels, scores,
nms_threshold=nmsThreshold,
conf_threshold=nmsConfThreshold)
img = visualizeResultsFaster(imgPath, labels, scores, regressed_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=nmsKeepIndices,
boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_regr_{}".format(results_base_path, i, os.path.basename(imgPath)), img)
####################################
# helper library
####################################
def imread(imgPath, boThrowErrorIfExifRotationTagSet = True):
if not os.path.exists(imgPath):
print("ERROR: image path does not exist.")
error
rotation = rotationFromExifTag(imgPath)
if boThrowErrorIfExifRotationTagSet and rotation != 0:
print ("Error: exif roation tag set, image needs to be rotated by %d degrees." % rotation)
img = cv2.imread(imgPath)
if img is None:
print ("ERROR: cannot load image " + imgPath)
error
if rotation != 0:
img = imrotate(img, -90).copy() # got this error occassionally without copy "TypeError: Layout of the output array img is incompatible with cv::Mat"
return img
def rotationFromExifTag(imgPath):
TAGSinverted = {v: k for k, v in TAGS.items()}
orientationExifId = TAGSinverted['Orientation']
try:
imageExifTags = Image.open(imgPath)._getexif()
except:
imageExifTags = None
# rotate the image if orientation exif tag is present
rotation = 0
if imageExifTags != None and orientationExifId != None and orientationExifId in imageExifTags:
orientation = imageExifTags[orientationExifId]
# print ("orientation = " + str(imageExifTags[orientationExifId]))
if orientation == 1 or orientation == 0:
rotation = 0 # no need to do anything
elif orientation == 6:
rotation = -90
elif orientation == 8:
rotation = 90
else:
print ("ERROR: orientation = " + str(orientation) + " not_supported!")
error
return rotation
def imwrite(img, imgPath):
cv2.imwrite(imgPath, img)
def imresize(img, scale, interpolation = cv2.INTER_LINEAR):
return cv2.resize(img, (0,0), fx=scale, fy=scale, interpolation=interpolation)
def imresizeMaxDim(img, maxDim, boUpscale = False, interpolation = cv2.INTER_LINEAR):
scale = 1.0 * maxDim / max(img.shape[:2])
if scale < 1 or boUpscale:
img = imresize(img, scale, interpolation)
else:
scale = 1.0
return img, scale
def imWidth(input):
return imWidthHeight(input)[0]
def imHeight(input):
return imWidthHeight(input)[1]
def imWidthHeight(input):
width, height = Image.open(input).size #this does not load the full image
return width,height
def imArrayWidth(input):
return imArrayWidthHeight(input)[0]
def imArrayHeight(input):
return imArrayWidthHeight(input)[1]
def imArrayWidthHeight(input):
width = input.shape[1]
height = input.shape[0]
return width,height
def imshow(img, waitDuration=0, maxDim = None, windowName = 'img'):
if isinstance(img, str): #test if 'img' is a string
img = cv2.imread(img)
if maxDim is not None:
scaleVal = 1.0 * maxDim / max(img.shape[:2])
if scaleVal < 1:
img = imresize(img, scaleVal)
cv2.imshow(windowName, img)
cv2.waitKey(waitDuration)
def drawRectangles(img, rects, color = (0, 255, 0), thickness = 2):
for rect in rects:
pt1 = tuple(ToIntegers(rect[0:2]))
pt2 = tuple(ToIntegers(rect[2:]))
try:
cv2.rectangle(img, pt1, pt2, color, thickness)
except:
import pdb; pdb.set_trace()
print("Unexpected error:", sys.exc_info()[0])
def drawCrossbar(img, pt):
(x,y) = pt
cv2.rectangle(img, (0, y), (x, y), (255, 255, 0), 1)
cv2.rectangle(img, (x, 0), (x, y), (255, 255, 0), 1)
cv2.rectangle(img, (img.shape[1],y), (x, y), (255, 255, 0), 1)
cv2.rectangle(img, (x, img.shape[0]), (x, y), (255, 255, 0), 1)
def ptClip(pt, maxWidth, maxHeight):
pt = list(pt)
pt[0] = max(pt[0], 0)
pt[1] = max(pt[1], 0)
pt[0] = min(pt[0], maxWidth)
pt[1] = min(pt[1], maxHeight)
return pt
def drawText(img, pt, text, textWidth=None, color = (255,255,255), colorBackground = None, font = ImageFont.truetype(available_font, 16)):
pilImg = imconvertCv2Pil(img)
pilImg = pilDrawText(pilImg, pt, text, textWidth, color, colorBackground, font)
return imconvertPil2Cv(pilImg)
def pilDrawText(pilImg, pt, text, textWidth=None, color = (255,255,255), colorBackground = None, font = ImageFont.truetype(available_font, 16)):
textY = pt[1]
draw = ImageDraw.Draw(pilImg)
if textWidth == None:
lines = [text]
else:
lines = textwrap.wrap(text, width=textWidth)
for line in lines:
width, height = font.getsize(line)
if colorBackground != None:
draw.rectangle((pt[0], pt[1], pt[0] + width, pt[1] + height), fill=tuple(colorBackground[::-1]))
draw.text(pt, line, fill = tuple(color), font = font)
textY += height
return pilImg
def getColorsPalette():
colors = [[255,0,0], [0,255,0], [0,0,255], [255,255,0], [255,0,255]]
for i in range(5):
for dim in range(0,3):
for s in (0.25, 0.5, 0.75):
if colors[i][dim] != 0:
newColor = copy.deepcopy(colors[i])
newColor[dim] = int(round(newColor[dim] * s))
colors.append(newColor)
return colors
def imconvertPil2Cv(pilImg):
rgb = pilImg.convert('RGB')
return np.array(rgb).copy()[:, :, ::-1]
def imconvertCv2Pil(img):
cv2_im = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
return Image.fromarray(cv2_im)
def ToIntegers(list1D):
return [int(float(x)) for x in list1D]
def getDictionary(keys, values, boConvertValueToInt = True):
dictionary = {}
for key,value in zip(keys, values):
if (boConvertValueToInt):
value = int(value)
dictionary[key] = value
return dictionary
class Bbox:
MAX_VALID_DIM = 100000
left = top = right = bottom = None
def __init__(self, left, top, right, bottom):
self.left = int(round(float(left)))
self.top = int(round(float(top)))
self.right = int(round(float(right)))
self.bottom = int(round(float(bottom)))
self.standardize()
def __str__(self):
return ("Bbox object: left = {0}, top = {1}, right = {2}, bottom = {3}".format(self.left, self.top, self.right, self.bottom))
def __repr__(self):
return str(self)
def rect(self):
return [self.left, self.top, self.right, self.bottom]
def max(self):
return max([self.left, self.top, self.right, self.bottom])
def min(self):
return min([self.left, self.top, self.right, self.bottom])
def width(self):
width = self.right - self.left + 1
assert(width>=0)
return width
def height(self):
height = self.bottom - self.top + 1
assert(height>=0)
return height
def surfaceArea(self):
return self.width() * self.height()