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mask_rcnn_tool.py
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mask_rcnn_tool.py
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"""
Mask R-CNN
Train on the toy My dataset and implement color splash effect.
Copyright (c) 2018 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
------------------------------------------------------------
Usage: import the module (see Jupyter notebooks for examples), or run from
the command line as such:
# Train a new model starting from pre-trained COCO weights
python3 my.py train --dataset=/path/to/my/dataset --weights=coco
# Resume training a model that you had trained earlier
python3 my.py train --dataset=/path/to/my/dataset --weights=last
# Train a new model starting from ImageNet weights
python3 my.py train --dataset=/path/to/my/dataset --weights=imagenet
# Apply color splash to an image
python3 my.py splash --weights=/path/to/weights/file.h5 --image=<URL or path to file>
# Apply color splash to video using the last weights you trained
python3 my.py splash --weights=last --video=<URL or path to file>
"""
import os
import sys
import json, math
import datetime
from datetime import datetime
import numpy as np
import skimage.draw
import time
from skimage.measure import find_contours
from matplotlib import patches, lines
from matplotlib.patches import Polygon
import matplotlib.pyplot as plt
import colorsys
import random
# Root directory of the project
# ROOT_DIR = os.path.abspath(".\\")
# Import Mask RCNN
# sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils
from mrcnn import visualize
# Path to trained weights file
COCO_WEIGHTS_PATH = "D:\\Projects\\Mask_RCNN\\mask_rcnn_coco.h5"
# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = "logs"
############################################################
# Configurations
############################################################
class MyConfig(Config):
"""Configuration for training on the toy dataset.
Derives from the base Config class and overrides some values.
"""
# Give the configuration a recognizable name
NAME = "mask"
# We use a GPU with 12GB memory, which can fit two images.
# Adjust down if you use a smaller GPU.
IMAGES_PER_GPU = 1
# Number of classes (including background)
NUM_CLASSES = 1 + 6 # Background + my
# Number of training steps per epoch
STEPS_PER_EPOCH = 1000
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.5
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MIN_DIM = 640
IMAGE_MAX_DIM = 1280
IMAGE_MIN_SCALE = 0
BACKBONE = "resnet50"
############################################################
# Dataset
############################################################
class MyDataset(utils.Dataset):
def print_size(self, poly):
for p in poly:
a = np.array(p['all_points_y'])
height = a.max() - a.min()
a = np.array(p['all_points_x'])
width = a.max() - a.min()
self.areas.append(height * width)
#if height * width < 4096:
# print(width, height)
def load_my(self, dataset_dir, subset, class_dict):
"""Load a subset of the My dataset.
dataset_dir: Root directory of the dataset.
subset: Subset to load: train or val
"""
self.areas = []
# Add classes. We have only one class to add.
for (k, v) in class_dict.items():
self.add_class("my", v, k)
# Train or validation dataset?
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
# Load annotations
# VGG Image Annotator (up to version 1.6) saves each image in the form:
# { 'filename': '28503151_5b5b7ec140_b.jpg',
# 'regions': {
# '0': {
# 'region_attributes': {},
# 'shape_attributes': {
# 'all_points_x': [...],
# 'all_points_y': [...],
# 'name': 'polygon'}},
# ... more regions ...
# },
# 'size': 100202
# }
# We mostly care about the x and y coordinates of each region
# Note: In VIA 2.0, regions was changed from a dict to a list.
annotations = json.load(open(os.path.join(dataset_dir, "via_region_data.json"),encoding='UTF-8'))
annotations = list(annotations.values()) # don't need the dict keys
# The VIA tool saves images in the JSON even if they don't have any
# annotations. Skip unannotated images.
annotations = [a for a in annotations if a['regions']]
print(class_dict)
# Add images
for a in annotations:
# Get the x, y coordinaets of points of the polygons that make up
# the outline of each object instance. These are stores in the
# shape_attributes (see json format above)
# The if condition is needed to support VIA versions 1.x and 2.x.
# print(a['regions'])
print(a['filename'])
if type(a['regions']) is dict:
polygons = [r['shape_attributes'] for r in a['regions'].values()]
class_ids = [class_dict[r['region_attributes']['type']] for r in a['regions'].values()]
else:
polygons = [r['shape_attributes'] for r in a['regions']]
class_ids = [class_dict[r['region_attributes']['type']] for r in a['regions']]
self.print_size(polygons)
# print(class_ids)
# load_mask() needs the image size to convert polygons to masks.
# Unfortunately, VIA doesn't include it in JSON, so we must read
# the image. This is only managable since the dataset is tiny.
image_path = os.path.join(dataset_dir, a['filename'])
image = skimage.io.imread(image_path)
height, width = image.shape[:2]
self.add_image(
"my",
image_id=a['filename'], # use file name as a unique image id
path=image_path,
width=width, height=height,
polygons=polygons,
class_ids=class_ids)
self.areas.sort()
#print(np.unique(np.round(np.sqrt(self.areas))))
def load_mask(self, image_id):
"""Generate instance masks for an image.
Returns:
masks: A bool array of shape [height, width, instance count] with
one mask per instance.
class_ids: a 1D array of class IDs of the instance masks.
"""
# If not a my dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "my":
return super(self.__class__, self).load_mask(image_id)
# Convert polygons to a bitmap mask of shape
# [height, width, instance_count]
info = self.image_info[image_id]
mask = np.zeros([info["height"], info["width"], len(info["polygons"])],
dtype=np.uint8)
for i, p in enumerate(info["polygons"]):
# Get indexes of pixels inside the polygon and set them to 1
rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])
mask[rr, cc, i] = 1
class_ids = np.array(info['class_ids'])
# Return mask, and array of class IDs of each instance. Since we have
# one class ID only, we return an array of 1s
return mask.astype(np.bool), class_ids.astype(np.int32)
def image_reference(self, image_id):
"""Return the path of the image."""
info = self.image_info[image_id]
if info["source"] == "my":
return info["path"]
else:
super(self.__class__, self).image_reference(image_id)
def train(model):
"""Train the model."""
class_dict = {}
if args.label:
label_file = open(args.label)
label_lines = label_file.readlines()
label_id = 1
for label_line in label_lines:
label_line = label_line.replace('\n', '')
class_dict[label_line] = label_id
label_id = label_id + 1
# Training dataset.
dataset_train = MyDataset()
dataset_train.load_my(args.dataset, "train", class_dict)
dataset_train.prepare()
# Validation dataset
dataset_val = MyDataset()
dataset_val.load_my(args.dataset, "val", class_dict)
dataset_val.prepare()
# *** This training schedule is an example. Update to your needs ***
# Since we're using a very small dataset, and starting from
# COCO trained weights, we don't need to train too long. Also,
# no need to train all layers, just the heads should do it.
print("Training network heads")
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=1500,
layers='all')
def display_differences(image,
gt_box, gt_class_id, gt_mask,
pred_box, pred_class_id, pred_score, pred_mask,
class_names, title="", ax=None,
show_mask=True, show_box=True,
iou_threshold=0.5, score_threshold=0.5):
"""Display ground truth and prediction instances on the same image."""
# Match predictions to ground truth
gt_match, pred_match, overlaps = utils.compute_matches(
gt_box, gt_class_id, gt_mask,
pred_box, pred_class_id, pred_score, pred_mask,
iou_threshold=iou_threshold, score_threshold=score_threshold)
# # Ground truth = green. Predictions = red
# colors = [(0, 1, 0, .8)] * len(gt_match)\
# + [(1, 0, 0, 1)] * len(pred_match)
# # Concatenate GT and predictions
# class_ids = np.concatenate([gt_class_id, pred_class_id])
# scores = np.concatenate([np.zeros([len(gt_match)]), pred_score])
# boxes = np.concatenate([gt_box, pred_box])
# masks = np.concatenate([gt_mask, pred_mask], axis=-1)
# # Captions per instance show score/IoU
# captions = ["" for m in gt_match] + ["{:.2f} / {:.2f}".format(
# pred_score[i],
# (overlaps[i, int(pred_match[i])]
# if pred_match[i] > -1 else overlaps[i].max()))
# for i in range(len(pred_match))]
# # Set title if not provided
# title = title or "Ground Truth and Detections\n GT=green, pred=red, captions: score/IoU"
# # Display
# display_instances(
# image,
# boxes, masks, class_ids,
# class_names, scores, ax=ax,
# show_bbox=show_box, show_mask=show_mask,
# colors=colors, captions=captions,
# title=title)
return gt_match, pred_match, overlaps
def toSquareBox(bbox):
"""bbox:[y1, x1, y2, x2]
将它按照宽高比转换为正方形
并调整左上和右下的坐标
正方形的坐标 [y1, x1, y2, x2]
"""
box_height = bbox[2] - bbox[0]
box_width = bbox[3] - bbox[1]
wh_ratio = box_width / box_height
box_size = box_width / math.sqrt(wh_ratio)
y1 = int(bbox[0] + box_height / 2 - box_size / 2)
y2 = int(y1 + box_size)
x1 = int(bbox[1] + box_width / 2 - box_size / 2)
x2 = int(x1 + box_size)
return wh_ratio, box_size, box_height * box_width, [y1, x1, y2, x2]
def recall(model, class_names):
class_dict = {}
label_dict = ['background']
if args.label:
label_file = open(args.label)
label_lines = label_file.readlines()
label_id = 1
for label_line in label_lines:
label_line = label_line.replace('\n', '')
class_dict[label_line] = label_id
label_dict.append(label_line)
label_id = label_id + 1
# Validation dataset
dataset_val = MyDataset()
dataset_val.load_my(args.dataset, "val", class_dict)
dataset_val.prepare()
pre_correct_dict = {}
pre_total_dict = {}
pre_iou_dict = {}
pre_scores_dict = {}
gt_total_dict = {}
for i in range(1, len(class_dict) + 1):
pre_correct_dict[i] = 0
pre_total_dict[i] = 0
pre_iou_dict[i] = 0.0
pre_scores_dict[i] = 0.0
gt_total_dict[i] = 0
backbone_shapes = modellib.compute_backbone_shapes(config, [768,1280,3])
anchor_boxes = utils.generate_pyramid_anchors(
config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
backbone_shapes,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)
#utils.generate_anchors(300, config.RPN_ANCHOR_RATIOS, [40,40], 32, config.RPN_ANCHOR_STRIDE)
#print(anchor_boxes)
rois = []
obj_groups = []
# {image_file, [gt_class_id], [gt_box, (y1,x1,y2,x2)], [gt_bbox_area], [gt_wh_ratio], [gt_mask_area], [gt_mask_ratio], [gt_size], }
for image_id in dataset_val.image_ids:
print(dataset_val.image_reference(image_id))
image, image_meta, gt_class_id, gt_box, gt_mask = modellib.load_image_gt(dataset_val, config, image_id, use_mini_mask=False)
#print(image.shape)
gt_detects = {}
gt_detects['image'] = dataset_val.image_reference(image_id)
gt_detects['gt_class_id'] = gt_class_id
gt_detects['gt_bbox'] = gt_box
gt_detects['gt_bbox_area'] = []
gt_detects['gt_wh_ratio'] = []
gt_detects['gt_mask_area'] = []
gt_detects['gt_mask_ratio'] = []
gt_detects['gt_size'] = []
for i in range(0, len(gt_class_id)):
gt_total_dict[gt_class_id[i]] = gt_total_dict[gt_class_id[i]] + 1
wh_ratio, box_size, box_area, square_box = toSquareBox(gt_box[i])
mask_area = np.sum(gt_mask[:,:,i]==True)
mask_ratio = mask_area / box_area
gt_detects['gt_bbox_area'].append(box_area)
gt_detects['gt_wh_ratio'].append(wh_ratio)
gt_detects['gt_mask_area'].append(mask_area)
gt_detects['gt_mask_ratio'].append(mask_ratio)
gt_detects['gt_size'].append(box_size)
molded_image = modellib.mold_image(image, config)
#print(molded_image.shape)
# Anchors
"""
anchors = model.get_anchors(molded_image.shape)
# Duplicate across the batch dimension because Keras requires it
# TODO: can this be optimized to avoid duplicating the anchors?
anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)
print(anchors)
# Run object detection
detections, mrcnn_class, mrcnn_bbox, mrcnn_mask, rpn_rois, rpn_class, rpn_bbox =\
model.keras_model.predict([np.expand_dims(molded_image, 0), np.expand_dims(image_meta, 0), anchors], verbose=0)
print(detections[0])
print(mrcnn_class[0])
print(rpn_class[0])
"""
#skimage.io.imsave("test.jpg", image)
start_time = time.time()
results = model.detect_molded(np.expand_dims(molded_image, 0), np.expand_dims(image_meta, 0), verbose=0)
end_time = time.time()
#print("Time: %s" % str(end_time - start_time))
#print(results)
r = results[0]
pre_class_ids = r['class_ids']
for i in range(0, len(pre_class_ids)):
pre_total_dict[pre_class_ids[i]] = pre_total_dict[pre_class_ids[i]] + 1
pre_scores = r['scores']
#print(r['rois'])
for roi in r['rois']:
whr, bsize, _, _ = toSquareBox(roi)
rois.append([bsize, whr])
#print(gt_detects['gt_size'])
#overlaps = utils.compute_iou(roi, gt_detects['gt_bbox'], roi_area, gt_detects['gt_bbox_area'])
#print(overlaps)
gt_match, pred_match, overlap = display_differences(image,
gt_box, gt_class_id, gt_mask,
r['rois'], pre_class_ids, pre_scores, r['masks'],
class_names, title="", ax=None,
show_mask=True, show_box=True,
iou_threshold=0.1, score_threshold=0.1)
gt_detects['rois'] = r['rois']
gt_detects['gt_match'] = gt_match
gt_detects['pred_match'] = pred_match
#print(gt_match)
"""
visualize.display_differences(image,
gt_box, gt_class_id, gt_mask,
r['rois'], pre_class_ids, pre_scores, r['masks'],
class_names, title="", ax=None,
show_mask=True, show_box=True,
iou_threshold=0.1, score_threshold=0.1)
"""
for i in range(0, len(pred_match)):
if pred_match[i] > -1.0:
#print(r['rois'][i])
pre_correct_dict[pre_class_ids[i]] = pre_correct_dict[pre_class_ids[i]] + 1
pre_iou_dict[pre_class_ids[i]] = pre_iou_dict[pre_class_ids[i]] + overlap[i, int(pred_match[i])]
pre_scores_dict[pre_class_ids[i]] = pre_scores_dict[pre_class_ids[i]] + pre_scores[i]
obj_groups.append(gt_detects)
#print(rois)
print("图片,类别,标注框,标注宽高比,标注尺寸,检测框,检测宽高比,检测尺寸,最大IOU")
for det in obj_groups:
for i in range(0, len(det['gt_class_id'])):
overlaped = utils.compute_overlaps(anchor_boxes, np.reshape(det['gt_bbox'][i],(1,4)))
omax = max(overlaped)
#if det['gt_size'][i] > 150 and det['gt_size'][i] < 367:
if omax[0] > 0.0:
print(det['image'], end='')
print(",", label_dict[det['gt_class_id'][i]], ",", det['gt_bbox'][i], ",", det['gt_wh_ratio'][i], ",", det['gt_size'][i], end="")
if det['gt_match'][i] > -1.0:
idx = int(det['gt_match'][i])
#print(idx, det['rois'])
whr, bsize, _, _ = toSquareBox(det['rois'][idx])
print(",", det['rois'][idx], ",", whr, ",", bsize, ",", omax[0])
else:
print(",", 0, ",", 0, ",", 0, ",", omax[0])
tol_pre_correct_dict = 0
tol_pre_total_dict = 0
tol_pre_iou_dict = 0
tol_pre_scores_dict = 0
tol_gt_total_dict = 0
lines = []
tile_line = 'Type,Number,Correct,Proposals,Total,Rps/img,Avg IOU,Avg score,Recall,Precision\n'
lines.append(tile_line)
for key in class_dict:
tol_pre_correct_dict = tol_pre_correct_dict + pre_correct_dict[class_dict[key]]
tol_pre_total_dict = pre_total_dict[class_dict[key]] + tol_pre_total_dict
tol_pre_iou_dict = pre_iou_dict[class_dict[key]] + tol_pre_iou_dict
tol_pre_scores_dict = pre_scores_dict[class_dict[key]] + tol_pre_scores_dict
tol_gt_total_dict = gt_total_dict[class_dict[key]] + tol_gt_total_dict
type_rps_img = pre_total_dict[class_dict[key]] / len(dataset_val.image_ids)
if pre_correct_dict[class_dict[key]] > 0:
type_avg_iou = pre_iou_dict[class_dict[key]] / pre_correct_dict[class_dict[key]]
type_avg_score = pre_scores_dict[class_dict[key]] / pre_correct_dict[class_dict[key]]
else:
type_avg_iou = 0
type_avg_score = 0
if gt_total_dict[class_dict[key]] > 0:
type_recall = pre_total_dict[class_dict[key]] / gt_total_dict[class_dict[key]]
else:
type_recall = 0
if pre_total_dict[class_dict[key]] > 0:
type_precision = pre_correct_dict[class_dict[key]] / pre_total_dict[class_dict[key]]
else:
type_precision = 0
line = '{:s},{:d},{:d},{:d},{:d},{:.2f},{:.2f}%,{:.2f},{:.2f}%,{:.2f}%\n'.format(key, len(dataset_val.image_ids), pre_correct_dict[class_dict[key]], pre_total_dict[class_dict[key]], gt_total_dict[class_dict[key]], type_rps_img, type_avg_iou * 100, type_avg_score, type_recall * 100, type_precision * 100)
lines.append(line)
print(line)
tol_rps_img = tol_pre_total_dict / len(dataset_val.image_ids)
if tol_pre_correct_dict > 0:
tol_avg_iou = tol_pre_iou_dict / tol_pre_correct_dict
tol_avg_score = tol_pre_scores_dict / tol_pre_correct_dict
else:
tol_avg_iou = 0
tol_avg_score = 0
if tol_gt_total_dict > 0:
tol_recall = tol_pre_total_dict / tol_gt_total_dict
else:
tol_recall = 0
if tol_pre_total_dict > 0:
tol_precision = tol_pre_correct_dict / tol_pre_total_dict
else:
tol_precision = 0
totle_line = '{:s},{:d},{:d},{:d},{:d},{:.2f},{:.2f}%,{:.2f},{:.2f}%,{:.2f}%\n'.format('Total', len(dataset_val.image_ids), tol_pre_correct_dict, tol_pre_total_dict, tol_gt_total_dict, type_rps_img, tol_avg_iou * 100, tol_avg_score, tol_recall * 100, tol_precision * 100)
print(totle_line)
lines.append(totle_line)
result_file_name = "result_{:%Y%m%dT%H%M%S}.csv".format(datetime.now())
result_file = open(result_file_name, 'w+')
result_file.writelines(lines)
result_file.close()
# *** This training schedule is an example. Update to your needs ***
# Since we're using a very small dataset, and starting from
# COCO trained weights, we don't need to train too long. Also,
# no need to train all layers, just the heads should do it.
def color_splash(image, mask):
"""Apply color splash effect.
image: RGB image [height, width, 3]
mask: instance segmentation mask [height, width, instance count]
Returns result image.
"""
# Make a grayscale copy of the image. The grayscale copy still
# has 3 RGB channels, though.
gray = skimage.color.gray2rgb(skimage.color.rgb2gray(image)) * 255
# Copy color pixels from the original color image where mask is set
if mask.shape[-1] > 0:
# We're treating all instances as one, so collapse the mask into one layer
mask = (np.sum(mask, -1, keepdims=True) >= 1)
splash = np.where(mask, image, gray).astype(np.uint8)
else:
splash = gray.astype(np.uint8)
return splash
def detect_and_color_splash(model, class_names, result_image_path, image_path):
assert image_path
# Image or video?
if image_path:
# Run model detection and generate the color splash effect
print("Running on {}".format(image_path))
# Read image
image = skimage.io.imread(image_path)
# Detect objects
r = model.detect([image], verbose=0)[0]
print(r)
# Color splash
# splash = color_splash(image, r['masks'])
# class_names = ['BG', 'car']
# masked_image = visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
# class_names, r['scores'])
# visualize.display_differences(image, r['rois'], r['masks'], r['class_ids'],
# class_names, r['scores'])
# Save output
# file_name = "splash_{:%Y%m%dT%H%M%S}.png".format(datetime.datetime.now())
# skimage.io.imsave(result_image_path, masked_image)
print("Saved to ", result_image_path)
# elif video_path:
# import cv2
# # Video capture
# vcapture = cv2.VideoCapture(video_path)
# width = int(vcapture.get(cv2.CAP_PROP_FRAME_WIDTH))
# height = int(vcapture.get(cv2.CAP_PROP_FRAME_HEIGHT))
# fps = vcapture.get(cv2.CAP_PROP_FPS)
# # Define codec and create video writer
# file_name = "splash_{:%Y%m%dT%H%M%S}.avi".format(datetime.datetime.now())
# vwriter = cv2.VideoWriter(file_name,
# cv2.VideoWriter_fourcc(*'MJPG'),
# fps, (width, height))
# count = 0
# success = True
# while success:
# print("frame: ", count)
# # Read next image
# success, image = vcapture.read()
# if success:
# # OpenCV returns images as BGR, convert to RGB
# image = image[..., ::-1]
# # Detect objects
# r = model.detect([image], verbose=0)[0]
# # Color splash
# splash = color_splash(image, r['masks'])
# # RGB -> BGR to save image to video
# splash = splash[..., ::-1]
# # Add image to video writer
# vwriter.write(splash)
# count += 1
# vwriter.release()
def apply_mask(image, mask, color, alpha=0.5):
"""Apply the given mask to the image.
"""
for c in range(3):
image[:, :, c] = np.where(mask == True,
image[:, :, c] *
(1 - alpha) + alpha * color[c] * 255,
image[:, :, c])
return image
def random_colors(N, bright=True):
"""
Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
def display_instances(image, boxes, masks, class_ids, class_names, result_path,
scores=None, title="",
figsize=(16, 16), ax=None,
show_mask=True, show_bbox=False,
colors=None, captions=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
# If no axis is passed, create one and automatically call show()
auto_show = False
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# _, ax = plt.subplots(1)
auto_show = True
# Generate random colors
colors = colors or random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
if not captions:
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
caption = "{} {:.3f}".format(label, score) if score else label
else:
caption = captions[i]
#ax.text(x1, y1 + 8, caption,
# color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
if show_mask: # and (class_names[class_ids[i]] == 'fish_head'):
print(class_names[class_ids[i]])
maskroll = np.roll(mask, 1, axis=1)
m = np.logical_xor(mask, maskroll)
masked_image = apply_mask(masked_image, m, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
# TODO 这儿把鱼身鱼尾跳过去了
if class_names[class_ids[i]] != 'fish_head':
continue
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
# print(verts)
# rb = np.max(verts, axis=0)
# print(verts[verts[:,1]==rb[1]])
# rb = np.max(verts[], axis=0)
# lt = np.min(verts, axis=0)
# TODO 因为只有鱼头，下面结果恰好是头身分割线。同样方法能否得到鱼腹分割线？
ti = np.argmin(verts, axis=0)
print(ti)
# v = verts[verts[:,0]>(rb[0]-30)]
v = verts[ti[1]:,:]
# 通过拟合得到一个点的集合，XY需要交换一下
v1 = v[:, [1,0]]
# 多次拟合得到结果的MSE
MSEs = np.array([])
coefs = []
for i in range(1, 5):
coef = np.polyfit(v1[:,0], v1[:, 1], i)
coefs.append(coef)
x_fit = np.polyval(coef, v1[:, 0])
# print(len(v1), len(x_fit))
MSE = np.linalg.norm(x_fit - v[:, 0], ord=2)**2/len(v)
MSEs = np.append(MSEs, MSE)
# RMSE = np.linalg.norm(x_fit - v[:, 0], ord=2)/len(v)**0.5
# MAE = np.linalg.norm(x_fit - v[:, 0], ord=1)/len(v)
print('MSEs: ', MSEs) #, 'RMSE: ', RMSE, 'MAE: ', MAE)
print('COEFS: ', coefs)
diffMSE = np.diff(MSEs)
print('diffMSE: ', abs(diffMSE))
co_ind = np.where(abs(diffMSE) < 1.0) # 拟合的MSE差异中选择一个 < 1.0 的
print('coef index: ', co_ind)
fx = np.poly1d(coefs[co_ind[0][0]]) # 选择一个合适的
dfx = fx.deriv() # 一阶导
ddfx = dfx.deriv() # 二阶导
# print(np.argmax(v1, axis=0)[0])
# print(np.argmin(v1, axis=0)[0])
# print(v1[np.argmin(v1, axis=0)[0]][0])
# 做一个均匀数据集（一个像素一个点）
v11 = np.arange(v1[np.argmin(v1, axis=0)[0]][0], v1[np.argmax(v1, axis=0)[0]][0])
print(v11, len(v11))
v12 = np.polyval(coefs[co_ind[0][0]], v11)
r = abs(ddfx(v11))/(1 + dfx(v11)**2)**(3.0/2.0)
# print(1./r)
np.savetxt("r.txt", r)
print('CUT Circle VAR: ', np.var(r))
print('CUT Circle VAR: ', np.var(1./r))
# 一维变二维，数据分组，比如分成20组
indices = [3] # 最少3个点为一组子序列
while (len(r) - indices[-1]) > 3: # 有足够点分配就循环
a = np.split(r, indices) # 分组 r 曲率
print(np.var(1./a[-2]))
if np.var(a[-2]) < 0.000001: # 方差够小
indices[-1] += 1 # 增加子序列数量
elif np.var(a[-1]) < -0.001: # 最后子序列方差够小则结束循环
print(a[-1])
print(np.var(a[-1]))
break
else:
indices = np.append(indices, indices[-1]+3) # 添加一个子序列
indices = np.insert(indices, 0, 0)
# print(v[indices], v1[indices])
# print(v[0:20, :], x_fit[0:20])
print('INDICES: ', indices)
# print(v1[indices[0:2], 1])
x_fit1 = np.array([])
next = indices
while len(next) >= 2:
one, _ = np.split(next, [2])
coef = np.polyfit(v11[one], v12[one], 1)
# print(v11[one], v12[one])
# print(v11[one[0]], v11[one[1]])
x_fit1 = np.append(x_fit1, np.polyval(coef, v11[one[0]:one[1]]))
# print(x_fit1)
# print(one, len(x_fit1))
# print(x_fit1, x_fit[indices[0]:indices[1]+1])
# print(v[0:20, :])
_, next = np.split(next, [1])
print('MSE:', np.linalg.norm(x_fit1 - v12[:indices[-1]], ord=2)**2/len(x_fit1))
# print(x_fit1)
# print(v12[:indices[-1]])
"""
# 按照 indices 分组
l = int(len(r) / 10)
inds = np.arange(l, len(r), l) # 分组索引
a = np.split(r, inds)
# 计算每一组的方差
var = [np.var(e) for e in a]
print(var)
indss = np.append(inds, 0) # 补充一位对齐
ind_var = np.array([indss, var]).T # 转置后配对
# 方差很小的，合并；方差很大的，就拆分？Numpy二维怎么合并拆分？
# 当前方差小，则合并后续的一个。移动一个数据。
# 当前方差大，则拆分一个数据给后续。移动一个数据。
# 结束条件是什么呢？所有方差在阈值以下？
for iv in ind_var:
if iv[1] < threshold:
iv[0] += 1
else:
iv[0] -= 1
"""
# 结束后取每组的首尾作为最终结果
# 合并成一个集合
v1[:, 1] = x_fit
v1[:, [0, 1]] = v1[:, [1, 0]]
print(v1[0:20])
co = Polygon(v1, facecolor="none", edgecolor=color)
ax.add_patch(co)
# print(v)
p = Polygon(v, facecolor="none", edgecolor="red")
np.savetxt(result_path+'.txt', v)
ax.add_patch(p)
vv = v1[indices].T
ax.plot(v12[indices], v11[indices], 'go-')
ax.imshow(masked_image.astype(np.uint8))
# ax.savefig('test.png')
plt.savefig(result_path)
# ax.clf()
#plt.close()
# if auto_show:
# plt.show()
# return ax
def display_instances3(image, boxes, masks, ids, names, scores):
"""
take the image and results and apply the mask, box, and Label
"""
import cv2 as cv
n_instances = boxes.shape[0]
# colors = random_colors(n_instances)
colors = {1:(1.0,0.0,0.0), 2:(0.0,1.0,0.0), 3:(0.0,0.0,1.0), 4:(0.5,0.0,0.0), 5:(0.0,0.5,0.0), 6:(0.0,0.0,0.5), 7:(0.25,0.0,0.0)}
colors2 = {1:(255,0,0), 2:(0,255,0), 3:(0,0,255), 4:(128,0,0), 5:(0,128,0), 6:(0,0,128), 7:(64,0,0)}
if not n_instances:
print('NO INSTANCES TO DISPLAY')
else:
assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]
for i in range(n_instances):
# for i, color in enumerate(boxes):
if not np.any(boxes[i]):
continue
# if ids[i] == 0 or ids[i] == 2 or ids[i] == 3:
# continue
y1, x1, y2, x2 = boxes[i]
label = names[ids[i]]
score = scores[i] if scores is not None else None
caption = '{} {:.2f}'.format(label, score) if score else label
mask = masks[:, :, i]
image = apply_mask(image, mask, colors[ids[i]])
image = cv.rectangle(image, (x1, y1), (x2, y2), colors2[ids[i]], 2)
image = cv.putText(
image, caption, (x1, y1), cv.FONT_HERSHEY_COMPLEX, 0.7, colors2[ids[i]], 2
)
return image
import glob
def test_image(model, class_names, result_image_path, image_path, config):
assert image_path
# Image or video?
for image_name in glob.glob(image_path):
# Run model detection and generate the color splash effect
print("Running on {}".format(image_name))
# Read image
image = skimage.io.imread(image_name)
print(image.shape)
# Detect objects
r = model.detect([image], verbose=0)[0]
# print(r)
# Color splash
dt = datetime.now().strftime('%Y%m%d%H%M%S')
result_image_path = image_name + '_result.png'
display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, result_image_path, r['scores'])
image, window, scale, padding, crop = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
min_scale=config.IMAGE_MIN_SCALE,
max_dim=config.IMAGE_MAX_DIM,
mode=config.IMAGE_RESIZE_MODE)
#skimage.io.imsave(result_image_path, image)
print("window: (y1, x1, y2, x2)=",window)
print("scale=",scale)
print("padding:[(top, bottom), (left, right), (0, 0)]=",padding)
print("crop=",crop)
print("Saved to ", result_image_path)
def test_video(model, class_names, result_video_path, video_path):
assert video_path
if video_path:
import cv2 as cv
# Video capture
print(video_path)
vcapture = cv.VideoCapture(video_path)
width = int(vcapture.get(cv.CAP_PROP_FRAME_WIDTH))
height = int(vcapture.get(cv.CAP_PROP_FRAME_HEIGHT))
fps = vcapture.get(cv.CAP_PROP_FPS)
# Define codec and create video writer
print('width: %d, height: %d, fps: %d' % (width, height, fps))
vwriter = cv.VideoWriter(result_video_path,
cv.VideoWriter_fourcc(*'DIVX'),
fps, (width, height))
success = True
frames = []
frames2 = []
frame_count = 0
while success:
print("frame: ", frame_count)
# Read next image
success, frame = vcapture.read()
if not success:
break
frame_count += 1
frames.append(frame)
# OpenCV returns images as BGR, convert to RGB
frame2 = frame[..., ::-1]
frames2.append(frame2)
# Detect objects
results = model.detect(frames2, verbose=0)
for i, item in enumerate(zip(frames, results)):
frame = item[0]
r = item[1]
frame = display_instances3(
frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']
)
name = '{0}.jpg'.format(frame_count + i)
name = os.path.join('./', name)
# cv.imwrite(name, frame)
vwriter.write(frame)
# print('writing to file:{0}'.format(name))
# Clear the frames array to start the next batch
frames = []
frames2 = []
# result_image_path = 'tmp.png'
# # Color splash
# display_instances(image, r['rois'], r['masks'], r['class_ids'],
# class_names, result_image_path, r['scores'])
# new_frame = cv.imread(result_image_path)
# splash = color_splash(image, r['masks'])
# splash = splash[..., ::-1]
# cv.imwrite(result_image_path, splash)
# RGB -> BGR to save image to video
# Add image to video writer
# RGB -> BGR to save image to video
# splash = splash[..., ::-1]
# Add image to video writer
# vwriter.write(new_frame)
# count += 1
# if count == 80:
# break
vwriter.release()
def test_video2(model, class_names, result_video_path, video_path):
assert video_path
if video_path:
import cv2 as cv
# Video capture
vcapture = cv.VideoCapture(video_path)
width = int(vcapture.get(cv.CAP_PROP_FRAME_WIDTH))
height = int(vcapture.get(cv.CAP_PROP_FRAME_HEIGHT))
fps = vcapture.get(cv.CAP_PROP_FPS)
# Define codec and create video writer
vwriter = cv.VideoWriter(result_video_path,
cv.VideoWriter_fourcc(*'DIVX'),
fps, (1600, 1600))
count = 0
success = True
while success:
print("frame: ", count)
# Read next image