asteroid-automator/imagepipeline.py

166 lines
6.6 KiB
Python

import cv2
import numpy as np
import typing
import pointcluster
from shapes import Rect
from utility import *
class CVImage:
"""Dummy definition to allow recursive type hints"""
pass
class CVImage:
def __init__(self, label="", img:np.ndarray=None, **kwargs):
"""You can provide a 'filename' keyword arg to automatically load a file."""
self.label = label
self.image = img
self._init_kwargs = kwargs
if kwargs:
load_kwargs = dict(kwargs) # copy
self.load(**load_kwargs)
@property
def is_color(self):
return len(self.image.shape) == 3
def load(self, filename:str, label:str=None):
"""Load an image from file. You can optionally set the 'label' keyword."""
self.image = cv2.imread(filename)
if label: self.label = label
return self
def convert_color(self, color:bool):
return cv2.cvtColor(self.image, cv2.COLOR_GRAY2BGR if color else cv2.COLOR_BGR2GRAY)
def __repr__(self):
if self._init_kwargs:
kwargstr = ", " + ", ".join([f"{k}={repr(self._init_kwargs[k])}" for k in self._init_kwargs])
else:
kwargstr = ''
return f"<CVImage label={repr(self.label)}, image={self.image.shape} px, is_color={self.is_color}{kwargstr}>"
def copy(self):
return np.copy(self.image)
def snip(self, rect):
assert all((len(rect)==2, len(rect[0])==2, len(rect[1])==2)) #((x,y),(w,h))
return self.image[rect[0][1]:rect[0][1]+rect[1][1],
rect[0][0]:rect[0][0]+rect[1][0]
]
def mask(self, rect, mask_color=None, nonmask_color=None):
assert all((len(rect)==2, len(rect[0])==2, len(rect[1])==2)) #((x,y),(w,h))
if mask_color is None:
mask_color = (0, 0, 0) if self.is_color else 0
if nonmask_color is None:
nonmask_color = (255, 255, 255) if self.is_color else 255
mask = np.full(self.image.shape, nonmask_color, dtype=np.uint8)
cv2.rectangle(mask, *rect, color=mask_color, thickness=cv2.FILLED)
return cv2.bitwise_and(self.image, mask)
def sift_clusters(self, cluster_radius) -> pointcluster.PointCluster:
sift = cv2.SIFT_create()
keypoints, descriptions = sift.detectAndCompute(self.image, None)
return pointcluster.cluster_set([k.pt for k in keypoints], cluster_radius)
@staticmethod
def blob_params(*, minThreshold = 10, maxThreshold = 200,
minArea = None, maxArea = None,
minCircularity = None, maxCircularity = None,
minConvexity = None, maxConvexity = None,
minInertiaRatio = None, maxInertiaRatio = None):
p = cv2.SimpleBlobDetector_Params()
p.minThreshold = minThreshold
p.maxThreshold = maxThreshold
if minArea or maxArea:
p.filterByArea = True
if minArea: p.minArea = minArea
if maxArea: p.maxArea = maxArea
if minConvexity or maxConvexity:
p.filterByConvexity = True
if minConvexity: p.minConvexity = minConvexity
if maxConvexity: p.maxConvexity = maxConvexity
if minInertiaRatio or maxInertiaRatio:
p.filterByInertia = True
if minInertiaRatio: p.minInertiaRatio = minInertiaRatio
if maxInertiaRatio: p.maxInertiaRatio = maxInertiaRatio
if minCircularity or maxCircularity:
p.filterByCircularity = True
if minCircularity: p.minCircularity = minCircularity
if maxCircularity: p.maxCircularity = maxCircularity
return p
def blob_detect(self, size:int, params=None, invert:bool=False, label:str=None) -> typing.List[Rect]:
if params is None: params = CVImage.blob_params()
detector = cv2.SimpleBlobDetector_create(params)
keypoints = detector.detect(cv2.bitwise_not(self.image) if invert else self.image)
rects = []
s = size / 2.0
for kp in keypoints:
rects.append(Rect(x=kp.pt[0] - s, y = kp.pt[1] - s,
w = size, h = size,
label = label or "blob"))
return rects
def template_detect(self, template:CVImage, threshold:int, dupe_spacing:int) -> typing.List[Rect]:
if template.is_color:
h, w, _ = template.image.shape
else:
h, w = template.image.shape
if template.is_color != self.is_color:
template = CVImage(template.label, template.convert_color(not template.is_color))
res = cv2.matchTemplate(self.image, template.image, cv2.TM_CCOEFF_NORMED)
loc = np.where(res >= threshold)
rects = []
for pt in zip(*loc[::-1]):
if len(rects) > 0:
if squared_distance(rects[-1][0], pt) < dupe_spacing: continue
rects.append(Rect(x=pt[0], y=pt[1], w=w, h=h, label=template.label))
return rects
def show(self, delay=0):
cv2.imshow(self.label, self.image)
cv2.waitKey(delay)
def draw_rect(self, rect:Rect, color=None, text_color=None, text:bool=True, thickness=1):
if color is None:
color = (255, 255, 255) if self.is_color else 255
cv2.rectangle(self.image, rect.point, rect.point2, color, thickness)
if text:
self.draw_text(rect.label, rect.point, text_color if text_color else color)
def draw_poly(self, points:typing.List[typing.Tuple], closed=True, color=None):
if color is None:
color = (255, 255, 255) if self.is_color else 255
cv2.polylines(self.image, np.int32([points]), closed, color)
def draw_circle(self, center, radius, color=None, thickness = 1):
if color is None:
color = (255, 255, 255) if self.is_color else 255
cv2.circle(self.image, np.int32(center), np.int32(radius), color, thickness)
def draw_text(self, text, point, color):
cv2.putText(self.image, text, np.int32(point), cv2.FONT_HERSHEY_PLAIN, 1.0, color)
class ImagePipeline:
def __init__(self):
pass
# running this module executes tests
if __name__ == '__main__':
# initializer for CVImage can load from file
img = CVImage("test frame", filename="/home/john/Desktop/Screenshot at 2021-12-19 20-55-22.png")
#img.show()
# initializer for CVImage can accept a numpy array
img_no_title = CVImage("test frame", img.snip( ((0,24),(800,600)) ))
#img_no_title.show()
#standard rectangle format used throughout the class, avoiding ugly splat operator
lives_rect = ((10,10), (190, 65))
lives = CVImage("lives", img_no_title.snip(lives_rect))
lives.show()