Coverage for /opt/conda/envs/apienv/lib/python3.10/site-packages/daiquiri/implementors/imageviewer/autofocus.py: 0%

1import time 

2import numpy as np 

3import cv2 

4 

5from daiquiri.core.components import ComponentActor 

6 

7 

8class AutofocusActor(ComponentActor): 

9 name = "autofocus" 

10 

11 def method(self, *args, **kwargs): 

12 print("autofocus", self["z_iterations"], self["z_increment"]) 

13 

14 max_contrast = 0 

15 max_contrast_pos = None 

16 

17 current = self["z_motor"].get("position") 

18 start = current - (self["z_iterations"] * self["z_increment"]) 

19 for i in range(self["z_iterations"] * 2): 

20 position = start + self["z_increment"] * i 

21 

22 self["z_motor"].move(position) 

23 self["z_motor"].wait() 

24 

25 time.sleep(kwargs["camera"].get("exposure") * 1.2) 

26 

27 contrast = self.get_canny() 

28 print("point", i, position, contrast) 

29 if contrast > max_contrast: 

30 max_contrast = contrast 

31 max_contrast_pos = position 

32 

33 print("max contrast", max_contrast, "at", max_contrast_pos) 

34 if max_contrast_pos is not None: 

35 self["z_motor"].move(max_contrast_pos) 

36 self["z_motor"].wait() 

37 

38 def get_michelson(self): 

39 """Calculate the Michelson contrast 

40 

41 https://en.wikipedia.org/wiki/Contrast_(vision)#Michelson_contrast 

42 """ 

43 frame = self["camera"].frame() 

44 Y = cv2.cvtColor(frame, cv2.COLOR_RGB2YUV)[:, :, 0] 

45 

46 min = np.min(Y) 

47 max = np.max(Y) 

48 

49 contrast = (max - min) / (max + min) 

50 print("min", min, "max", max, "contrast", contrast) 

51 

52 return contrast 

53 

54 def get_canny(self): 

55 """Use canny edge detection to detect frame sharpness 

56 

57 https://medium.com/@sedara/how-the-camera-autofocus-feature-works-in-digital-smartphones-8382d511996c 

58 https://github.com/opencv/opencv/blob/master/samples/cpp/videocapture_gphoto2_autofocus.cpp 

59 """ 

60 frame = self["camera"].frame() 

61 gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) 

62 

63 gray = cv2.GaussianBlur(gray, (7, 7), 1.5, 1.5) 

64 edges = cv2.Canny(gray, 0, 30, 3) 

65 

66 total = np.sum(edges) / (frame.shape[0] * frame.shape[1]) 

67 print("total", total) 

68 

69 return total