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from typing import Tuple, Dict, Optional
import numpy as np
import cv2
def component_filter_thresh(
img: np.ndarray,
color_thresh: int = 75,
avg_area: float = 800,
kernal_size: tuple[int, int] = (3, 3),
max_eggs: Optional[int] = None
) -> Dict:
# Clone image, get grayscale, and masc for candidate egg pixels
visualization_img = img.copy()
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
bin_mask = cv2.inRange(img_gray, 0, color_thresh)
# Filter pixels not part of a elliptical region
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernal_size)
opening = cv2.morphologyEx(bin_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations = 1)
# Get connected components of filtered image
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
close,
)
num_eggs = 0
# Iterate over stats, calculating the number of eggs in each connected component
for curr_label, curr_stat in enumerate(stats):
left_x = curr_stat[cv2.CC_STAT_LEFT]
top_y = curr_stat[cv2.CC_STAT_TOP]
area = curr_stat[cv2.CC_STAT_AREA]
if (area < avg_area / 2) or curr_label == 0:
continue
# Calculate number of eggs
curr_num_eggs = round(area / avg_area)
if max_eggs and (curr_num_eggs > max_eggs):
continue
# Draw border around current component
component_mask = (labels == curr_label).astype(np.uint8)
contours, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(visualization_img, contours, -1, (255, 0, 0), 2)
# Label current component with number of eggs
cv2.putText(
visualization_img,
str(curr_num_eggs),
(left_x, top_y),
cv2.FONT_HERSHEY_SIMPLEX,
1.5,
(0, 0, 255),
3
)
num_eggs += curr_num_eggs
return {
"stats": {
"Num-Eggs": num_eggs
},
"vis": {
"Gray-Scale": img_gray.astype(np.uint8),
"Egg-Mask": bin_mask.astype(np.uint8),
"Ellipse-Filter": close.astype(np.uint8),
"Visualization": visualization_img.astype(np.uint8)
}
}
def component_thesh(
img: np.ndarray,
color_thresh: int = 75,
avg_area: float = 800,
max_eggs: Optional[int] = None
) -> Dict:
# Clone image
visualization_img = img.copy()
# Convert to grayscale
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Mask out egg pixels
bin_mask = cv2.inRange(img_gray, 0, color_thresh)
# Get connected components
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
bin_mask,
)
num_eggs = 0
# Iterate over stats, calculating the number of eggs in each connected component
for curr_label, curr_stat in enumerate(stats):
left_x = curr_stat[cv2.CC_STAT_LEFT]
top_y = curr_stat[cv2.CC_STAT_TOP]
area = curr_stat[cv2.CC_STAT_AREA]
if (area < avg_area / 2) or curr_label == 0:
continue
# Calculate number of eggs
curr_num_eggs = round(area / avg_area)
if max_eggs and (curr_num_eggs > max_eggs):
continue
# Draw border around current component
component_mask = (labels == curr_label).astype(np.uint8)
contours, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(visualization_img, contours, -1, (255, 0, 0), 2)
# Label current component with number of eggs
cv2.putText(
visualization_img,
str(curr_num_eggs),
(left_x, top_y),
cv2.FONT_HERSHEY_SIMPLEX,
1.5,
(0, 0, 255),
3
)
num_eggs += curr_num_eggs
return {
"stats": {
"Num-Eggs": num_eggs
},
"vis": {
"Gray-Scale": img_gray.astype(np.uint8),
"Egg-Mask": bin_mask.astype(np.uint8),
"Visualization": visualization_img.astype(np.uint8)
}
}
def contour_thresh(
img: np.ndarray,
color_thresh: int = 75,
avg_area: float = 800,
kernal_size: tuple[int, int] = (3, 3),
max_eggs: Optional[int] = None
) -> Dict:
visualization_img = img.copy()
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
bin_mask = cv2.inRange(img_gray, 0, color_thresh)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernal_size)
opening = cv2.morphologyEx(bin_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations = 2)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
num = 0
for cnt in cnts:
area = cv2.contourArea(cnt)
if area > avg_area / 2:
curr_num = round(area / avg_area)
if max_eggs and (curr_num > max_eggs):
continue
cv2.drawContours(visualization_img, [cnt], -1, (255, 0, 0), 2)
curr_num = round(area / avg_area)
num += curr_num
cv2.putText(
visualization_img,
str(curr_num),
cnt[0, 0],
cv2.FONT_HERSHEY_SIMPLEX,
1.5,
(0, 0, 255),
3
)
return {
"stats": {
"Num-Eggs": num
},
"vis": {
"Gray-Scale": img_gray.astype(np.uint8),
"Egg-Mask": bin_mask.astype(np.uint8),
"Ellipse-Filter": close.astype(np.uint8),
"Visualization": visualization_img.astype(np.uint8)
}
}
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