Spaces:
Runtime error
Runtime error
basic web-app implementation
Browse files- eggcount/app.py +25 -0
- eggcount/cli.py +131 -0
- eggcount/demo.py +0 -37
- eggcount/eggcount.py +0 -44
- eggcount/gradient.py +184 -0
- eggcount/pages/__init__.py +0 -0
- eggcount/pages/home.py +22 -0
- eggcount/ui/ui_utils.py +19 -0
eggcount/app.py
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import dash_bootstrap_components as dbc
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import dash
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from eggcount.ui.ui_utils import (
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get_navbar
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)
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from dash import Dash, html, dcc
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app = Dash(
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__name__,
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use_pages = True,
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external_stylesheets=[dbc.themes.BOOTSTRAP]
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)
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app.layout = dbc.Container(
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children = [
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get_navbar(),
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dash.page_container
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],
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class_name = "m-0 p-0 w-100 mw-100",
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id = "content-container"
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)
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if __name__ == '__main__':
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app.run(debug = True)
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eggcount/cli.py
ADDED
@@ -0,0 +1,131 @@
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from eggcount.gradient import (
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contour_thresh,
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component_thesh,
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component_filter_thresh
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)
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from os import PathLike
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from PIL import Image
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from pillow_heif import register_heif_opener
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from typing import Optional
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import os
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import fire
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register_heif_opener()
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def filter_connected_components(
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img_dir: PathLike,
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color_thresh: int = 75,
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avg_area: float = 800,
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vis: bool = False,
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save_loc: PathLike = "",
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kernal_size: tuple[int, int] = (3, 3),
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max_eggs: Optional[int] = None
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) -> None:
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# Open image, supports apple HEIC format
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pil_img = Image.open(img_dir)
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# Convert to standard RGB Image
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img = np.array(pil_img)
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res = component_filter_thresh(
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img,
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color_thresh = color_thresh,
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avg_area = avg_area,
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kernal_size = kernal_size,
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max_eggs = max_eggs
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)
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res_vis = res["vis"]
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res_stats = res["stats"]
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for label, stat in res_stats.items():
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print(f"{label.replace('-', ' ')}: {stat}")
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if vis:
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for label, curr_img in res_vis.items():
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plt.imshow(curr_img)
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plt.show()
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if save_loc:
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for label, curr_img in res_vis.items():
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save_path = os.path.join(save_loc, label + ".png")
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plt.imsave(save_path, curr_img)
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def connected_components(
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img_dir: PathLike,
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color_thresh: int = 75,
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avg_area: float = 800,
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vis: bool = False,
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save_loc: PathLike = "",
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max_eggs: Optional[int] = None
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) -> None:
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# Open Image
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pil_img = Image.open(img_dir)
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# Convert to standard RGB Image
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img = np.array(pil_img)
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res = component_thesh(
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img,
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color_thresh = color_thresh,
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avg_area = avg_area,
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max_eggs = max_eggs
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)
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res_vis = res["vis"]
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res_stats = res["stats"]
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for label, stat in res_stats.items():
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print(f"{label.replace('-', ' ')}: {stat}")
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if vis:
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for label, curr_img in res_vis.items():
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plt.imshow(curr_img)
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plt.show()
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if save_loc:
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for label, curr_img in res_vis.items():
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save_path = os.path.join(save_loc, label + ".png")
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plt.imsave(save_path, curr_img)
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def contour(
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img_dir: PathLike,
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color_thresh: int = 75,
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avg_area: float = 800,
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vis: bool = False,
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save_loc: PathLike = "",
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kernal_size: tuple[int, int] = (3, 3)
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) -> None:
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# Open image, supports apple HEIC format
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pil_img = Image.open(img_dir)
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# Convert to standard RGB Image
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img = np.array(pil_img)
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res = contour_thresh(
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img,
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color_thresh = color_thresh,
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avg_area = avg_area,
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kernal_size = kernal_size
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)
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res_vis = res["vis"]
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res_stats = res["stats"]
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for label, stat in res_stats.items():
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print(f"{label.replace('-', ' ')}: {stat}")
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if vis:
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for label, curr_img in res_vis.items():
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plt.imshow(curr_img)
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plt.show()
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if save_loc:
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for label, curr_img in res_vis.items():
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save_path = os.path.join(save_loc, label + ".png")
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plt.imsave(save_path, curr_img)
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if __name__ == "__main__":
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fire.Fire()
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eggcount/demo.py
DELETED
@@ -1,37 +0,0 @@
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from eggcount.eggcount import (
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count_eggs_contour_thresh
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)
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from os import PathLike
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from PIL import Image
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from pillow_heif import register_heif_opener
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import numpy as np
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import matplotlib.pyplot as plt
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import fire
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register_heif_opener()
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def demo(
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img_dir: PathLike,
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color_thresh: int = 75,
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avg_area: float = 800
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) -> None:
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# Open image, suppoorts apple HEIC format
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pil_img = Image.open(img_dir)
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# Convert to standard RGB Image
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img = np.array(pil_img)
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num, processed_image = count_eggs_contour_thresh(
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img,
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color_thresh = color_thresh,
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avg_area = avg_area
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)
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print(num)
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plt.imshow(processed_image)
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plt.show()
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if __name__ == "__main__":
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fire.Fire(demo)
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eggcount/eggcount.py
DELETED
@@ -1,44 +0,0 @@
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from typing import Tuple
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import numpy as np
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import cv2
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import fire
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def contour_thresh(
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img: np.ndarray,
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color_thresh: int = 75,
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avg_area: float = 800
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) -> Tuple[int, np.ndarray]:
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img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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bin_mask = cv2.inRange(img_gray, 0, color_thresh)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
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opening = cv2.morphologyEx(bin_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
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close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations = 2)
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-
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cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = cnts[0] if len(cnts) == 2 else cnts[1]
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num = 0
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for cnt in cnts:
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area = cv2.contourArea(cnt)
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-
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if area > avg_area / 2:
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cv2.drawContours(img, [cnt], -1, (255, 0, 0), 2)
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curr_num = round(area / avg_area)
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num += curr_num
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cv2.putText(
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img,
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str(curr_num),
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cnt[0, 0],
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cv2.FONT_HERSHEY_SIMPLEX,
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1.5,
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(0, 0, 255),
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3
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)
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return num, img
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if __name__ == "__main__":
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fire.Fire()
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eggcount/gradient.py
ADDED
@@ -0,0 +1,184 @@
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1 |
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from typing import Tuple, Dict, Optional
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2 |
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3 |
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import numpy as np
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4 |
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import cv2
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5 |
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6 |
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def component_filter_thresh(
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7 |
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img: np.ndarray,
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8 |
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color_thresh: int = 75,
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9 |
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avg_area: float = 800,
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10 |
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kernal_size: tuple[int, int] = (3, 3),
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11 |
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max_eggs: Optional[int] = None
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) -> Dict:
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# Clone image, get grayscale, and masc for candidate egg pixels
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visualization_img = img.copy()
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img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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bin_mask = cv2.inRange(img_gray, 0, color_thresh)
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# Filter pixels not part of a elliptical region
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19 |
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernal_size)
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20 |
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opening = cv2.morphologyEx(bin_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
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21 |
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close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations = 1)
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22 |
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23 |
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# Get connected components of filtered image
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24 |
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
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25 |
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close,
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26 |
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)
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27 |
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num_eggs = 0
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29 |
+
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30 |
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# Iterate over stats, calculating the number of eggs in each connected component
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31 |
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for curr_label, curr_stat in enumerate(stats):
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32 |
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left_x = curr_stat[cv2.CC_STAT_LEFT]
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33 |
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top_y = curr_stat[cv2.CC_STAT_TOP]
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34 |
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area = curr_stat[cv2.CC_STAT_AREA]
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35 |
+
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36 |
+
if (area < avg_area / 2) or curr_label == 0:
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37 |
+
continue
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38 |
+
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39 |
+
# Calculate number of eggs
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40 |
+
curr_num_eggs = round(area / avg_area)
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41 |
+
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42 |
+
if max_eggs and (curr_num_eggs > max_eggs):
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43 |
+
continue
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44 |
+
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45 |
+
# Draw border around current component
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46 |
+
component_mask = (labels == curr_label).astype(np.uint8)
|
47 |
+
contours, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
48 |
+
cv2.drawContours(visualization_img, contours, -1, (255, 0, 0), 2)
|
49 |
+
|
50 |
+
# Label current component with number of eggs
|
51 |
+
cv2.putText(
|
52 |
+
visualization_img,
|
53 |
+
str(curr_num_eggs),
|
54 |
+
(left_x, top_y),
|
55 |
+
cv2.FONT_HERSHEY_SIMPLEX,
|
56 |
+
1.5,
|
57 |
+
(0, 0, 255),
|
58 |
+
3
|
59 |
+
)
|
60 |
+
|
61 |
+
num_eggs += curr_num_eggs
|
62 |
+
|
63 |
+
return {
|
64 |
+
"stats": {
|
65 |
+
"Num-Eggs": num_eggs
|
66 |
+
},
|
67 |
+
"vis": {
|
68 |
+
"Egg-Mask": bin_mask.astype(np.uint8),
|
69 |
+
"Ellipse-Filter": close.astype(np.uint8),
|
70 |
+
"Visualization": visualization_img.astype(np.uint8)
|
71 |
+
}
|
72 |
+
}
|
73 |
+
|
74 |
+
def component_thesh(
|
75 |
+
img: np.ndarray,
|
76 |
+
color_thresh: int = 75,
|
77 |
+
avg_area: float = 800,
|
78 |
+
max_eggs: Optional[int] = None
|
79 |
+
) -> Dict:
|
80 |
+
# Clone image
|
81 |
+
visualization_img = img.copy()
|
82 |
+
|
83 |
+
# Convert to grayscale
|
84 |
+
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
|
85 |
+
|
86 |
+
# Mask out egg pixels
|
87 |
+
bin_mask = cv2.inRange(img_gray, 0, color_thresh)
|
88 |
+
|
89 |
+
# Get connected components
|
90 |
+
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
|
91 |
+
bin_mask,
|
92 |
+
)
|
93 |
+
|
94 |
+
num_eggs = 0
|
95 |
+
|
96 |
+
# Iterate over stats, calculating the number of eggs in each connected component
|
97 |
+
for curr_label, curr_stat in enumerate(stats):
|
98 |
+
left_x = curr_stat[cv2.CC_STAT_LEFT]
|
99 |
+
top_y = curr_stat[cv2.CC_STAT_TOP]
|
100 |
+
area = curr_stat[cv2.CC_STAT_AREA]
|
101 |
+
|
102 |
+
if (area < avg_area / 2) or curr_label == 0:
|
103 |
+
continue
|
104 |
+
|
105 |
+
# Calculate number of eggs
|
106 |
+
curr_num_eggs = round(area / avg_area)
|
107 |
+
|
108 |
+
if max_eggs and (curr_num_eggs > max_eggs):
|
109 |
+
continue
|
110 |
+
|
111 |
+
# Draw border around current component
|
112 |
+
component_mask = (labels == curr_label).astype(np.uint8)
|
113 |
+
contours, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
114 |
+
cv2.drawContours(visualization_img, contours, -1, (255, 0, 0), 2)
|
115 |
+
|
116 |
+
# Label current component with number of eggs
|
117 |
+
cv2.putText(
|
118 |
+
visualization_img,
|
119 |
+
str(curr_num_eggs),
|
120 |
+
(left_x, top_y),
|
121 |
+
cv2.FONT_HERSHEY_SIMPLEX,
|
122 |
+
1.5,
|
123 |
+
(0, 0, 255),
|
124 |
+
3
|
125 |
+
)
|
126 |
+
|
127 |
+
num_eggs += curr_num_eggs
|
128 |
+
|
129 |
+
return {
|
130 |
+
"stats": {
|
131 |
+
"Num-Eggs": num_eggs
|
132 |
+
},
|
133 |
+
"vis": {
|
134 |
+
"Egg-Mask": bin_mask.astype(np.uint8),
|
135 |
+
"Visualization": visualization_img.astype(np.uint8)
|
136 |
+
}
|
137 |
+
}
|
138 |
+
|
139 |
+
def contour_thresh(
|
140 |
+
img: np.ndarray,
|
141 |
+
color_thresh: int = 75,
|
142 |
+
avg_area: float = 800,
|
143 |
+
kernal_size: tuple[int, int] = (3, 3)
|
144 |
+
) -> Dict:
|
145 |
+
visualization_img = img.copy()
|
146 |
+
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
|
147 |
+
bin_mask = cv2.inRange(img_gray, 0, color_thresh)
|
148 |
+
|
149 |
+
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernal_size)
|
150 |
+
opening = cv2.morphologyEx(bin_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
|
151 |
+
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations = 2)
|
152 |
+
|
153 |
+
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
154 |
+
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
|
155 |
+
num = 0
|
156 |
+
|
157 |
+
for cnt in cnts:
|
158 |
+
area = cv2.contourArea(cnt)
|
159 |
+
|
160 |
+
if area > avg_area / 2:
|
161 |
+
cv2.drawContours(visualization_img, [cnt], -1, (255, 0, 0), 2)
|
162 |
+
curr_num = round(area / avg_area)
|
163 |
+
num += curr_num
|
164 |
+
|
165 |
+
cv2.putText(
|
166 |
+
visualization_img,
|
167 |
+
str(curr_num),
|
168 |
+
cnt[0, 0],
|
169 |
+
cv2.FONT_HERSHEY_SIMPLEX,
|
170 |
+
1.5,
|
171 |
+
(0, 0, 255),
|
172 |
+
3
|
173 |
+
)
|
174 |
+
|
175 |
+
return {
|
176 |
+
"stats": {
|
177 |
+
"Num-Eggs": num
|
178 |
+
},
|
179 |
+
"vis": {
|
180 |
+
"Egg-Mask": bin_mask.astype(np.uint8),
|
181 |
+
"Ellipse-Filter": close.astype(np.uint8),
|
182 |
+
"Visualization": visualization_img.astype(np.uint8)
|
183 |
+
}
|
184 |
+
}
|
eggcount/pages/__init__.py
ADDED
File without changes
|
eggcount/pages/home.py
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from dash import html, dcc, callback, Input, Output
|
2 |
+
|
3 |
+
import dash
|
4 |
+
import dash_bootstrap_components as dbc
|
5 |
+
|
6 |
+
dash.register_page(__name__, path = "/")
|
7 |
+
|
8 |
+
UPLOAD_HEIGHT = "25vh"
|
9 |
+
|
10 |
+
layout = dbc.Container(
|
11 |
+
children = dcc.Upload(
|
12 |
+
id = 'upload-data',
|
13 |
+
children = dbc.Container(
|
14 |
+
"Upload Image Here",
|
15 |
+
class_name = "w-100 border border-dark",
|
16 |
+
style = {
|
17 |
+
"height": UPLOAD_HEIGHT
|
18 |
+
}
|
19 |
+
)
|
20 |
+
),
|
21 |
+
class_name = "text-center mt-3"
|
22 |
+
)
|
eggcount/ui/ui_utils.py
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import dash_bootstrap_components as dbc
|
2 |
+
|
3 |
+
NAVBAR_MIN_HEIGHT = "4rem"
|
4 |
+
|
5 |
+
def get_navbar() -> dbc.Nav:
|
6 |
+
return dbc.Nav(
|
7 |
+
children = [
|
8 |
+
dbc.NavItem(
|
9 |
+
dbc.NavLink("Home")
|
10 |
+
),
|
11 |
+
dbc.NavItem(
|
12 |
+
dbc.NavLink("About")
|
13 |
+
)
|
14 |
+
],
|
15 |
+
class_name = "bg-dark",
|
16 |
+
style = {
|
17 |
+
"min-height": NAVBAR_MIN_HEIGHT
|
18 |
+
}
|
19 |
+
)
|