# Detecting shapes with OpenCV, and getting all the important information in a DataFrame
<img src="https://github.com/hansalemaos/screenshots/raw/main/findshapes_1.png"/>
<img src="https://github.com/hansalemaos/screenshots/raw/main/findshapes_2.png"/>
```python
$pip install a-cv2-shape-finder
from a_cv2_shape_finder import get_shapes_using_ADAPTIVE_THRESH_GAUSSIAN_C,get_shapes_using_ADAPTIVE_THRESH_MEAN_C,get_shapes_using_THRESH_OTSU
import cv2
import pandas as pd
from a_cv2_imshow_thread import add_imshow_thread_to_cv2
from a_cv_imwrite_imread_plus import add_imwrite_plus_imread_plus_to_cv2
import numpy as np
add_imwrite_plus_imread_plus_to_cv2()
add_imshow_thread_to_cv2()
image2=cv2.imread_plus( r"http://clipart-library.com/img/2000719.png")
#method1 (best results)
df, bw_pic = get_shapes_using_ADAPTIVE_THRESH_GAUSSIAN_C(
im=image2.copy(),
method=cv2.CHAIN_APPROX_SIMPLE,
approxPolyDPvar=0.02,
constant_subtracted=2,
block_size=11,
return_bw_pic=True,
)
#method2 (good results)
df, bw_pic = get_shapes_using_ADAPTIVE_THRESH_MEAN_C(
im=image2.copy(),
method=cv2.CHAIN_APPROX_SIMPLE,
approxPolyDPvar=0.04,
constant_subtracted=2,
block_size=11,
return_bw_pic=True,
)
#method3 (not always good results)
df, bw_pic = get_shapes_using_THRESH_OTSU(
im=image2.copy(),
method=cv2.CHAIN_APPROX_SIMPLE,
approxPolyDPvar=0.01,
kernel=(1, 1),
start_thresh=50,
end_thresh=255,
return_bw_pic=True,
)
aa_arcLength aa_isContourConvex aa_center_x aa_center_y aa_area aa_convexHull aa_len_convexHull aa_len_cnts aa_shape aa_rotated_rectangle aa_minEnclosingCircle_center aa_minEnclosingCircle_radius aa_fitEllipse aa_fitLine aa_h0 aa_h1 aa_h2 aa_h3 aa_bound_start_x aa_bound_start_y aa_bound_end_x aa_bound_end_y aa_bound_width aa_bound_height
0 1994.000000 True 286 212 242952.0 [[[573, 0]], [[573, 424]], [[0, 424]], [[0, 0]]] 4 4 rectangle [[0, 0], [573, 0], [573, 424], [0, 424]] (286, 212) 356 <NA> ((573, 212), (0, 212)) -1 -1 1 -1 0 0 574 425 574 425
1 17.656854 True 511 398 23.0 [[[509, 397]], [[510, 396]], [[513, 396]], [[514, 397]], [[514, 400]], [[513, 401]], [[510, 401]], [[509, 400]]] 8 8 circle [[509, 396], [514, 396], [514, 401], [509, 401]] (511, 398) 2 ((511.5, 398.5), (5.830951690673828, 5.830951690673828), 0.0) ((573, 398), (0, 398)) 2 -1 -1 0 509 396 515 402 6 6
2 67.213203 False 402 395 128.0 [[[396, 388]], [[406, 387]], [[409, 399]], [[405, 401]], [[397, 401]]] 5 7 oval [[395, 388], [407, 386], [409, 400], [397, 401]] (402, 394) 8 ((401.52850341796875, 394.2469482421875), (13.484291076660156, 29.70796012878418), 84.56861877441406) ((573, 1267), (0, -1646)) 3 1 -1 0 396 387 410 402 14 15
3 88.183766 False 543 396 172.5 [[[538, 386]], [[551, 389]], [[550, 401]], [[543, 401]], [[535, 400]]] 5 10 oval [[535, 400], [538, 386], [552, 389], [549, 403]] (542, 394) 9 ((540.5974731445312, 393.8035888671875), (14.401185989379883, 27.078874588012695), 93.7881088256836) ((573, 561), (0, -2567)) 4 2 -1 0 535 386 552 402 17 16
4 42.384776 True 530 393 122.5 [[[530, 386]], [[532, 386]], [[535, 389]], [[535, 397]], [[533, 400]], [[528, 401]], [[525, 398]], [[525, 392]], [[527, 388]]] 9 9 oval [[525, 401], [525, 386], [535, 386], [535, 401]] (530, 393) 7 ((530.2174682617188, 393.71160888671875), (11.07934856414795, 15.688089370727539), 10.645519256591797) ((573, -138), (0, 6796)) 6 3 5 0 525 386 536 402 11 16
5 12.242641 False 530 393 7.5 [[[531, 395]], [[530, 396]], [[529, 395]], [[529, 393]], [[530, 391]], [[531, 391]]] 6 7 oval [[529, 391], [531, 391], [531, 396], [529, 396]] (530, 393) 2 ((530.2325439453125, 393.48016357421875), (2.533412456512451, 5.182924270629883), 12.179041862487793) ((573, -82), (0, 6120)) -1 -1 -1 4 529 391 532 397 3 6
6 53.941125 False 520 393 102.0 [[[521, 386]], [[525, 389]], [[525, 396]], [[522, 400]], [[519, 401]], [[515, 398]], [[514, 394]], [[516, 389]]] 8 12 oval [[511, 395], [519, 384], [528, 391], [520, 402]] (520, 393) 7 ((519.7500610351562, 393.70819091796875), (10.821428298950195, 15.00129222869873), 12.044055938720703) ((573, 263), (0, 1652)) 7 4 -1 0 514 386 526 402 12 16
7 124.911687 False 475 395 252.5 [[[471, 386]], [[477, 386]], [[488, 389]], [[487, 398]], [[480, 410]], [[475, 409]], [[466, 398]], [[467, 392]]] 8 15 oval [[463, 405], [469, 383], [489, 389], [483, 411]] (477, 397) 13 ((477.4767761230469, 396.48944091796875), (20.678686141967773, 25.679677963256836), 21.347288131713867) ((573, 781), (0, -1472)) 9 6 8 0 466 386 489 411 23 25
8 10.242641 True 482 393 7.5 [[[484, 394]], [[483, 395]], [[482, 395]], [[481, 394]], [[481, 393]], [[482, 392]], [[484, 392]]] 7 7 circle [[481, 392], [484, 392], [484, 395], [481, 395]] (482, 393) 1 ((482.6419372558594, 393.3580627441406), (3.061401844024658, 3.7628066539764404), 45.0) ((573, 302), (0, 876)) -1 -1 -1 7 481 392 485 396 4 4
9 81.112698 False 459 399 212.5 [[[465, 386]], [[466, 405]], [[463, 410]], [[458, 410]], [[454, 403]], [[455, 391]], [[458, 387]]] 7 11 oval [[453, 386], [465, 386], [466, 409], [454, 410]] (461, 398) 12 ((460.94775390625, 397.08856201171875), (13.2537202835083, 25.697481155395508), 2.9813687801361084) ((573, 2400), (0, -7608)) 11 7 10 0 454 386 467 411 13 25
#Let's draw the results from the second picture
#There is nothing better than Pandas to process data.
image = image2.copy()
for name, group in df.groupby("aa_h3"):
if name == 0:
continue
fabb = (
np.random.randint(50, 250),
np.random.randint(50, 250),
np.random.randint(50, 250),
)
for key, item in group.loc[(group.aa_area > 200) & (
group.aa_shape.isin(['rectangle', 'triangle', 'circle', 'pentagon', 'hexagon']))].iterrows():
image = cv2.drawContours(
image, item.aa_convexHull, -1, color=fabb, thickness=5, lineType=cv2.LINE_AA
)
image = cv2.rectangle(
image,
(item.aa_bound_start_x, item.aa_bound_start_y),
(item.aa_bound_end_x, item.aa_bound_end_y),
(0, 0, 0),
3,
)
image = cv2.rectangle(
image,
(item.aa_bound_start_x, item.aa_bound_start_y),
(item.aa_bound_end_x, item.aa_bound_end_y),
fabb,
2,
)
image = cv2.putText(
image,
f'{str(item.aa_shape)} - {name}',
(item.aa_bound_start_x, item.aa_bound_start_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(0, 0, 0),
2,
cv2.LINE_AA,
)
image = cv2.putText(
image,
f'{str(item.aa_shape)} - {name}',
(item.aa_bound_start_x, item.aa_bound_start_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
fabb,
1,
cv2.LINE_AA,
)
cv2.imshow_thread([image, bw_pic])
```
Raw data
{
"_id": null,
"home_page": "https://github.com/hansalemaos/a_cv2_shape_finder",
"name": "a-cv2-shape-finder",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "opencv,cv2,pandas,DataFrame,shapes,object detection,shape",
"author": "Johannes Fischer",
"author_email": "<aulasparticularesdealemaosp@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/4b/fc/9143f49188dc2efd7a93d842659ce49408277dd253ddb2a849c63e78c448/a_cv2_shape_finder-0.11.tar.gz",
"platform": null,
"description": "\n# Detecting shapes with OpenCV, and getting all the important information in a DataFrame\n\n\n\n<img src=\"https://github.com/hansalemaos/screenshots/raw/main/findshapes_1.png\"/>\n\n<img src=\"https://github.com/hansalemaos/screenshots/raw/main/findshapes_2.png\"/>\n\n\n\n```python\n\n$pip install a-cv2-shape-finder\n\nfrom a_cv2_shape_finder import get_shapes_using_ADAPTIVE_THRESH_GAUSSIAN_C,get_shapes_using_ADAPTIVE_THRESH_MEAN_C,get_shapes_using_THRESH_OTSU\n\nimport cv2\n\nimport pandas as pd\n\nfrom a_cv2_imshow_thread import add_imshow_thread_to_cv2\n\nfrom a_cv_imwrite_imread_plus import add_imwrite_plus_imread_plus_to_cv2\n\nimport numpy as np\n\nadd_imwrite_plus_imread_plus_to_cv2()\n\nadd_imshow_thread_to_cv2()\n\nimage2=cv2.imread_plus( r\"http://clipart-library.com/img/2000719.png\")\n\n\n\n#method1 (best results)\n\ndf, bw_pic = get_shapes_using_ADAPTIVE_THRESH_GAUSSIAN_C(\n\n im=image2.copy(),\n\n method=cv2.CHAIN_APPROX_SIMPLE,\n\n approxPolyDPvar=0.02,\n\n constant_subtracted=2,\n\n block_size=11,\n\n return_bw_pic=True,\n\n)\n\n#method2 (good results)\n\ndf, bw_pic = get_shapes_using_ADAPTIVE_THRESH_MEAN_C(\n\n im=image2.copy(),\n\n method=cv2.CHAIN_APPROX_SIMPLE,\n\n approxPolyDPvar=0.04,\n\n constant_subtracted=2,\n\n block_size=11,\n\n return_bw_pic=True,\n\n)\n\n\n\n#method3 (not always good results)\n\ndf, bw_pic = get_shapes_using_THRESH_OTSU(\n\n im=image2.copy(),\n\n method=cv2.CHAIN_APPROX_SIMPLE,\n\n approxPolyDPvar=0.01,\n\n kernel=(1, 1),\n\n start_thresh=50,\n\n end_thresh=255,\n\n return_bw_pic=True,\n\n)\n\n\n\n\n\n aa_arcLength aa_isContourConvex aa_center_x aa_center_y aa_area aa_convexHull aa_len_convexHull aa_len_cnts aa_shape aa_rotated_rectangle aa_minEnclosingCircle_center aa_minEnclosingCircle_radius aa_fitEllipse aa_fitLine aa_h0 aa_h1 aa_h2 aa_h3 aa_bound_start_x aa_bound_start_y aa_bound_end_x aa_bound_end_y aa_bound_width aa_bound_height\n\n0 1994.000000 True 286 212 242952.0 [[[573, 0]], [[573, 424]], [[0, 424]], [[0, 0]]] 4 4 rectangle [[0, 0], [573, 0], [573, 424], [0, 424]] (286, 212) 356 <NA> ((573, 212), (0, 212)) -1 -1 1 -1 0 0 574 425 574 425\n\n1 17.656854 True 511 398 23.0 [[[509, 397]], [[510, 396]], [[513, 396]], [[514, 397]], [[514, 400]], [[513, 401]], [[510, 401]], [[509, 400]]] 8 8 circle [[509, 396], [514, 396], [514, 401], [509, 401]] (511, 398) 2 ((511.5, 398.5), (5.830951690673828, 5.830951690673828), 0.0) ((573, 398), (0, 398)) 2 -1 -1 0 509 396 515 402 6 6\n\n2 67.213203 False 402 395 128.0 [[[396, 388]], [[406, 387]], [[409, 399]], [[405, 401]], [[397, 401]]] 5 7 oval [[395, 388], [407, 386], [409, 400], [397, 401]] (402, 394) 8 ((401.52850341796875, 394.2469482421875), (13.484291076660156, 29.70796012878418), 84.56861877441406) ((573, 1267), (0, -1646)) 3 1 -1 0 396 387 410 402 14 15\n\n3 88.183766 False 543 396 172.5 [[[538, 386]], [[551, 389]], [[550, 401]], [[543, 401]], [[535, 400]]] 5 10 oval [[535, 400], [538, 386], [552, 389], [549, 403]] (542, 394) 9 ((540.5974731445312, 393.8035888671875), (14.401185989379883, 27.078874588012695), 93.7881088256836) ((573, 561), (0, -2567)) 4 2 -1 0 535 386 552 402 17 16\n\n4 42.384776 True 530 393 122.5 [[[530, 386]], [[532, 386]], [[535, 389]], [[535, 397]], [[533, 400]], [[528, 401]], [[525, 398]], [[525, 392]], [[527, 388]]] 9 9 oval [[525, 401], [525, 386], [535, 386], [535, 401]] (530, 393) 7 ((530.2174682617188, 393.71160888671875), (11.07934856414795, 15.688089370727539), 10.645519256591797) ((573, -138), (0, 6796)) 6 3 5 0 525 386 536 402 11 16\n\n5 12.242641 False 530 393 7.5 [[[531, 395]], [[530, 396]], [[529, 395]], [[529, 393]], [[530, 391]], [[531, 391]]] 6 7 oval [[529, 391], [531, 391], [531, 396], [529, 396]] (530, 393) 2 ((530.2325439453125, 393.48016357421875), (2.533412456512451, 5.182924270629883), 12.179041862487793) ((573, -82), (0, 6120)) -1 -1 -1 4 529 391 532 397 3 6\n\n6 53.941125 False 520 393 102.0 [[[521, 386]], [[525, 389]], [[525, 396]], [[522, 400]], [[519, 401]], [[515, 398]], [[514, 394]], [[516, 389]]] 8 12 oval [[511, 395], [519, 384], [528, 391], [520, 402]] (520, 393) 7 ((519.7500610351562, 393.70819091796875), (10.821428298950195, 15.00129222869873), 12.044055938720703) ((573, 263), (0, 1652)) 7 4 -1 0 514 386 526 402 12 16\n\n7 124.911687 False 475 395 252.5 [[[471, 386]], [[477, 386]], [[488, 389]], [[487, 398]], [[480, 410]], [[475, 409]], [[466, 398]], [[467, 392]]] 8 15 oval [[463, 405], [469, 383], [489, 389], [483, 411]] (477, 397) 13 ((477.4767761230469, 396.48944091796875), (20.678686141967773, 25.679677963256836), 21.347288131713867) ((573, 781), (0, -1472)) 9 6 8 0 466 386 489 411 23 25\n\n8 10.242641 True 482 393 7.5 [[[484, 394]], [[483, 395]], [[482, 395]], [[481, 394]], [[481, 393]], [[482, 392]], [[484, 392]]] 7 7 circle [[481, 392], [484, 392], [484, 395], [481, 395]] (482, 393) 1 ((482.6419372558594, 393.3580627441406), (3.061401844024658, 3.7628066539764404), 45.0) ((573, 302), (0, 876)) -1 -1 -1 7 481 392 485 396 4 4\n\n9 81.112698 False 459 399 212.5 [[[465, 386]], [[466, 405]], [[463, 410]], [[458, 410]], [[454, 403]], [[455, 391]], [[458, 387]]] 7 11 oval [[453, 386], [465, 386], [466, 409], [454, 410]] (461, 398) 12 ((460.94775390625, 397.08856201171875), (13.2537202835083, 25.697481155395508), 2.9813687801361084) ((573, 2400), (0, -7608)) 11 7 10 0 454 386 467 411 13 25\n\n\n\n#Let's draw the results from the second picture\n\n#There is nothing better than Pandas to process data.\n\nimage = image2.copy()\n\nfor name, group in df.groupby(\"aa_h3\"):\n\n if name == 0:\n\n continue\n\n fabb = (\n\n np.random.randint(50, 250),\n\n np.random.randint(50, 250),\n\n np.random.randint(50, 250),\n\n )\n\n for key, item in group.loc[(group.aa_area > 200) & (\n\n group.aa_shape.isin(['rectangle', 'triangle', 'circle', 'pentagon', 'hexagon']))].iterrows():\n\n image = cv2.drawContours(\n\n image, item.aa_convexHull, -1, color=fabb, thickness=5, lineType=cv2.LINE_AA\n\n )\n\n image = cv2.rectangle(\n\n image,\n\n (item.aa_bound_start_x, item.aa_bound_start_y),\n\n (item.aa_bound_end_x, item.aa_bound_end_y),\n\n (0, 0, 0),\n\n 3,\n\n )\n\n image = cv2.rectangle(\n\n image,\n\n (item.aa_bound_start_x, item.aa_bound_start_y),\n\n (item.aa_bound_end_x, item.aa_bound_end_y),\n\n fabb,\n\n 2,\n\n )\n\n image = cv2.putText(\n\n image,\n\n f'{str(item.aa_shape)} - {name}',\n\n (item.aa_bound_start_x, item.aa_bound_start_y),\n\n cv2.FONT_HERSHEY_SIMPLEX,\n\n 0.4,\n\n (0, 0, 0),\n\n 2,\n\n cv2.LINE_AA,\n\n )\n\n image = cv2.putText(\n\n image,\n\n f'{str(item.aa_shape)} - {name}',\n\n (item.aa_bound_start_x, item.aa_bound_start_y),\n\n cv2.FONT_HERSHEY_SIMPLEX,\n\n 0.4,\n\n fabb,\n\n 1,\n\n cv2.LINE_AA,\n\n )\n\n\n\ncv2.imshow_thread([image, bw_pic])\n\n```\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Detecting shapes with OpenCV, and getting all the important information in a DataFrame",
"version": "0.11",
"split_keywords": [
"opencv",
"cv2",
"pandas",
"dataframe",
"shapes",
"object detection",
"shape"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a60da8e75361d7c6720993fcf5d9eb066ed9e9bf4faa0ca4808391ec32d0b734",
"md5": "56c67a7ac51b8ef1018e7f0a6f7a6147",
"sha256": "4fb3cbd98c05006bbe036abcdbce853d6d12e7f390a01b67e72838de3881c084"
},
"downloads": -1,
"filename": "a_cv2_shape_finder-0.11-py3-none-any.whl",
"has_sig": false,
"md5_digest": "56c67a7ac51b8ef1018e7f0a6f7a6147",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 12620,
"upload_time": "2022-11-13T03:14:09",
"upload_time_iso_8601": "2022-11-13T03:14:09.689209Z",
"url": "https://files.pythonhosted.org/packages/a6/0d/a8e75361d7c6720993fcf5d9eb066ed9e9bf4faa0ca4808391ec32d0b734/a_cv2_shape_finder-0.11-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4bfc9143f49188dc2efd7a93d842659ce49408277dd253ddb2a849c63e78c448",
"md5": "cb398bfe5e21e89232615cb6085b1d1a",
"sha256": "dd8039bd8ed250e530a82fa8c2af78ed4345b8a683177b67ae38031805ef675a"
},
"downloads": -1,
"filename": "a_cv2_shape_finder-0.11.tar.gz",
"has_sig": false,
"md5_digest": "cb398bfe5e21e89232615cb6085b1d1a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 9392,
"upload_time": "2022-11-13T03:14:11",
"upload_time_iso_8601": "2022-11-13T03:14:11.684623Z",
"url": "https://files.pythonhosted.org/packages/4b/fc/9143f49188dc2efd7a93d842659ce49408277dd253ddb2a849c63e78c448/a_cv2_shape_finder-0.11.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-11-13 03:14:11",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "hansalemaos",
"github_project": "a_cv2_shape_finder",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "a-cv2-shape-finder"
}
JSON
