https://github.com/dhchenx/quick-image
A simple image processing toolkit
https://github.com/dhchenx/quick-image
image-processing quick-image quick-image-processing
Last synced: 8 months ago
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A simple image processing toolkit
- Host: GitHub
- URL: https://github.com/dhchenx/quick-image
- Owner: dhchenx
- License: mit
- Created: 2022-09-02T07:30:48.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-09-02T07:32:13.000Z (almost 4 years ago)
- Last Synced: 2025-01-29T06:27:50.524Z (over 1 year ago)
- Topics: image-processing, quick-image, quick-image-processing
- Language: Python
- Homepage: https://pypi.org/project/quick-image/
- Size: 746 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Quick-Image: A simple image processing toolkit.
### Installation
```
pip install quick-image
```
## Examples
1. Basic Usage
```python
from quick_image import *
# quick_download_image(
# pic_url='https://pixnio.com/free-images/2022/07/21/2022-07-21-08-38-18-1350x900.jpg',
# save_path='flower.jpg')
# quick_show_image("flower.jpg")
# quick_show_image_by_grayscale("flower.jpg")
# quick_show_image_by_grayscale2("flower.jpg")
# quick_show_image_gray("flower.jpg")
# quick_convert_12bit_gray("flower.jpg","flower_12bit.jpg")
# quick_show_canny("flower.jpg")
# quick_replace_image_color("flower.jpg",show=True)
# quick_save_edges("flower.jpg","flower_edges.jpg",t=50)
# quick_filter_by_dist("flower.jpg",max_dist=1000)
'''
list_points,list_colors=quick_pick_image_color("flower.jpg","points.csv" ,"colors.csv")
print(list_points)
print(list_colors)
'''
# quick_remove_pix_color("flower.jpg",target_color= [203,152,125],save_path='flower_removed_color.jpg')
quick_remove_pix_color_by_range("flower.jpg",lower_color= np.array([100, 150, 0]),
upper_color=np.array([140, 255, 255]),show=True)
```
2. Remove noise
```python
from quick_image import *
from quick_image.quick_image_similarity_measures import *
quick_remove_noise1(image_path="flower.jpg",save_path="test4/output1.jpg")
# quick_remove_noise2(image_path="flower.jpg",save_path="test4/output2.jpg",min_size=5)
score_ssim = ssim('flower.jpg', 'test4/output1.jpg')
score_dvsim = dvsim('flower.jpg', 'test4/output1.jpg')
print(score_ssim)
print(score_dvsim)
```
3. Estimate color similarity
```python
from quick_image import *
from skimage import io
'''
find image color similarity
'''
# Example 1:
img_rgb = io.imread('flower.jpg')
green = [203,152,125]
s=get_pct_color(img_rgb, green, 10)
print("s=",s)
# Example 2:
base=[35,103,239]
test_color=[153,0,0]
test_color1=[0,128,255]
print(quick_color_similarity(base,test_color))
print(quick_color_similarity(base,test_color1))
```
4. Edge detection
```python
from quick_image.quick_image_processing import *
import time
time_cost={}
if __name__=="__main__":
image_path="flower.jpg"
# coords = load_polygon_file(f'datasets/areas/{gender}/{body_part}_polygon_area.pickle')
file_name="flower.jpg"
start=time.time()
# Using Canny algorithm (86)
detect_edges(img_path=image_path,save_path='test3_output/'+file_name)
time1=time.time()
# Using Canny algorithm with polygons
detect_edges_with_polygon(img_path=image_path, save_path='test3_output/' + file_name)
time2=time.time()
# Using single-color isolate algorithm
isolate_image(image_path=image_path,save_path='test3_output/'+file_name)
time3=time.time()
# Using multi-color isolate algorithm
isolate_image2(image_path=image_path, save_main_color='test3_output/' + file_name,
save_path='test3_output/' + file_name)
time4=time.time()
time_cost["canny"]=time1-start
time_cost["canny_polygon"]=time2-time1
time_cost["isolate1"]=time3-time2
time_cost["isolate2"]=time4-time3
print("Method\tTime cost")
for k in time_cost:
print(f"{k}\t{round(time_cost[k],4)}")
```
### License
The `quick-image` toolkit is provided by [Donghua Chen](https://github.com/dhchenx) with MIT License.