gabrielburnworth/plant-detection

Detects and marks plants in a soil area image using Python OpenCV

plant-detection's Issues

Coordinate conversion implemented

Conversion from pixel locations to machine coordinates has been implemented.

Usage:

detect_plants("soil_image.jpg", blur=15, morph=6, iterations=4,
             calibration_img="pixel_to_coordinate/p2c_test_calibration.jpg",
             known_plants=[[800, 200, 100], [1130, 600, 120]])

Example output shown below used a rotated calibration image.

Example image output:

Green = known plant
Blue = saved plant
Red = plant marked for removal

Example text output:

Processing image: soil_image.jpg
14 plants detected in image.
Detected object machine coordinates ( X Y ) with R = radius:
    (  1224   115 ) R = 28
    (   677   126 ) R = 33
    (   826   147 ) R = 28
    (  1077   220 ) R = 21
    (   870   238 ) R = 23
    (  1132   280 ) R = 24
    (  1178   317 ) R = 5
    (   950   438 ) R = 27
    (  1291   473 ) R = 31
    (   777   507 ) R = 31
    (   420   552 ) R = 32
    (  1140   602 ) R = 53
    (   303   644 ) R = 23
    (   309   843 ) R = 32

2 known plants inputted.
Plants at the following machine coordinates ( X Y ) with R = radius are to be saved:
    (   800   200 ) R = 100
    (  1130   600 ) R = 120

11 plants marked for removal.
Plants at the following machine coordinates ( X Y ) with R = radius are to be removed:
    (  1224   115 ) R = 49
    (   677   126 ) R = 57
    (  1077   220 ) R = 35
    (  1132   280 ) R = 41
    (  1178   317 ) R = 8
    (   950   438 ) R = 47
    (  1291   473 ) R = 54
    (   777   507 ) R = 53
    (   420   552 ) R = 55
    (   303   644 ) R = 39
    (   309   843 ) R = 55

3 detected plants are known or have escaped removal.
Plants at the following machine coordinates ( X Y ) with R = radius have been saved:
    (   826   147 ) R = 47
    (   870   238 ) R = 39
    (  1140   602 ) R = 92

Can I figure out the proportion of plants

I need to identify the proportion of plants in the picture，What do I need to do？

New experimental options

Comparison between normal GUI mode (left) and GUI with clump buster and soil grey-out enabled (right).
Still need to implement a GUI option for clump buster and a GUI option and keyword argument for soil grey-out.

Manual selection of morph amount required

The detect_weeds function to process an image uses a default or selected morph amount. This value usually needs to be optimized for each image to:

• Avoid marking multiple weeds close to each other as one weed with a center between the two
• Avoid marking a void inside a weed as a separate new weed

Sometimes there is not an optimum solution using the current approach of a closing morphology (as described here).

Multiple plant error:

Void false positive:

Both errors:

ModuleNotFoundError

I have a problem with module. First I have no error however now I have module error. How can I fix this?
ModuleNotFoundError: No module named 'plant_detection'

Crop counting using plant-detection in aerial image.

Dear All,
I intend to apply plant-detection for counting number of banana plantation in an aerial image taken above 50-60 m above the ground. Can this be fit for my purpose? Has anyone applied this for similar purpose using Drones?

I look forward to hear from you.
Best,
Suman

Safe-remove feature added

See What's a weed? wiki page for explanation and example.

[bug] Camera Calibration crashes in env_var_converter

This issue in FarmBot Forum describes the problem(s).

Here's a screenshot of the Raspberry Pi Serial Console when I try CALIBRATE in the WebApp Farmware Camera Calibration ( I'm running FarmbotOS v8.2.4-rc1 on Raspberry Pi 3 Model B Plus with USB Camera

[    6.454538] uvcvideo: Found UVC 1.00 device C922 Pro Stream Webcam (046d:085c)

which captures stills nicely using take-photo :-)

Thanks

kernel is not using morph_amount

I tried changing morph_amount from the default of 5 to some extremes: 1, 10, 100, and 1,000. There were no apparent changes in the output images.

Then I changed this line: kernel = np.ones((morph_amount, morph_amount), np.uint8)

To: kernel = np.ones((1000, 1000), np.uint8) and that took effect.