### Description

Adds opencv-python and ffmpeg.

### Status

opencv-python is modularized with a bunch of modules, I'll keep updating the status.

- [x] Core library
- [x] JPEG
- [x] PNG
- [x] WEBP
- [x] Image processing
- [x] video processing (FFMPEG)
- [x] objectdetect
- [x] dnn
- [x] ml
- [x] calib3d
- [x] feature2d
- [x] highgui (built, but nonsense)
- [x] photo
- [x] stitching
- [x] flann
- contrib algorithms?
- extra modules?

### Checklists

<!-- Note on checklists:
     If you think some of these steps are not necessary for your PR,
     just remove those checkboxes, or mark them as checked. Otherwise,
     if some checkboxes are left unmarked, we might assume that your PR
     is not ready to be merged and we might keep you waiting -->

- [x] Add a [CHANGELOG](https://github.com/pyodide/pyodide/blob/main/docs/project/changelog.md) entry
- [x] Add / update tests

Resolve #381
Resolve #1627
PKG Add opencv-python and ffmpeg. ### Description

Adds opencv-python and ffmpeg.

### Status

opencv-python is modularized with a bunch of modules, I'll keep updating the status.

- [x] Core library
- [x] JPEG
- [x] PNG
- [x] WEBP
- [x] Image processing
- [x] video processing (FFMPEG)
- [x] objectdetect
- [x] dnn
- [x] ml
- [x] calib3d
- [x] feature2d
- [x] highgui (built, but nonsense)
- [x] photo
- [x] stitching
- [x] flann
- contrib algorithms?
- extra modules?

### Checklists

<!-- Note on checklists:
     If you think some of these steps are not necessary for your PR,
     just remove those checkboxes, or mark them as checked. Otherwise,
     if some checkboxes are left unmarked, we might assume that your PR
     is not ready to be merged and we might keep you waiting -->

- [x] Add a [CHANGELOG](https://github.com/pyodide/pyodide/blob/main/docs/project/changelog.md) entry
- [x] Add / update tests

Resolve #381
Resolve #1627
# setup: import numpy as np; image = np.zeros((64, 32), dtype = np.uint8)
# run: mandel(-2.0, 1.0, -1.0, 1.0, image, 20)

# pythran export mandel(float, float, float, float, uint8[][], int)


def kernel(x, y, max_iters):
    """
    Given the real and imaginary parts of a complex number,
    determine if it is a candidate for membership in the Mandelbrot
    set given a fixed number of iterations.
    """
    c = complex(x, y)
    z = 0.0j
    for i in range(max_iters):
        z = z * z + c
        if (z.real * z.real + z.imag * z.imag) >= 4:
            return i

    return max_iters


def mandel(min_x, max_x, min_y, max_y, image, iters):
    height = image.shape[0]
    width = image.shape[1]

    pixel_size_x = (max_x - min_x) / width
    pixel_size_y = (max_y - min_y) / height

    for x in range(width):
        real = min_x + x * pixel_size_x
        for y in range(height):
            imag = min_y + y * pixel_size_y
            color = kernel(real, imag, iters)
            image[y, x] = color

# setup: import numpy as np; image = np.zeros((64, 32), dtype = np.uint8)
# run: mandel(-2.0, 1.0, -1.0, 1.0, image, 20)

# pythran export mandel(float, float, float, float, uint8[][], int)


def kernel(x, y, max_iters):
    """
    Given the real and imaginary parts of a complex number,
    determine if it is a candidate for membership in the Mandelbrot
    set given a fixed number of iterations.
    """
    c = complex(x, y)
    z = 0.0j
    for i in range(max_iters):
        z = z * z + c
        if (z.real * z.real + z.imag * z.imag) >= 4:
            return i

    return max_iters


def mandel(min_x, max_x, min_y, max_y, image, iters):
    height = image.shape[0]
    width = image.shape[1]

    pixel_size_x = (max_x - min_x) / width
    pixel_size_y = (max_y - min_y) / height

    for x in range(width):
        real = min_x + x * pixel_size_x
        for y in range(height):
            imag = min_y + y * pixel_size_y
            color = kernel(real, imag, iters)
            image[y, x] = color
