image_process.py

# This is a sample Python script.

# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.

import numpy as np
import cv2
import base64


def cv2_base64(image):
    base64_str = cv2.imencode('.png',image)[1].tobytes()
    base64_str = base64.b64encode(base64_str)
    return base64_str.decode('utf-8')

def base64_cv2(base64_str):
    imgString = base64.b64decode(base64_str)
    nparr = np.frombuffer(imgString,np.uint8)
    image = cv2.imdecode(nparr,cv2.IMREAD_COLOR)
    return image



def image_pose_mask(imagepath : str):
    img = base64_cv2(imagepath)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnt = contours[-1]
    hull = cv2.convexHull(cnt)
    length = len(hull)
    if length > 4:
        for i in range(length):
            cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
    mask = img.copy()
    kernel = np.ones((30, 30), dtype=np.uint8)
    dilated = cv2.dilate(threshold, kernel, 20)
    dilated = cv2.dilate(dilated, kernel, 20)
    contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    valid = len(contours) > 0

    area = []

    for k in range(len(contours)):
        area.append(cv2.contourArea(contours[k]))
    max_idx = np.argmax(np.array(area))

    mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)
    img_base64 = cv2_base64(mask2)

    return img_base64



def image_pose_mask_numpy(image):
    img = np.uint8(image)
    print("shape:" + str(img.shape))

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)

    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnt = contours[-1]
    hull = cv2.convexHull(cnt)
    length = len(hull)
    if length > 4:
        for i in range(length):
            cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
    mask = img.copy()
    kernel = np.ones((30, 30), dtype=np.uint8)

    dilated = cv2.dilate(threshold, kernel, 20)

    dilated = cv2.dilate(dilated, kernel, 20)
    contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    valid = len(contours) > 0

    area = []

    for k in range(len(contours)):
        area.append(cv2.contourArea(contours[k]))
    max_idx = np.argmax(np.array(area))

    mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)

    img_base64 = cv2_base64(mask2)

    return img_base64

def image_canny(imagepath:str):

    img = base64_cv2(imagepath)

    image_map  = cv2.Canny(img, 100, 200)

    contours, hierarchy = cv2.findContours(image_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnt = contours[-1]
    hull = cv2.convexHull(cnt)
    length = len(hull)
    if length > 4:
        for i in range(length):
            cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (0, 0, 255), 2)

    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    valid = len(contours) > 0
    area = []
    for k in range(len(contours)):
        area.append(cv2.contourArea(contours[k]))
    max_idx = np.argmax(np.array(area))

    mask2 = cv2.drawContours(image_map, contours, max_idx, (0, 0, 255), thickness=3)

    img_base64 = cv2_base64(mask2)

    return img_base64

# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    image_canny(imagepath='download (13).png')