相机实时进行透视变换矫正 并用streamlit做个界面

import cv2
import numpy as np
 
def get_rectangle_corners(image, resize_height, prev_rectangle=None, stability_factor=12):
    def process_image(image, height):
        ratio = image.shape[0] / height
        image = cv2.resize(image, (int(image.shape[1] / ratio), height))
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (3, 3), 0)
        edges = cv2.Canny(gray, 25, 125)
        return edges, ratio
 
    def find_rectangle(edges):
        contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5]
 
        for contour in contours:
            peri = cv2.arcLength(contour, True)
            approx = cv2.approxPolyDP(contour, 0.02 * peri, True)
 
            if len(approx) == 4:
                return approx
 
    edges, ratio = process_image(image, resize_height)
    rectangle = find_rectangle(edges)
 
    if rectangle is not None:
        rectangle = rectangle.reshape(4, 2) * ratio
        rectangle = [[int(point[0]), int(point[1])] for point in rectangle]
 
        # if prev rectangle exists and the change is smaller than the stability factor, use the previous rectangle
        if prev_rectangle is not None:
            diff = np.abs(np.array(prev_rectangle) - np.array(rectangle))
            if np.all(diff < stability_factor):
                return prev_rectangle
 
        return rectangle
    else:
        return prev_rectangle  # if no rectangle detected, use the previous one
prev_rectangle = None
dst = np.float32([[0, 0], [0, 488],[337, 488],[337, 0]])
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if ret:
        rectangle = get_rectangle_corners(frame, 500, prev_rectangle)
        prev_rectangle = rectangle  # save the rectangle for the next frame
        print(rectangle)
        if rectangle is not None:
            src = np.float32(rectangle)
            m = cv2.getPerspectiveTransform(src, dst)
            result = cv2.warpPerspective(frame, m, (337, 488))
 
            cv2.imshow("src", frame)
            cv2.imshow("result", result)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

streamlit界面

import streamlit as st
from cnstd import CnStd
from cnocr import CnOcr
import cv2
import numpy as np
import pandas as pd
from PIL import Image
 
def get_rectangle_corners(image, resize_height, prev_rectangle=None, stability_factor=12):
    def process_image(image, height):
        ratio = image.shape[0] / height
        image = cv2.resize(image, (int(image.shape[1] / ratio), height))
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (3, 3), 0)
        edges = cv2.Canny(gray, 50, 100)
        return edges, ratio
 
    def find_rectangle(edges):
        contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        contours = sorted(contours, key=cv2.contourArea, reverse=True)[:1]
 
        for contour in contours:
            peri = cv2.arcLength(contour, True)
            approx = cv2.approxPolyDP(contour, 0.02 * peri, True)
 
            if len(approx) == 4:
                return approx
 
    edges, ratio = process_image(image, resize_height)
    rectangle = find_rectangle(edges)
 
    if rectangle is not None:
        rectangle = rectangle.reshape(4, 2) * ratio
        rectangle = [[int(point[0]), int(point[1])] for point in rectangle]
 
 
        if prev_rectangle is not None:
            diff = np.abs(np.array(prev_rectangle) - np.array(rectangle))
            if np.all(diff < stability_factor):
                return prev_rectangle
 
        return rectangle
    else:
        return prev_rectangle  # if no rectangle detected, use the previous one
 
# Code where the two scripts are fused together
dst = np.float32([[0, 0], [0, 588],[640, 588],[640, 0]])
prev_rectangle = None
cap = cv2.VideoCapture(0)
std = CnStd()
cn_ocr = CnOcr()
 
st.sidebar.title("易读写")
option = st.sidebar.selectbox('请选择要运行的功能', ('显示矫正后画面', '图片转文字'))
# 定义两个图像显示窗口
FRAME_WINDOW_RAW = st.image([])
FRAME_WINDOW_TRANSFORMED = st.image([])
# 需要实时显示矫正后画面
if option == '显示矫正后画面':
    run = st.checkbox('开始运行')
    FRAME_WINDOW = st.image([])
    alpha = st.sidebar.slider('亮度调节 Brightness', min_value=0.0, max_value=3.0,
                              value=1.0)  # Add a slider for brightness
    angle = st.sidebar.slider('视角调节 Rotation', 0, 360, 0)  # Add a slider for rotation
    if run:
        while True:
            ret, frame = cap.read()
            if not ret:
                st.write("Can't receive frame (Stream end?). Exiting..")
                break
            rectangle = get_rectangle_corners(frame, 500, prev_rectangle)
            prev_rectangle = rectangle  # save the rectangle for the next frame
            if rectangle is not None:
                src = np.float32(rectangle)
                m = cv2.getPerspectiveTransform(src, dst)
                result = cv2.warpPerspective(frame, m, (640, 588))
 
                raw_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB for raw frame
                img_raw = Image.fromarray(raw_frame)
                FRAME_WINDOW_RAW.image(img_raw)  # Show raw frame
 
                result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB for transformed
                result = cv2.convertScaleAbs(result, alpha=alpha, beta=0)
                img_transformed = Image.fromarray(result)
                FRAME_WINDOW_TRANSFORMED.image(img_transformed)  # Show transformed result
    else:
        st.write('Stopped')
    cap.release()
 
elif option == '图片转文字':
    uploaded_file = st.sidebar.file_uploader("选择一个图片文件")
 
    if uploaded_file is not None:
        img = Image.open(uploaded_file)
        st.image(img, caption='Uploaded Image.', use_column_width=True)
 
        if st.sidebar.button('开始运行'):
            # Copy and adapt your code here
            with st.spinner('OCR is in progress...'):
                np_img = np.array(img)  # Convert PIL Image to numpy array
                box_infos = std.detect(np_img)
 
                text = []
                for box_info in box_infos['detected_texts']:
                    cropped_img = box_info['cropped_img']
                    ocr_res = cn_ocr.ocr_for_single_line(cropped_img)
                    text.append(ocr_res['text'])
 
                st.subheader("Extracted text:")
                for line in text:
                    st.write(line)
 
            st.success('Text extraction complete')