Spaces:
Runtime error
Runtime error
File size: 4,634 Bytes
ec99c40 65e03a2 ec99c40 65e03a2 ec99c40 65e03a2 ec99c40 65e03a2 ec99c40 65e03a2 ec99c40 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
import streamlit as st
import numpy as np
import cv2
def preprocess_image(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 15, 7)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
opening = cv2.morphologyEx(threshold, cv2.MORPH_OPEN, kernel, iterations=1)
return opening
def four_point_transform(image, pts):
rect = np.zeros((4, 2), dtype=np.float32)
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
(tl, tr, br, bl) = rect
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype=np.float32)
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
return warped
def perspective_correction(image, threshold_value=100, min_line_length=100, max_line_gap=10, length_ratio=1, width_ratio=1):
processed_image = preprocess_image(image)
lines = cv2.HoughLinesP(
processed_image,
rho=1,
theta=np.pi/180,
threshold=threshold_value,
minLineLength=min_line_length,
maxLineGap=max_line_gap
)
if len(lines) < 1:
st.write("No lines found.")
return image
endpoints = []
for line in lines:
x1, y1, x2, y2 = line[0]
endpoints.append((x1, y1))
endpoints.append((x2, y2))
if len(endpoints) >= 4:
endpoints = np.array(endpoints)
corrected_image = four_point_transform(image, endpoints)
# Calculate the desired dimensions based on the length and width ratio
desired_width = int(corrected_image.shape[1] * width_ratio)
desired_length = int(corrected_image.shape[0] * length_ratio)
# Resize the image to the desired dimensions
corrected_image = cv2.resize(corrected_image, (desired_width, desired_length))
else:
st.write("Insufficient endpoints found.")
return image
return corrected_image
def perspective_correction2(image, threshold_value=100, min_line_length=100, max_line_gap=10, length_ratio=1, width_ratio=1):
processed_image = preprocess_image(image)
lines = cv2.HoughLinesP(
processed_image,
rho=1,
theta=np.pi/180,
threshold=threshold_value,
minLineLength=min_line_length,
maxLineGap=max_line_gap
)
if len(lines) < 1:
st.write("No lines found.")
return image
endpoints = []
for line in lines:
x1, y1, x2, y2 = line[0]
endpoints.append((x1, y1))
endpoints.append((x2, y2))
if len(endpoints) >= 4:
endpoints = np.array(endpoints)
hull = cv2.convexHull(endpoints)
rect = np.zeros((4, 2), dtype=np.float32)
hull = hull.reshape(-1, 2)
rect[0] = hull[np.argmin(hull.sum(axis=1))]
rect[2] = hull[np.argmax(hull.sum(axis=1))]
rect[1] = hull[np.argmin(np.diff(hull, axis=1))]
rect[3] = hull[np.argmax(np.diff(hull, axis=1))]
widthA = np.sqrt(((rect[2][0] - rect[3][0]) ** 2) + ((rect[2][1] - rect[3][1]) ** 2))
widthB = np.sqrt(((rect[1][0] - rect[0][0]) ** 2) + ((rect[1][1] - rect[0][1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((rect[1][0] - rect[2][0]) ** 2) + ((rect[1][1] - rect[2][1]) ** 2))
heightB = np.sqrt(((rect[0][0] - rect[3][0]) ** 2) + ((rect[0][1] - rect[3][1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# Calculate the desired dimensions based on the length and width ratio
desired_width = int(maxWidth * width_ratio)
desired_length = int(maxHeight * length_ratio)
dst = np.array([[0, 0], [desired_width - 1, 0], [desired_width - 1, desired_length - 1], [0, desired_length - 1]], dtype=np.float32)
M, _ = cv2.findHomography(rect, dst)
corrected_image = cv2.warpPerspective(image, M, (desired_width, desired_length))
else:
st.write("Insufficient endpoints found.")
return image
return corrected_image |