mein Skript Wollen Sie auf dem Originalbild arbeiten anstelle der Größe geänderte Bild

Question

Frage: Ich brauche jemanden
zu beantworten Was benötige ich für die unten Skript zu ändern, auf dem Originalbild anstelle des skalierte Bild zu arbeiten?

Info zu helfen, mein Problem
Die Bilder stammen aus einem Scanner zu lösen und enthalten 2 oder 3 receipes Schriftgut auf 3 „x5“ Karteikarten.

Das Skript identifiziert jede Rezeptur korrekt und speichert sie in einzelnen Dateien. Leider ist der Text in den neuen Bildern zu klein für mein OCR-Skript, um die handgeschriebenen Rezepte genau zu lesen.

Die Entfernung der img = cv2.resize() Linie (Linie 54) Ergebnisse in „Indexerror: Listenindex außerhalb des zulässigen Bereichs“ von def transform() auf der Leitung geworfen 33.

# Original Script: https://www.quora.com/How-can-I-detect-an-object-from-static-image-and-crop-it-from-the-image-using-openCV 

import cv2 
import numpy as np 
import os 

def transform(pos): 
    # This function is used to find the corners of the object and the dimensions of the object 

    pts=[] 
    n=len(pos) 

    for i in range(n): 
     pts.append(list(pos[i][0])) 

    sums={} 
    diffs={} 
    tl=tr=bl=br=0 

    for i in pts: 
     x=i[0] 
     y=i[1] 
     sum=x+y 
     diff=y-x 
     sums[sum]=i 
     diffs[diff]=i 

    sums=sorted(sums.items()) 
    diffs=sorted(diffs.items()) 
    n=len(sums) 

    # The IndexError occurs on this line 
    rect=[sums[0][1],diffs[0][1],diffs[n-1][1],sums[n-1][1]] 
    #  top-left top-right bottom-left bottom-right 

    h1 = np.sqrt((rect[0][0]-rect[2][0])**2 + (rect[0][1]-rect[2][1])**2)  #height of left side 
    h2 = np.sqrt((rect[1][0]-rect[3][0])**2 + (rect[1][1]-rect[3][1])**2)  #height of right side 
    h = max(h1, h2) 

    w1 = np.sqrt((rect[0][0]-rect[1][0])**2 + (rect[0][1]-rect[1][1])**2)  #width of upper side 
    w2 = np.sqrt((rect[2][0]-rect[3][0])**2 + (rect[2][1]-rect[3][1])**2)  #width of lower side 
    w = max(w1, w2) 

    return int(w), int(h), rect 

img = cv2.imread('source_image.png') 

# 
# Resizing of image is done here 
# 
# Removal of these lines results in "IndexError: list index out of range" being displayed 
r = 500.0/img.shape[1] 
dim = (500, int(img.shape[0] * r)) 
img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
gray = cv2.GaussianBlur(gray, (11,11), 0) 
edge = cv2.Canny(gray, 100, 200) 
_, contours, _ = cv2.findContours(edge.copy(), 1, 1) 

# Give each output image a unique name 
loopcnt = 0 
for pos in contours: 
    peri = cv2.arcLength(pos, True) 
    approx = cv2.approxPolyDP(pos, 0.02 * peri, True) 

    w, h, arr = transform(approx) 

    if w > 0 and h > 0: 

     pts2 = np.float32([[0, 0], [w, 0], [0, h], [w, h]]) 
     pts1 = np.float32(arr) 
     M = cv2.getPerspectiveTransform(pts1, pts2) 
     dst = cv2.warpPerspective(img, M, (w, h)) 
     image = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY) 
     cv2.imwrite("output_image_"+str(loopcnt)+".png", image) 

     loopcnt+=1 

Answer 1

Hier ist meine Lösung.

Das Skript funktioniert jeweils für ein Bild. Wenn Sie mehrere Bilder verarbeiten müssen, müssen Sie dieses Skript einmal pro Bild aufrufen.

# Original Script: https://www.quora.com/How-can-I-detect-an-object-from-static-image-and-crop-it-from-the-image-using-openCV 

########## 
# Process a scanned image and place each receipe card into its own image file. 
# 
# WARNING: This script will overwrite existing files when saving output images. 
# 
# Created: 2017-12-29 1148 
# Modified: 2017-12-31 1358 
########## 

import cv2 
import numpy as np 
import math 
import argparse 
import os 

def transform(pos): 
    # This function is used to find the corners and dimensions of the object 

    pts=[] 
    n=len(pos) 

    for i in range(n): 
     pts.append(list(pos[i][0])) 

    sums={} 
    diffs={} 
    tl=tr=bl=br=0 

    for i in pts: 
     x=i[0] 
     y=i[1] 
     sum=x+y 
     diff=y-x 
     sums[sum]=i 
     diffs[diff]=i 

    sums=sorted(sums.items()) 
    diffs=sorted(diffs.items()) 
    n=len(sums) 

    rect=[sums[0][1],diffs[0][1],diffs[n-1][1],sums[n-1][1]] 
    #  top-left top-right bottom-left bottom-right 

    h1 = np.sqrt((rect[0][0]-rect[2][0])**2 + (rect[0][1]-rect[2][1])**2)  #height of left side 
    h2 = np.sqrt((rect[1][0]-rect[3][0])**2 + (rect[1][1]-rect[3][1])**2)  #height of right side 
    h = max(h1, h2) 

    w1 = np.sqrt((rect[0][0]-rect[1][0])**2 + (rect[0][1]-rect[1][1])**2)  #width of upper side 
    w2 = np.sqrt((rect[2][0]-rect[3][0])**2 + (rect[2][1]-rect[3][1])**2)  #width of lower side 
    w = max(w1, w2) 

    return int(w), int(h), rect 

def file_choices(fname): 

    # List of valid file extensions 
    choices = ('bmp', 'dib' ,'jpeg' ,'jpg' ,'jpe' ,'jp2' ,'png' ,'webp' ,'pbm' ,'pgm' ,'ppm' ,'sr' ,'ras' ,'tiff' ,'tif') 

    # Get file extension 
    ext = os.path.splitext(fname)[1][1:] 

    # Check if extension is valid 
    if ext not in choices: 
     ap.error("File doesn't end with one of {}".format(choices)) 

    return fname 

## 
## Main program starts here 
## 

ap = argparse.ArgumentParser() 
ap.add_argument("-i", "--image", required=True, help="Path to source image", type=lambda s:file_choices(s)) 
ap.add_argument("-c", "--color", help="If set, output images will be in color if original image is in color. Default is to return grayscale (black & white) images.", action="store_true") 
args = ap.parse_args() 

srcimage = args.image 

# Save output images in same directory as srcimage 
destpath = os.path.dirname(os.path.abspath(srcimage)) 

# -Should the output images be in color or grayscale? 
# -Note: If the source image is alread in grayscale, this setting 
#  will have no effect on the output images. 
if args.color: 
    returncolor = True 
else: 
    returncolor = False 

# Load in the source image 
imgorig = cv2.imread(srcimage) 

# Resizing of image is done here to speed up processing 
ratio = 500.0/imgorig.shape[1] 
dim = (500, int(imgorig.shape[0] * ratio)) 
img = cv2.resize(imgorig, dim, interpolation = cv2.INTER_AREA) 

# Convert to grayscale 
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

# Add a blur to remove some of the noise 
# Image noise is random variation of brightness or color. 
# More info: https://en.wikipedia.org/wiki/Image_noise 
gray = cv2.GaussianBlur(gray, (11,11), 0) 

# Find the contours of the receipe cards 
edge = cv2.Canny(gray, 100, 200) 
_, contours, _ = cv2.findContours(edge.copy(), 1, 1) 

# Give each output image a unique name 
loopcnt = 0 

# Process all found contours 
for pos in contours: 

    # Get length of the contour in pixels 
    # peri is a float 
    peri = cv2.arcLength(pos, True) 

    # Approximates a polygonal curve(s) with the specified precision 
    # More info: https://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#approxpolydp 
    approx = cv2.approxPolyDP(pos, 0.02 * peri, True) 

    # Find the corners and dimensions of the object 
    w, h, arr = transform(approx) 

    # Only process contours that have a valid dimension 
    if w > 0 and h > 0: 

     # Adjust width and height to match dimensions of 
     # each receipe card on the original image 
     wr = int(w/ratio) 
     hr = int(h/ratio) 

     # Adjust pixel coordinates to match orignal image 
     arr_us=[] 
     for a in arr: 
      a[0] = int(math.floor(a[0]/ratio)) 
      a[1] = int(math.floor(a[1]/ratio)) 
      arr_us.append(list(a)) 

     arr = arr_us 

     # Convert all of the numbers to floats 
     pts1 = np.float32(arr) 
     pts2 = np.float32([[0, 0], [wr, 0], [0, hr], [wr, hr]]) 

     # Changes perspective to a top-down view (a.k.a.: birds eye view) 
     M = cv2.getPerspectiveTransform(pts1, pts2) 
     dst = cv2.warpPerspective(imgorig, M, (wr, hr)) 

     if returncolor: 
      # Keep original image colors in output images 
      image = dst 
     else: 
      # Convert output images to grayscale before saving 
      image = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY) 

     # Save each receipe card to individual image files 
     # WARNING: This will overwrite existing files. 
     cv2.imwrite(destpath + "/output_"+str(loopcnt)+".png", image) 

    loopcnt+=1