Question: I have this image for a treeline crop. I need to find the general direction in which the crop is aligned. I'm trying to get the Hough lines of the image, and then find the mode of distribution of angles. I've been following this tutorialon crop lines, however in that one, the crop lines are sparse. Here they are densely pack, and after grayscaling, blurring, and using canny edge detection, this is what i get import cv2 import numpy as np import matplotlib.pyplot as plt img = cv2.imread('drive/MyDrive/tree/sample.jpg') gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) gauss = cv2.GaussianBlur(gray, (3,3), 3) plt.figure(figsize=(15,15)) plt.subplot(1,2,1) plt.imshow(gauss) gscale = cv2.Canny(gauss, 80, 140) plt.subplot(1,2,2) plt.imshow(gscale) plt.show()  (Left side blurred image without canny, left one preprocessed with canny)  After that, I followed the tutorial and "skeletonized" the preprocessed image size = np.size(gscale) skel = np.zeros(gscale.shape, np.uint8) ret, gscale = cv2.threshold(gscale, 128, 255,0) element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3)) done = False while not done: eroded = cv2.erode(gscale, element) temp = cv2.dilate(eroded, element) temp = cv2.subtract(gscale, temp) skel = cv2.bitwise_or(skel, temp) gscale = eroded.copy() zeros = size - cv2.countNonZero(gscale) if zeros==size: done = True  Giving me  As you can see, there are a bunch of curvy lines still. When using the HoughLines algorithm on it, there are 11k lines scattered everywhere lines = cv2.HoughLinesP(skel,1,np.pi/180,130) a,b,c = lines.shape for i in range(a): rho = lines[i][0][0] theta = lines[i][0][1] a = np.cos(theta) b = np.sin(theta) x0 = a*rho y0 = b*rho x1 = int(x0 + 1000*(-b)) y1 = int(y0 + 1000*(a)) x2 = int(x0 - 1000*(-b)) y2 = int(y0 - 1000*(a)) cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2, cv2.LINE_AA)#showing the results: plt.figure(figsize=(15,15)) plt.subplot(121)#OpenCV reads images as BGR, this corrects so it is displayed as RGB plt.plot() plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.title('Row Detection') plt.xticks([]) plt.yticks([]) plt.subplot(122) plt.plot() plt.imshow(skel,cmap='gray') plt.title('Skeletal Image') plt.xticks([]) plt.yticks([]) plt.show()   I am a newbie when it comes to cv2, so I have 0 clue what to do. Searched and tried a bunch of stuff but none works. How can I remove the mildly big dots, and remove the squiggly lines?

Answer: You can use a 2D FFT to find the general direction in which the crop is aligned (as proposed by mozway in the comments). The idea is that the general direction can be easily extracted from centred beaming rays appearing in the magnitude spectrum when the input contains many lines in the same direction. You can find more information about how it works in this previous post. It works directly with the input image, but it is better to apply the Gaussian + Canny filters. Here is the interesting part of the magnitude spectrum of the filtered gray image:  The main beaming ray can be easily seen. You can extract its angle by iterating over many lines with an increasing angle and sum the magnitude values on each line as in the following figure:  Here is the magnitude sum of each line plotted against the angle (in radian) of the line:  Based on that, you just need to find the angle that maximize the computed sum. Here is the resulting code: def computeAngle(arr): # Naive inefficient algorithm n, m = arr.shape yCenter, xCenter = (n-1, m//2-1) lineLen = m//2-2 sMax = 0.0 bestAngle = np.nan for angle in np.arange(0, math.pi, math.pi/300): i = np.arange(lineLen) y, x = (np.sin(angle) * i + 0.5).astype(np.int_), (np.cos(angle) * i + 0.5).astype(np.int_) s = np.sum(arr[yCenter-y, xCenter+x]) if s > sMax: bestAngle = angle sMax = s return bestAngle # Load the image in gray img = cv2.imread('lines.jpg') gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # Apply some filters gauss = cv2.GaussianBlur(gray, (3,3), 3) gscale = cv2.Canny(gauss, 80, 140) # Compute the 2D FFT of real values freqs = np.fft.rfft2(gscale) # Shift the frequencies (centering) and select the low frequencies upperPart = freqs[:freqs.shape[0]//4,:freqs.shape[1]//2] lowerPart = freqs[-freqs.shape[0]//4:,:freqs.shape[1]//2] filteredFreqs = np.vstack((lowerPart, upperPart)) # Compute the magnitude spectrum magnitude = np.log(np.abs(filteredFreqs)) # Correct the angle magnitude = np.rot90(magnitude).copy() # Find the major angle bestAngle = computeAngle(magnitude)