pls

python
from PIL import Image
import matplotlib.pyplot as plt

img = Image.open('/mnt/data/Screenshot 2025-06-04 12.25.54 PM.png')
plt.imshow(img)
plt.axis('off')

(-0.5, 603.5, 484.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((200,100,650,650)))
plt.axis('off')

(-0.5, 449.5, 549.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((230,126,420,315)))
plt.axis('off')

(-0.5, 189.5, 188.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((240,150,380,280)))
plt.axis('off')

(-0.5, 139.5, 129.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((250,160,330,240)))
plt.axis('off')

(-0.5, 79.5, 79.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((260,170,310,220)))
plt.axis('off')

(-0.5, 49.5, 49.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((210,100,430,320)))
plt.axis('off')

(-0.5, 219.5, 219.5, -0.5)

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((210,100,260,320)))
plt.axis('off')

(-0.5, 49.5, 219.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((210,240,430,280)))
plt.axis('off')

(-0.5, 219.5, 39.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((210,100,430,320)))
plt.axis('off')
plt.show()

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((250,160,390,305)))
plt.axis('off')

(-0.5, 139.5, 144.5, -0.5)

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((350,100,500,250)))
plt.axis('off')

(-0.5, 149.5, 149.5, -0.5)

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((320,60,420,190)))
plt.axis('off')

(-0.5, 99.5, 129.5, -0.5)

python
plt.figure(figsize=(6,6))
plt.imshow(img.crop((180,80,480,380)))
plt.axis('off')

(-0.5, 299.5, 299.5, -0.5)

python
plt.figure(figsize=(6,2))
plt.imshow(img.crop((180,190,480,260)))
plt.axis('off')

(-0.5, 299.5, 69.5, -0.5)

python
plt.figure(figsize=(3,4))
plt.imshow(img.crop((220,80,280,300)))
plt.axis('off')

(-0.5, 59.5, 219.5, -0.5)

python
plt.figure(figsize=(4,4))
plt.imshow(img.crop((260,135,370,210)))
plt.axis('off')

(-0.5, 109.5, 74.5, -0.5)

python
plt.figure(figsize=(6,6))
plt.imshow(img.crop((220,110,370,260)))
plt.axis('off')

(-0.5, 149.5, 149.5, -0.5)

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((250,150,290,190)))
plt.axis('off')

(-0.5, 39.5, 39.5, -0.5)

python
plt.figure(figsize=(3,3))
plt.imshow(img.crop((285,150,315,190)))
plt.axis('off')

(-0.5, 29.5, 39.5, -0.5)

python
import numpy as np
import cv2
import matplotlib.pyplot as plt

# load grayscale version
gray = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY)
# find vertical gray axis maybe darker grey line without black.
# Let's find column with many dark pixels horizontally across mix.
# compute vertical sum of low intensities under threshold
h, w = gray.shape
threshold = 100
col_sums = np.sum(gray < threshold, axis=0)
# display peaks
# find index of maximum
x_axis = np.argmax(col_sums)
x_axis

30

python
plt.figure(figsize=(4,4))
plt.imshow(img)
plt.axvline(x=30,color='red')
plt.axis('off')

(-0.5, 603.5, 484.5, -0.5)

python
graph = img.crop((180,80,480,380))
grayg = cv2.cvtColor(np.array(graph), cv2.COLOR_BGR2GRAY)
h2,w2 = grayg.shape
col_s = np.sum(grayg < threshold, axis=0)
row_s = np.sum(grayg < threshold, axis=1)
# find vertical axis, choose col with high sum
x_axis_col = np.argmax(col_s)
y_axis_row = np.argmax(row_s)
x_axis_col, y_axis_row

(116, 119)

python
plt.figure(figsize=(4,4))
plt.imshow(graph)
plt.axvline(x=x_axis_col,color='red')
plt.axhline(y=y_axis_row,color='blue')
plt.axis('off')

(-0.5, 299.5, 299.5, -0.5)

python
# detect black pixels
# threshold: black pixel intensity < 50 maybe
mask = grayg < 50
# find coordinates of mask
coords = np.column_stack(np.where(mask))
# choose coords where x <= x_axis_col perhaps for left side
left_coords = coords[coords[:,1] < x_axis_col - 5]  # avoid axis
# may also filter above y axis row maybe y<=y_axis_row? Actually P is above y-axis and above origin maybe around y slightly above axis row
# Actually P is above x-axis maybe above row y_axis_row?
# Let's filter rows less than maybe y_axis_row but greater than y_axis_row - some margin?
p_candidates = left_coords[left_coords[:,0] <= y_axis_row + 10]  # within maybe 10 px above axis row
# Actually P dot is above x-axis maybe 1-3 squares above axis, which may be around 2*grid spacing ~ maybe 40 pixels above y_axis_row?
# Let's search candidates where row < y_axis_row
p_candidates = left_coords[left_coords[:,0] < y_axis_row - 10]
# compute mean row/col of cluster
py, px = np.mean(p_candidates, axis=0)
py, px

(93.45161290322581, 103.58064516129032)