ls -la /tmp/workspace/ 2>/dev/null || mkdir -p /tmp/workspace && ls -la /tmp/workspace/

mkdir -p /tmp/workspace/photo-edit && cd /tmp/workspace/photo-edit && curl -s -o original.jpg "https://d361swluvexzqs.cloudfront.net/chats-attachments/1000349004-1784961722075-yt6ph0.jpg" && echo "Downloaded: $(ls -lh original.jpg)"

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") print(f"Size: {img.size}, Mode: {img.mode}") arr = np.array(img) print(f"Shape: {arr.shape}") # Let's inspect the header area to find underlines # The header text is roughly in the top 350 pixels header = arr[:350, :, :] print(f"Header shape: {header.shape}") # Show some row statistics to find horizontal dark lines (underlines) for y in range(0, 350, 5): row = header[y, :, :] # Count dark pixels (potential ink) dark = np.sum(np.all(row < 100, axis=1)) print(f"Row {y}: dark_pixels={dark}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img) # Let me look more carefully at the header area # Check row by row for wide continuous dark lines (underlines vs text) # An underline is typically a thin horizontal band of dark pixels spanning a wide portion of the row for y in range(0, 400, 1): row = arr[y, 100:1000, :] # Focus on center area dark_mask = np.all(row < 100, axis=1) dark_count = np.sum(dark_mask) if dark_count > 100: # Rows with significant darkness # Check if it's a continuous run (underline) vs scattered (text) # Find longest consecutive run run = 0 max_run = 0 for px in dark_mask: if px: run += 1 max_run = max(max_run, run) else: run = 0 print(f"Row {y}: dark={dark_count}, max_continuous_run={max_run}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img) # Save crops of header rows to understand what's text vs underline # Let me save crops around key row areas for row_range, name in [ (75, 95, "row75_95"), (128, 145, "row128_145"), (183, 196, "row183_196"), (237, 252, "row237_252"), (258, 280, "row258_280"), (324, 342, "row324_342"), ]: pass # Better approach: save a wider crop of the full header header_crop = Image.fromarray(arr[60:400, 50:1080, :]) header_crop.save("header_area.jpg") print("Saved header_area.jpg") # Also check the bottom of header for the separator line for y in range(380, 430, 1): row = arr[y, 50:1080, :] dark_mask = np.all(row < 80, axis=1) dark_count = np.sum(dark_mask) if dark_count > 200: run = 0 max_run = 0 for px in dark_mask: if px: run += 1 max_run = max(max_run, run) else: run = 0 print(f"Row {y}: dark={dark_count}, max_run={max_run}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img) # Save header area crop header_crop = Image.fromarray(arr[60:400, 50:1080, :]) header_crop.save("header_area.jpg") print("Saved header_area.jpg") # Check for wide separator line (horizontal black line separating header from body) print("\nLooking for wide dark horizontal lines (separator lines):") for y in range(60, 450): row = arr[y, 50:1080, :] dark_mask = np.all(row < 80, axis=1) dark_count = np.sum(dark_mask) if dark_count > 500: # Very wide dark line = separator print(f"Row {y}: dark={dark_count} <- WIDE LINE (likely separator/underline)") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img, dtype=np.float32) # Looking at the image, the underlines appear as thin horizontal dark lines # directly under each of the 4 header text lines. # The header section spans roughly rows 65-220 (before the big horizontal separator line at ~row 220) # # Let me scan precisely for thin horizontal dark line segments (underlines) # in the header region. Underlines = thin (1-3px) dark rows spanning 200+ pixels # Convert to grayscale for analysis gray = np.mean(arr[:450, 100:1000, :], axis=2) # Shape: (450, 900) print("Scanning for underline candidates (thin dark rows with wide spans):") print("="*70) for y in range(65, 430): row = gray[y, :] dark_mask = row < 100 dark_count = np.sum(dark_mask) if dark_count > 150: # Check if upper and lower rows have much less darkness (thin line = underline) above = gray[max(0,y-3):y, :] below = gray[y+1:y+4, :] above_dark = np.sum(above < 100) below_dark = np.sum(below < 100) # Find max consecutive run run = max_run = 0 for px in dark_mask: run = run + 1 if px else 0 max_run = max(max_run, run) print(f"Row {y}: dark={dark_count:4d}, max_run={max_run:4d}, above_dark={above_dark:4d}, below_dark={below_dark:4d}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np # Looking at the image carefully: # - The header has 4 text lines, each with an underline below it # - There's also a thick separator line separating header from body (~row 263-274) # - The underlines appear as thin dark horizontal marks under each text line # # From visual inspection, the 4 underlines in the header are approximately: # Line 1 (وزارت): underline around row 88-92 # Line 2 (مؤسسه): underline around row 143-148 # Line 3 (ریاست): underline around row 195-200 # Line 4 (معاونیت): underline around row 248-253 # Thick separator line: rows 260-278 # # Let me save individual horizontal strips to verify img = Image.open("original.jpg") arr = np.array(img) # Save strips around candidate underline rows for label, y1, y2 in [ ("line1_text_underline", 70, 100), ("line2_text_underline", 120, 155), ("line3_text_underline", 175, 210), ("line4_text_underline", 225, 265), ("separator_line", 255, 285), ("form_title", 285, 350), ("name_line", 400, 430), ]: strip = Image.fromarray(arr[y1:y2, 50:1080, :]) strip.save(f"strip_{label}.jpg") print(f"Saved strip_{label}.jpg (rows {y1}-{y2})") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img, dtype=np.uint8) result = arr.copy() # From analysis: # The header text lines with underlines: # Each text line ends with a thin underline just below the text # # Based on visual inspection of the image: # Row 82-84: area around Line 1 "وزارت تح...صیلات عالی" - these are part of BOLD text, not underlines # The bold text appears thick because it IS bold script # # Looking again at the actual image: # The "underline" format = line UNDER the text, like _text_ # Each of the 4 header lines appears bold AND underlined # The underlines would be thin horizontal lines sitting just BELOW the text baseline # # Let me look for single-pixel-height or 2-pixel-height dark rows that have # CLEAN (few dark pixels) rows both ABOVE and BELOW them # i.e., isolated thin dark strips = underlines gray = np.mean(arr[:, :, :3].astype(float), axis=2) # Scan in the header region for underlines # Focus on center area (avoiding logo and photo on sides) print("Analyzing rows in header for thin dark underlines:") print("Format: row | dark_pixels_center | (above3_dark, below3_dark) | ISOLATED?") print("="*75) for y in range(65, 270): center_row = gray[y, 200:900] dark_here = np.sum(center_row < 120) if dark_here > 80: # Has some darkness above3 = np.sum(gray[max(0,y-5):y, 200:900] < 120) below3 = np.sum(gray[y+1:y+6, 200:900] < 120) # Underline = isolated (low darkness above and below relative to current) isolation = (above3 < dark_here * 2) and (below3 < dark_here * 2) # Check if it looks like a WIDE LINE - dark pixels spanning wide consecutive range center_dark_mask = center_row < 120 run = max_run = 0 for px in center_dark_mask: run = run + 1 if px else 0 max_run = max(max_run, run) marker = " <<< UNDERLINE CANDIDATE" if (max_run > 150 and above3 < 300 and below3 < 300) else "" print(f"Row {y:3d}: dark={dark_here:4d}, max_run={max_run:4d}, above={above3:4d}, below={below3:4d}{marker}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np img = Image.open("original.jpg") arr = np.array(img, dtype=np.uint8) result = arr.copy() H, W = arr.shape[:2] # Strategy: For the 4 header text lines, the underline is at the BOTTOM of each text cluster # The rows with high max_run and isolated below (low below_dark) are the underline rows: # # Text line 1: rows ~75-84, underline = row 82-84 (max_run ~122-130) # Text line 2: rows ~130-139, underline = row 136-139 (max_run ~36-55) # Text line 3: rows ~183-190, underline = row 187-190 (max_run ~29-37) # Text line 4: rows ~237-244, underline = row 241-244 (max_run ~51-69) # Separator: rows ~263-269 (max_run ~144-169) - THIS IS THE THICK SEPARATOR, keep it # # Wait - looking at the original image again more carefully: # The UNDERLINES are the thin lines DIRECTLY UNDER each text # Each header text line has bold text + underline below it # # From the data: rows 82-84 have dark_here~139-194 with max_run ~89-130 # These rows have MUCH lower "below" than the rows above them -> they're at the BOTTOM edge # So rows 82-84 ARE the underline of text line 1 # # For text line 2: rows 136-139 are at bottom with rapidly decreasing below values # For text line 3: rows 188-190 are at bottom # For text line 4: rows 242-244 are at bottom # # But wait - these could just be the bottom strokes of the bold LETTERS themselves # Let me think differently: # In the actual image, the underlines appear as SEPARATE thin lines below the text # They would show as isolated dark rows with white space BOTH above AND below # # Looking at row 82: above=383, but below=413... both have darkness -> this is IN the text # The TRUE underline would be a row with: moderate dark pixels, wide span, # and mostly WHITE (low dark) BELOW it # # Rows with mostly white below (below < 200): # Row 83: below=240 # Row 84: below=103 <- LOWEST below, this is end of text/underline # Row 139: below=64 <- End of line 2 # Row 190: below=128 <- End of line 3 # Row 244: below=90 <- End of line 4 # # So the underlines = the last 1-3 rows of each text cluster that span wide # Let me find exactly where text ends vs where the underline line is # # In Arabic/Persian bold text with underline formatting: # - The text body has scattered dark pixels (letters with gaps) # - The underline is a SOLID or near-solid thin line # # Let me check pixel pattern for row 82-84 vs 75-81 (text body) gray = np.mean(arr.astype(float), axis=2) print("Detailed analysis of text line 1 area:") for y in range(73, 90): row = gray[y, 200:900] dark_mask = row < 120 dark_pix = np.sum(dark_mask) # Measure "solidness" = dark pixels / total span (from first to last dark pixel) dark_positions = np.where(dark_mask)[0] if len(dark_positions) > 5: span = dark_positions[-1] - dark_positions[0] density = dark_pix / max(span, 1) else: span = density = 0 # Count white gaps (transitions from dark to white within span) gaps = 0 in_dark = False for px in dark_mask: if px and not in_dark: in_dark = True elif not px and in_dark: in_dark = False gaps += 1 print(f"Row {y:3d}: dark={dark_pix:4d}, span={span:4d}, density={density:.3f}, gaps={gaps:4d}") print() print("Text line 4 area:") for y in range(236, 250): row = gray[y, 200:900] dark_mask = row < 120 dark_pix = np.sum(dark_mask) dark_positions = np.where(dark_mask)[0] if len(dark_positions) > 5: span = dark_positions[-1] - dark_positions[0] density = dark_pix / max(span, 1) else: span = density = 0 gaps = 0 in_dark = False for px in dark_mask: if px and not in_dark: in_dark = True elif not px and in_dark: in_dark = False gaps += 1 print(f"Row {y:3d}: dark={dark_pix:4d}, span={span:4d}, density={density:.3f}, gaps={gaps:4d}") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np # From the analysis, the text lines in the header are BOLD text (not underlines per se) # The "underlines" the user sees are likely the thick horizontal lines that appear # directly beneath each of the 4 header text rows # # Looking at the data more carefully: # - Line 1: text ends at row 88, with peak darkness at rows 81-84 (density ~0.7) # - The text itself is the DENSE part (rows 81-85) # - Rows 73-80 are the tops of letters # - The "underline" = a SEPARATE thin dark bar sitting just below the letter baselines # # In bold+underline Persian/Arabic text, the underline is a literal line drawn under words # Based on row 82-84: density ~0.5-0.7 with only 8-14 gaps across the whole span # That's actually pretty dense for a LETTER ROW (letters don't fill 70% of horizontal space) # But for an underline line, yes it would be dense # # The issue is: at rows 82-84, we have BOTH the bottoms of letters AND the underline # Perfect surgical removal isn't possible without reconstructing text # # BEST APPROACH: The user wants the UNDERLINE REMOVED but text preserved # Since the underlines are mixed with letter bottoms, let me use a different strategy: # 1. Find rows that are PREDOMINANTLY dark across a wide span (underline-like) # 2. Only erase pixels that form continuous dark runs (the underline part) # 3. Leave isolated dark clusters (letter strokes) intact # # Actually - re-reading the user request: "Remove the underline text" # This might mean: remove the text that HAS underlines (the bold header text lines) # OR: remove the underline formatting (keep text, remove just the underline marks) # # Given this is a document with header info, most likely they want the UNDERLINE MARKS # removed while keeping the text readable. # # Let me try: for rows that have high density dark pixels (>0.35 density), # find the long continuous dark runs and break them up (paint white), # but preserve short runs (letter strokes) img = Image.open("original.jpg") arr = np.array(img, dtype=np.uint8) result = arr.copy() H, W = arr.shape[:2] gray = np.mean(arr.astype(float), axis=2) def remove_underlines_in_row(arr_result, gray, y, x_start=150, x_end=980, threshold=120, min_run=60): """Remove continuous dark runs in a row that are longer than min_run pixels (underline)""" row = gray[y, x_start:x_end] dark_mask = row < threshold # Find continuous runs runs = [] in_run = False run_start = 0 for i, is_dark in enumerate(dark_mask): if is_dark and not in_run: in_run = True run_start = i elif not is_dark and in_run: in_run = False runs.append((run_start, i)) if in_run: runs.append((run_start, len(dark_mask))) # Only remove runs longer than min_run (these are underlines, not letter strokes) count_removed = 0 for (rs, re) in runs: if (re - rs) >= min_run: # Paint this section white arr_result[y, x_start + rs:x_start + re, :] = 255 count_removed += re - rs return count_removed # The underlines for each header line are at the DENSEST rows near the bottom of each text cluster # From analysis: # Text line 1: dense rows 81-85, underline specifically at 82-84 # Text line 2: dense rows 135-139 # Text line 3: dense rows 186-190 # Text line 4: dense rows 241-244 # Separator: rows 263-269 (user likely wants this KEPT as it's a formal separator) # Let me remove long continuous dark runs in the underline rows total_removed = 0 for y in list(range(81, 86)) + list(range(135, 140)) + list(range(186, 191)) + list(range(241, 246)): n = remove_underlines_in_row(result, gray, y, x_start=150, x_end=980, threshold=120, min_run=50) if n > 0: print(f"Row {y}: removed {n} underline pixels") total_removed += n print(f"\nTotal pixels removed: {total_removed}") # Save result output = Image.fromarray(result) output.save("result_no_underlines.jpg", quality=95) print("Saved result_no_underlines.jpg") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' # Let me reconsider - looking at the image VERY carefully # # The "underline text" the user refers to is the BOTTOM LINE in the header: # "معاونیـــت امور محصلان" which appears with an underline # OR all 4 header lines which appear in bold+underlined format # # But actually: looking at the image description again carefully: # ALL 4 header text lines appear BOLD. Some appear with underlines. # Line 1: وزارت تح___صیلات عالی (has elongated dash in middle) # Line 2: مؤسسه تحصیلات عالی سپین غر "کابل" # Line 3: ریاست پوهنخی طلب معالجوی # Line 4: معاونیـت امور محصلان # # The underlines are the lines drawn UNDER the text, not the dashes within words # These are formatting underlines # # The issue with my current approach: removing "continuous dark runs" may be # breaking up the bottoms of letters too # # Better approach: detect PURE underline rows = rows where darkness is present # in a LONG SPAN but with FEW GAPS (underline = solid line, letters = fragmented) # AND where the row just BELOW is nearly white # Let me check what the result looks like by examining the header area pixels from PIL import Image import numpy as np result = np.array(Image.open("result_no_underlines.jpg")) original = np.array(Image.open("original.jpg")) # Check header region - compare original vs result gray_orig = np.mean(original[65:270, 150:980, :].astype(float), axis=2) gray_result = np.mean(result[65:270, 150:980, :].astype(float), axis=2) print("Comparing original vs result in header region:") print("Rows where something changed:") for y in range(0, 205): diff = np.sum(np.abs(gray_orig[y].astype(float) - gray_result[y].astype(float))) if diff > 0: print(f" Row {y+65}: total_diff={diff:.0f}") print("\nDone. Result saved.") EOF

cd /tmp/workspace/photo-edit && python3 - <<'EOF' from PIL import Image import numpy as np from scipy.ndimage import median_filter img = Image.open("original.jpg") arr = np.array(img, dtype=np.uint8) result = arr.copy() H, W = arr.shape[:2] gray = np.mean(arr.astype(float), axis=2) def remove_underline_in_row(result, gray, y, threshold=120, min_run=40, x_start=150, x_end=980): """ Remove underline (continuous dark pixels) in a given row. Only removes continuous runs >= min_run pixels wide. Replaces with white (255). """ row = gray[y, x_start:x_end] dark_mask = row < threshold # Find continuous dark runs runs = [] in_run = False run_start = 0 for i, is_dark in enumerate(dark_mask): if is_dark and not in_run: in_run = True run_start = i elif not is_dark and in_run: in_run = False runs.append((run_start, i)) if in_run: runs.append((run_start, len(dark_mask))) # Remove long runs (underlines) for (rs, re) in runs: if (re - rs) >= min_run: result[y, x_start + rs : x_start + re, :] = 255 # Process each text line's underline rows # Line 1: underlines at rows 82-84 for y in range(82, 85): remove_underline_in_row(result, gray, y, threshold=120, min_run=40) # Line 2: underlines at rows 137-138 for y in range(136, 140): remove_underline_in_row(result, gray, y, threshold=120, min_run=40) # Line 3: underlines at rows 188-190 for y in range(187, 192): remove_underline_in_row(result, gray, y, threshold=120, min_run=40) # Line 4: underlines at rows 241-245 for y in range(241, 246): remove_underline_in_row(result, gray, y, threshold=120, min_run=40) # Save the final result output = Image.fromarray(result) output.save("final_no_underlines.jpg", quality=95) print("Saved final_no_underlines.jpg") print(f"Image size: {output.size}") EOF

ls -la /tmp/workspace/photo-edit/ && python3 -c " from PIL import Image img = Image.open('/tmp/workspace/photo-edit/final_no_underlines.jpg') print('OK:', img.size, img.mode) # Crop just the header area and save separately header = img.crop((50, 60, 1080, 280)) header.save('/tmp/workspace/photo-edit/header_result.jpg', quality=95) print('Header crop saved') "