WIP : reading checks
parent
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commit
c94c773c10
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import sys
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import os
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import json
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import shutil
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import concurrent.futures
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import threading
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# Fix for Matplotlib in threads: Set backend to non-interactive 'Agg'
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import matplotlib
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matplotlib.use('Agg')
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from PIL import Image, ImageDraw, ImageFont
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import annotating
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from annotating import MARGIN_LEFT, ANNOT_WIDTH
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# Global lock for Matplotlib/Latex rendering to prevent race conditions
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LATEX_LOCK = threading.Lock()
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BOX_SIZE = 30
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SCORE_BOX_SIZE = 30
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SCORES = [x * 0.5 for x in range(9)] # 0.0 to 4.0
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try:
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CHECKBOX_FONT = ImageFont.truetype("DejaVuSans.ttf", 20)
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except IOError:
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try:
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CHECKBOX_FONT = ImageFont.truetype("arial.ttf", 20)
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except IOError:
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CHECKBOX_FONT = ImageFont.load_default()
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def draw_checkbox(draw, x, y, size=BOX_SIZE, label=None, fill="white"):
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if label:
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draw.text((x - BOX_SIZE-5, y + 2), str(label), fill="black", font=CHECKBOX_FONT)
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draw.rectangle([x, y, x + size, y + size], fill=fill, outline="black", width=2)
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return [x, y, x + size, y + size]
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def safe_render_latex(text, **kwargs):
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"""Thread-safe wrapper for latex rendering."""
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with LATEX_LOCK:
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return annotating.render_latex_text(text, **kwargs)
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def create_base_image(pdf_path):
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"""Converts PDF pages to a single vertically stacked image."""
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try:
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pages = annotating.convert_from_path(pdf_path)
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total_h = sum(page.height for page in pages)
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max_w = max(page.width for page in pages)
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base_img = Image.new("RGBA", (max_w, total_h), "white")
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cy = 0
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for page in pages:
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base_img.paste(page.convert("RGBA"), (0, cy))
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cy += page.height
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return base_img
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except Exception:
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return None
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def render_header(label, score, feedbacks, base_width):
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"""Generates the score line and global feedback elements."""
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elements = []
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# Score Line
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score_text_img = safe_render_latex(f"{label} ; Note : {score}", width_px=base_width // 2, fontsize=18)
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score_line_h = max(score_text_img.height, SCORE_BOX_SIZE + 10)
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score_line_img = Image.new("RGBA", (base_width, score_line_h), (255, 255, 255, 0))
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score_line_img.paste(score_text_img, (0, 0))
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draw_score = ImageDraw.Draw(score_line_img)
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start_x = score_text_img.width + 20
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local_boxes = []
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for val in SCORES:
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box = draw_checkbox(draw_score, start_x, 5, SCORE_BOX_SIZE, str(val))
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local_boxes.append({
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"type": "score", "label": label, "value": val,
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"rel_box": box, "elem_y": 0
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})
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start_x += SCORE_BOX_SIZE + 60
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elements.append((score_line_img, local_boxes))
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# Global Feedback
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for i, fb in enumerate(feedbacks):
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fb_img = safe_render_latex(fb['text'], width_px=base_width)
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draw_fb = ImageDraw.Draw(fb_img)
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bx = fb_img.width - BOX_SIZE - 5
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by = 5
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box = draw_checkbox(draw_fb, bx, by, BOX_SIZE)
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elements.append((fb_img, [{
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"type": "del_global", "label": label, "index": i,
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"text_preview": fb['text'][:20], "rel_box": box, "elem_y": 0
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}]))
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return elements
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def process_label(root_dir, student_id, label, content):
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"""Processes a single label (PDF) -> Annotated Image + Checkboxes."""
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copie_folder = f"Copie{student_id}"
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pdf_path = os.path.join(root_dir, copie_folder, f"{label}.pdf")
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if not os.path.exists(pdf_path):
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return None, []
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base_img = create_base_image(pdf_path)
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if not base_img:
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return None, []
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# Extract Data
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coordinates = content.get('coordinates', (0, 0))
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hmin = coordinates[0]
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result = content.get('result', {})
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score = result.get('score', 0)
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feedbacks = result.get('feedback', [])
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global_fb = [f for f in feedbacks if not f.get('box_2d')]
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local_fb = [f for f in feedbacks if f.get('box_2d')]
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local_fb.sort(key=lambda x: x['box_2d'][0])
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checkbox_map = []
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# 1. Render Header
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header_elements = render_header(label, score, global_fb, base_img.width)
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header_height = sum(x[0].height for x in header_elements)
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# 2. Assemble Base + Header
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total_height = base_img.height + header_height
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final_img = Image.new("RGB", (base_img.width + MARGIN_LEFT, total_height), "white")
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current_y = 0
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for img, boxes in header_elements:
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final_img.paste(img, (0, current_y))
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for b in boxes:
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b['final_box'] = [b['rel_box'][0], b['rel_box'][1] + current_y,
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b['rel_box'][2], b['rel_box'][3] + current_y]
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checkbox_map.append(b)
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current_y += img.height
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image_offset_y = current_y
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final_img.paste(base_img, (MARGIN_LEFT, image_offset_y))
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# 3. Draw Local Annotations
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draw = ImageDraw.Draw(final_img, "RGBA")
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last_text_bottom = 0
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for i, fb in enumerate(local_fb):
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box = fb.get('box_2d')
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if not box: continue
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ymin, xmin, ymax, xmax = box
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target_ymin = (ymin - hmin) + image_offset_y
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target_ymax = (ymax - hmin) + image_offset_y
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target_xmin = xmin + MARGIN_LEFT
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target_xmax = xmax + MARGIN_LEFT
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draw.rectangle([target_xmin, target_ymin, target_xmax, target_ymax], outline="red", width=3)
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rect_cb_box = draw_checkbox(draw, target_xmax - BOX_SIZE, target_ymin, BOX_SIZE)
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checkbox_map.append({
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"type": "del_local", "label": label, "index": i,
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"text_preview": fb['text'][:20], "final_box": rect_cb_box
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})
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txt_img_raw = safe_render_latex(fb['text'], width_px=ANNOT_WIDTH, bg_color=(255, 200, 200, 180), max_lines=3)
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container_h = max(txt_img_raw.height, BOX_SIZE)
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txt_img = Image.new("RGBA", (ANNOT_WIDTH, container_h), (255, 255, 255, 0))
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txt_img.paste(txt_img_raw, (0, 0))
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d_txt = ImageDraw.Draw(txt_img)
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draw_checkbox(d_txt, ANNOT_WIDTH - BOX_SIZE, 0, BOX_SIZE)
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center_y = (target_ymin + target_ymax) / 2
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paste_y = max(center_y - (txt_img.height / 2), image_offset_y)
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if paste_y < last_text_bottom: paste_y = last_text_bottom + 5
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req_h = int(paste_y + txt_img.height + 20)
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if req_h > final_img.height:
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new_final = Image.new("RGB", (final_img.width, req_h), "white")
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new_final.paste(final_img, (0,0))
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final_img = new_final
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draw = ImageDraw.Draw(final_img, "RGBA")
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final_img.paste(txt_img, (10, int(paste_y)), mask=txt_img)
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checkbox_map.append({
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"type": "del_local", "label": label, "index": i,
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"text_preview": fb['text'][:20],
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"final_box": [10 + ANNOT_WIDTH - BOX_SIZE, int(paste_y), 10 + ANNOT_WIDTH, int(paste_y) + BOX_SIZE]
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})
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last_text_bottom = paste_y + txt_img.height
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return final_img, checkbox_map
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import re
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def natural_key(text):
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return [int(c) if c.isdigit() else c.lower() for c in re.split(r'(\d+)', str(text))]
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def process_student(args):
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"""Thread worker: Processes one student."""
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root_dir, student_id, labels = args
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print(f"Generating Checkable PDF for: {student_id}")
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output_dir = os.path.join(root_dir, "Bnot", f"Copie{student_id}")
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if os.path.exists(output_dir):
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shutil.rmtree(output_dir)
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os.makedirs(output_dir)
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label_images = []
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student_checkboxes = []
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processed_labels_order = []
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for label, content in sorted(labels.items(), key=lambda x: natural_key(x[0])):
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img, boxes = process_label(root_dir, student_id, label, content)
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if img:
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label_images.append(img)
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student_checkboxes.append(boxes)
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processed_labels_order.append(label)
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if not label_images:
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return
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max_w = max(i.width for i in label_images)
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total_h = sum(i.height for i in label_images)
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concat_img = Image.new("RGB", (max_w, total_h), "white")
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final_json_map = []
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current_y = 0
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for label_name, img, boxes in zip(processed_labels_order, label_images, student_checkboxes):
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concat_img.paste(img, (0, current_y))
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for item in boxes:
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b = item['final_box']
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item['global_box'] = [b[0], b[1] + current_y, b[2], b[3] + current_y]
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final_json_map.append(item)
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current_y += img.height
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with open(os.path.join(output_dir, "checkboxes.json"), "w") as f:
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json.dump(final_json_map, f, indent=2)
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concat_img.save(os.path.join(output_dir, "Reference.png"))
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concat_img.save(os.path.join(output_dir, "Concat.pdf"), "PDF", resolution=100.0)
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python annotating_with_checks.py <Dir>")
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sys.exit(1)
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root_dir = sys.argv[1]
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results = annotating.make_dictionary(root_dir)
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tasks = [(root_dir, sid, lbls) for sid, lbls in results.items()]
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# print(tasks)
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with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
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executor.map(process_student, tasks)
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import sys
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import os
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import json
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import numpy as np
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import shutil
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from PIL import Image, ImageChops
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Image.MAX_IMAGE_PIXELS = None
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from pdf2image import convert_from_path
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import annotating # Reuse rendering logic
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def detect_checks_and_notes(output_dir):
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"""
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Returns:
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actions: List of dicts {type, label, ...} for checked boxes
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notes_img: RGBA image of manual notes (checks masked out)
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"""
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pdf_path = os.path.join(output_dir, "Concat_annotated.pdf")
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ref_path = os.path.join(output_dir, "Reference.png")
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json_path = os.path.join(output_dir, "checkboxes.json")
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if not (os.path.exists(pdf_path) and os.path.exists(ref_path)):
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print(f"Missing files in {output_dir}")
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return [], None
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# Load Coordinates
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with open(json_path, 'r') as f:
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boxes = json.load(f)
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# Load Reference
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ref_img = Image.open(ref_path).convert("RGB")
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# Load User PDF (First page only, assuming it's one long strip)
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# Warning: If the PDF is huge, pdf2image might split pages or OOM.
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# Assuming user didn't change page dimensions/order.
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try:
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user_pages = convert_from_path(pdf_path)
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except Exception as e:
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print(f"Error reading PDF: {e}")
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return [], None
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print("Debug : user_pages", len(user_pages))
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# Concatenate PDF pages back to one image if user saved as multiple pages
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# (Xournal++ might preserve the long format or split it)
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total_h = sum(p.height for p in user_pages)
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user_img = Image.new("RGB", (user_pages[0].width, total_h))
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y = 0
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for p in user_pages:
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user_img.paste(p, (0, y))
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y += p.height
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# Resize user_img to match ref_img if slight mismatch (DPI export diffs)
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if user_img.size != ref_img.size:
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print("Debug : size mismatch : ", user_img.size, ref_img.size)
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user_img = user_img.resize(ref_img.size, Image.Resampling.LANCZOS)
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# --- Detection Phase ---
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actions = []
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# Convert to numpy for analysis
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ref_arr = np.array(ref_img)
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user_arr = np.array(user_img)
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# Diff for analysis
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# Simple absolute difference
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diff = np.abs(ref_arr.astype(int) - user_arr.astype(int)).astype(np.uint8)
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# Convert to grayscale for thresholding
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diff_gray = np.mean(diff, axis=2)
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# Threshold for "Checked"
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CHECK_THRESHOLD = 30 # intensity diff
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DENSITY_THRESHOLD = 0.05 # 5% of pixels darkened
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# Mask to hide checkmarks from the "Notes" extraction
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mask_img = Image.new("L", ref_img.size, 255) # White = keep, Black = hide
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mask_draw = ImageDraw.Draw(mask_img)
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for box in boxes:
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# global_box: [x1, y1, x2, y2]
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b = box['global_box']
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x1, y1, x2, y2 = map(int, b)
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# Ensure bounds
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(ref_img.width, x2), min(ref_img.height, y2)
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# Analyze ROI
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roi = diff_gray[y1+5:y2-5, x1+5:x2-5]
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if roi.size == 0: continue
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changed_pixels = np.sum(roi > CHECK_THRESHOLD)
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density = changed_pixels / roi.size
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if density > DENSITY_THRESHOLD:
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print("A checked box !", density, b)
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actions.append(box)
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# It's checked, so we mask this area out for manual notes
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# Expand mask slightly to catch sloppy ticks
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mask_draw.rectangle([x1-5, y1-5, x2+5, y2+5], fill=0)
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else:
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# print("A box, not checked !", density)
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# Even if not "checked", mask the box area slightly to avoid
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# artifacts if user hovered over it, though arguably we keep it.
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# Let's strictly mask only if checked to verify detection?
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# No, prompt says "not extract the part that are just checking".
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# If user checked it, we mask it.
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pass
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# --- Extraction Phase ---
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# Create the "Manual Notes" layer
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# Logic: User - Ref. If Diff is dark -> Note.
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# We want a transparent image with just the pen strokes.
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# 1. Get difference image
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diff_img = ImageChops.difference(ref_img, user_img).convert("L")
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# 2. Threshold to remove JPEG noise (white background isn't perfect)
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# Pixels that are different enough:
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diff_data = np.array(diff_img)
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# Create alpha channel: 0 where no diff, 255 where diff
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alpha = np.where(diff_data > 20, 255, 0).astype(np.uint8)
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# 3. Create output image (Black strokes, variable alpha)
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# Or Copy user colors? Better to copy user pixels.
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notes = user_img.convert("RGBA")
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r, g, b, a = notes.split()
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# Combine the diff-based alpha with the box-mask
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mask_arr = np.array(mask_img)
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final_alpha = np.minimum(alpha, mask_arr)
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notes.putalpha(Image.fromarray(final_alpha))
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return actions, notes
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from PIL import ImageDraw
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def apply_actions_and_regenerate(root_dir, data, student_id, actions, notes_layer):
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"""
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Modifies data based on actions, calls annotating.process_correction logic,
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overlays notes, saves Concat.jpg.
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"""
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labels = data[student_id]
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# 1. Apply Actions to Data
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# Sort actions to handle indices correctly (delete from end?)
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# But we regenerate from dictionary, so modifying the dictionary is fine.
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# Separate actions by label
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actions_by_label = {}
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for a in actions:
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l = a['label']
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if l not in actions_by_label: actions_by_label[l] = []
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actions_by_label[l].append(a)
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for label, acts in actions_by_label.items():
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if label not in labels: continue
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content = labels[label]
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result = content['result']
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feedbacks = result.get('feedback', [])
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# Split feedbacks again to match indices
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global_fb_indices = [i for i, f in enumerate(feedbacks) if not f.get('box_2d')]
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local_fb_indices = [i for i, f in enumerate(feedbacks) if f.get('box_2d')]
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# Sort local by Y to match generation order in annotating.py
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local_fb_sorted_map = sorted(local_fb_indices, key=lambda i: feedbacks[i]['box_2d'][0])
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items_to_remove = set()
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for act in acts:
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if act['type'] == 'set_score':
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result['score'] = act['value']
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print(f" > Updated score for {label} to {act['value']}")
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elif act['type'] == 'del_global':
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# act['index'] is the index within the global_fb list
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# We need to find the actual index in the main list
|
||||
if act['index'] < len(global_fb_indices):
|
||||
real_idx = global_fb_indices[act['index']]
|
||||
items_to_remove.add(real_idx)
|
||||
print(f" > Deleted global feedback in {label}")
|
||||
|
||||
elif act['type'] == 'del_local':
|
||||
# act['index'] is index in sorted local list
|
||||
if act['index'] < len(local_fb_sorted_map):
|
||||
real_idx = local_fb_sorted_map[act['index']]
|
||||
items_to_remove.add(real_idx)
|
||||
print(f" > Deleted local feedback in {label}")
|
||||
|
||||
# Remove feedbacks (in reverse to preserve indices)
|
||||
for idx in sorted(list(items_to_remove), reverse=True):
|
||||
del feedbacks[idx]
|
||||
|
||||
# 2. Regenerate Clean Image
|
||||
# We use a temporary modified dictionary
|
||||
temp_data = {student_id: labels}
|
||||
|
||||
# Run the original process (but we need to intercept it to not save, or just let it save)
|
||||
# annotating.process_correction saves to "Anot_CopieID".
|
||||
# We want "Bnot_CopieID" (updated).
|
||||
|
||||
# Hijack the output dir in logic or copy code?
|
||||
# Easiest: Let's create a temporary helper or modify annotating logic slightly?
|
||||
# The prompt implies we use `annotating.py` logic.
|
||||
# Let's call `annotating.process_correction` but point it to a temp root or modify path?
|
||||
# No, `process_correction` takes `root_dir` and writes to `Anot_...`.
|
||||
# Let's just implement the rendering loop here to be safe and clean,
|
||||
# overlaying the notes at the end.
|
||||
|
||||
output_dir = os.path.join(root_dir, "Bnot", f"Copie{student_id}")
|
||||
# Don't delete output_dir, we need it.
|
||||
|
||||
# ... (Reuse rendering logic from annotating.py exactly) ...
|
||||
# See below for condensed integration
|
||||
|
||||
final_concats = []
|
||||
|
||||
for label, content in labels.items():
|
||||
# ... [PDF to Image Conversion] ...
|
||||
copie_folder = f"Copie{student_id}"
|
||||
pdf_path = os.path.join(root_dir, copie_folder, f"{label}.pdf")
|
||||
if not os.path.exists(pdf_path): continue
|
||||
|
||||
pages = annotating.convert_from_path(pdf_path)
|
||||
base_img = Image.new("RGBA", (max(p.width for p in pages), sum(p.height for p in pages)), "white")
|
||||
y=0
|
||||
for p in pages: base_img.paste(p.convert("RGBA"), (0,y)); y+=p.height
|
||||
|
||||
# ... [Draw Header/Margin (Clean)] ...
|
||||
margin_left = 200
|
||||
result = content['result']
|
||||
coordinates = content.get('coordinates', (0,0))
|
||||
hmin = coordinates[0]
|
||||
|
||||
score_text = f"{label} ; Note : {result.get('score', 0)}"
|
||||
if result.get('error') and result.get('error') != "null": score_text += f" | Error: {result.get('error')}"
|
||||
|
||||
header_imgs = [annotating.render_latex_text(score_text, base_img.width, fontsize=18)]
|
||||
|
||||
feedbacks = result.get('feedback', [])
|
||||
# Separate again (now cleaned)
|
||||
global_fb = [f for f in feedbacks if not f.get('box_2d')]
|
||||
local_fb = [f for f in feedbacks if f.get('box_2d')]
|
||||
local_fb.sort(key=lambda x: x['box_2d'][0])
|
||||
|
||||
for fb in global_fb: header_imgs.append(annotating.render_latex_text(fb['text'], base_img.width))
|
||||
|
||||
total_h = base_img.height + sum(i.height for i in header_imgs)
|
||||
label_img = Image.new("RGB", (base_img.width + margin_left, total_h), "white")
|
||||
|
||||
cy = 0
|
||||
for i in header_imgs: label_img.paste(i, (0, cy)); cy+=i.height
|
||||
img_offset_y = cy
|
||||
label_img.paste(base_img, (margin_left, img_offset_y))
|
||||
|
||||
draw = ImageDraw.Draw(label_img, "RGBA")
|
||||
last_bot = 0
|
||||
for fb in local_fb:
|
||||
box = fb['box_2d']
|
||||
ymin, xmin, ymax, xmax = box
|
||||
t_ymin = (ymin - hmin) + img_offset_y
|
||||
t_ymax = (ymax - hmin) + img_offset_y
|
||||
draw.rectangle([xmin+margin_left, t_ymin, xmax+margin_left, t_ymax], outline="red", width=3)
|
||||
|
||||
txt = annotating.render_latex_text(fb['text'], 500, (255,200,200,180), max_lines=3)
|
||||
py = max((t_ymin+t_ymax)/2 - txt.height/2, img_offset_y)
|
||||
if py < last_bot: py = last_bot + 5
|
||||
|
||||
if py + txt.height + 20 > label_img.height:
|
||||
new_l = Image.new("RGB", (label_img.width, int(py+txt.height+20)), "white")
|
||||
new_l.paste(label_img, (0,0))
|
||||
label_img = new_l
|
||||
draw = ImageDraw.Draw(label_img, "RGBA")
|
||||
|
||||
label_img.paste(txt, (10, int(py)), mask=txt)
|
||||
last_bot = py + txt.height
|
||||
|
||||
final_concats.append(label_img)
|
||||
|
||||
# Concatenate Labels
|
||||
if not final_concats: return
|
||||
|
||||
mw = max(i.width for i in final_concats)
|
||||
th = sum(i.height for i in final_concats)
|
||||
full_clean_img = Image.new("RGB", (mw, th), "white")
|
||||
y=0
|
||||
for i in final_concats:
|
||||
full_clean_img.paste(i, (0,y))
|
||||
y+=i.height
|
||||
|
||||
# 3. Overlay Manual Notes
|
||||
if notes_layer:
|
||||
# Notes layer might be different size if regenerated image size changed (e.g. deleted comments reduced height)
|
||||
# However, usually reducing content reduces height, so we align top-left.
|
||||
# But `notes_layer` is based on the "Reference.png" which had boxes.
|
||||
# The new `full_clean_img` does NOT have boxes. The dimensions should be identical
|
||||
# unless removing a feedback at the very bottom shrinks the image.
|
||||
|
||||
# We paste notes_layer on top.
|
||||
full_clean_img.paste(notes_layer, (0,0), mask=notes_layer)
|
||||
|
||||
# Save final Concat.jpg
|
||||
final_path = os.path.join(output_dir, "Concat.jpg")
|
||||
full_clean_img.save(final_path)
|
||||
print(f"Saved regenerated: {final_path}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python reading_annotations.py <Dir>")
|
||||
sys.exit(1)
|
||||
|
||||
root_dir = sys.argv[1]
|
||||
|
||||
# Load original data
|
||||
original_data = annotating.make_dictionary(root_dir)
|
||||
|
||||
# Process each Bnot folder
|
||||
for student_id in original_data.keys():
|
||||
bnot_dir = os.path.join(root_dir, "Bnot", f"Copie{student_id}")
|
||||
if os.path.exists(bnot_dir):
|
||||
print(f"Processing annotations for: {student_id}")
|
||||
actions, notes = detect_checks_and_notes(bnot_dir)
|
||||
if actions or notes:
|
||||
apply_actions_and_regenerate(root_dir, original_data, student_id, actions, notes)
|
||||
else:
|
||||
print(" No changes detected or missing files.")
|
||||
Loading…
Reference in New Issue