added measuring

S2FApp/app.py

@@ -1,9 +1,12 @@
 """
 Shape2Force (S2F) - GUI for force map prediction from bright field microscopy images.
 """
+import csv
+import io
 import os
 import sys
-import io
+import traceback
+
 import cv2
 cv2.utils.logging.setLogLevel(cv2.utils.logging.LOG_LEVEL_ERROR)
 
@@ -13,19 +16,418 @@ from PIL import Image
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
-# Ensure S2F is in path
 S2F_ROOT = os.path.dirname(os.path.abspath(__file__))
 if S2F_ROOT not in sys.path:
     sys.path.insert(0, S2F_ROOT)
 
 from utils.substrate_settings import list_substrates
 
-st.set_page_config(page_title="Shape2Force (S2F)", page_icon="🦠", layout="centered")
-st.markdown("""
+try:
+    from streamlit_drawable_canvas import st_canvas
+    HAS_DRAWABLE_CANVAS = True
+except (ImportError, AttributeError):
+    HAS_DRAWABLE_CANVAS = False
+
+# Constants
+MODEL_TYPE_LABELS = {"single_cell": "Single cell", "spheroid": "Spheroid"}
+DRAW_TOOLS = ["polygon", "rect", "circle"]
+TOOL_LABELS = {"polygon": "Polygon", "rect": "Rectangle", "circle": "Circle"}
+CANVAS_SIZE = 320
+SAMPLE_EXTENSIONS = (".tif", ".tiff", ".png", ".jpg", ".jpeg")
+CITATION = (
+    "Lautaro Baro, Kaveh Shahhosseini, Amparo Andrés-Bordería, Claudio Angione, and Maria Angeles Juanes. "
+    "**\"Shape-to-force (S2F): Predicting Cell Traction Forces from LabelFree Imaging\"**, 2026."
+)
+
+
+def _make_annotated_heatmap(heatmap_rgb, mask, fill_alpha=0.3, stroke_color=(255, 102, 0), stroke_width=2):
+    """Composite heatmap with drawn region overlay. heatmap_rgb and mask must match in size."""
+    annotated = heatmap_rgb.copy()
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # Semi-transparent orange fill
+    overlay = annotated.copy()
+    cv2.fillPoly(overlay, contours, stroke_color)
+    mask_3d = np.stack([mask] * 3, axis=-1).astype(bool)
+    annotated[mask_3d] = (
+        (1 - fill_alpha) * annotated[mask_3d].astype(np.float32)
+        + fill_alpha * overlay[mask_3d].astype(np.float32)
+    ).astype(np.uint8)
+    # Orange contour
+    cv2.drawContours(annotated, contours, -1, stroke_color, stroke_width)
+    return annotated
+
+
+def _parse_canvas_shapes_to_mask(json_data, canvas_h, canvas_w, heatmap_h, heatmap_w):
+    """
+    Parse drawn shapes from streamlit-drawable-canvas json_data and create a binary mask
+    in heatmap coordinates. Returns (mask, num_shapes) or (None, 0) if no valid shapes.
+    """
+    if not json_data or "objects" not in json_data or not json_data["objects"]:
+        return None, 0
+    scale_x = heatmap_w / canvas_w
+    scale_y = heatmap_h / canvas_h
+    mask = np.zeros((heatmap_h, heatmap_w), dtype=np.uint8)
+    count = 0
+    for obj in json_data["objects"]:
+        obj_type = obj.get("type", "")
+        pts = []
+        if obj_type == "rect":
+            left = obj.get("left", 0)
+            top = obj.get("top", 0)
+            w = obj.get("width", 0)
+            h = obj.get("height", 0)
+            pts = np.array([
+                [left, top], [left + w, top], [left + w, top + h], [left, top + h]
+            ], dtype=np.float32)
+        elif obj_type == "circle" or obj_type == "ellipse":
+            left = obj.get("left", 0)
+            top = obj.get("top", 0)
+            width = obj.get("width", 0)
+            height = obj.get("height", 0)
+            radius = obj.get("radius", 0)
+            angle_deg = obj.get("angle", 0)
+            if radius > 0:
+                # Circle: (left, top) is mouse start point, not center.
+                # Center = start + radius * (cos(angle), sin(angle))
+                rx = ry = radius
+                angle_rad = np.deg2rad(angle_deg)
+                cx = left + radius * np.cos(angle_rad)
+                cy = top + radius * np.sin(angle_rad)
+            else:
+                # Ellipse: left, top = top-left of bounding box
+                rx = width / 2 if width > 0 else 0
+                ry = height / 2 if height > 0 else 0
+                if rx <= 0 or ry <= 0:
+                    continue
+                cx = left + rx
+                cy = top + ry
+            if rx <= 0 or ry <= 0:
+                continue
+            n = 32
+            angles = np.linspace(0, 2 * np.pi, n, endpoint=False)
+            pts = np.column_stack([cx + rx * np.cos(angles), cy + ry * np.sin(angles)]).astype(np.float32)
+        elif obj_type == "path":
+            path = obj.get("path", [])
+            for cmd in path:
+                if isinstance(cmd, (list, tuple)) and len(cmd) >= 3:
+                    if cmd[0] in ("M", "L"):
+                        pts.append([float(cmd[1]), float(cmd[2])])
+                    elif cmd[0] == "Q" and len(cmd) >= 5:
+                        pts.append([float(cmd[3]), float(cmd[4])])
+                    elif cmd[0] == "C" and len(cmd) >= 7:
+                        pts.append([float(cmd[5]), float(cmd[6])])
+            if len(pts) < 3:
+                continue
+            pts = np.array(pts, dtype=np.float32)
+        else:
+            continue
+        pts[:, 0] *= scale_x
+        pts[:, 1] *= scale_y
+        pts = np.clip(pts, 0, [heatmap_w - 1, heatmap_h - 1]).astype(np.int32)
+        cv2.fillPoly(mask, [pts], 1)
+        count += 1
+    return mask, count
+
+
+def _heatmap_to_png_bytes(scaled_heatmap):
+    """Convert scaled heatmap (float 0-1) to PNG bytes buffer."""
+    heatmap_uint8 = (np.clip(scaled_heatmap, 0, 1) * 255).astype(np.uint8)
+    heatmap_rgb = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
+    heatmap_rgb = cv2.cvtColor(heatmap_rgb, cv2.COLOR_BGR2RGB)
+    buf = io.BytesIO()
+    Image.fromarray(heatmap_rgb).save(buf, format="PNG")
+    buf.seek(0)
+    return buf
+
+
+def _build_original_vals(scaled_heatmap, pixel_sum, force, force_scale):
+    """Build original_vals dict for measure tool."""
+    return {
+        "pixel_sum": pixel_sum * force_scale,
+        "force": force * force_scale,
+        "max": float(np.max(scaled_heatmap)),
+        "mean": float(np.mean(scaled_heatmap)),
+    }
+
+
+def _render_result_display(img, scaled_heatmap, pixel_sum, force, force_scale, key_img, download_key_suffix=""):
+    """Render prediction result: plot, metrics, expander, and download/measure buttons."""
+    buf_hm = _heatmap_to_png_bytes(scaled_heatmap)
+    base_name = os.path.splitext(key_img or "image")[0]
+    main_csv_rows = [
+        ["image", "Sum of all pixels", "Cell force (scaled)", "Heatmap max", "Heatmap mean"],
+        [base_name, f"{pixel_sum * force_scale:.2f}", f"{force * force_scale:.2f}",
+         f"{np.max(scaled_heatmap):.4f}", f"{np.mean(scaled_heatmap):.4f}"],
+    ]
+    buf_main_csv = io.StringIO()
+    csv.writer(buf_main_csv).writerows(main_csv_rows)
+
+    tit1, tit2 = st.columns(2)
+    with tit1:
+        st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Input: Bright-field image</p>', unsafe_allow_html=True)
+    with tit2:
+        st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Output: Predicted traction force map</p>', unsafe_allow_html=True)
+    fig_pl = make_subplots(rows=1, cols=2)
+    fig_pl.add_trace(go.Heatmap(z=img, colorscale="gray", showscale=False), row=1, col=1)
+    fig_pl.add_trace(go.Heatmap(z=scaled_heatmap, colorscale="Jet", zmin=0, zmax=1, showscale=True,
+        colorbar=dict(len=0.4, thickness=12)), row=1, col=2)
+    fig_pl.update_layout(
+        height=400,
+        margin=dict(l=10, r=10, t=10, b=10),
+        xaxis=dict(scaleanchor="y", scaleratio=1),
+        xaxis2=dict(scaleanchor="y2", scaleratio=1),
+    )
+    fig_pl.update_xaxes(showticklabels=False)
+    fig_pl.update_yaxes(showticklabels=False, autorange="reversed")
+    st.plotly_chart(fig_pl, use_container_width=True, config={"displayModeBar": True, "responsive": True})
+
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("Sum of all pixels", f"{pixel_sum * force_scale:.2f}", help="Raw sum of all pixel values in the force map")
+    with col2:
+        st.metric("Cell force (scaled)", f"{force * force_scale:.2f}", help="Total traction force in physical units")
+    with col3:
+        st.metric("Heatmap max", f"{np.max(scaled_heatmap):.4f}", help="Peak force intensity in the map")
+    with col4:
+        st.metric("Heatmap mean", f"{np.mean(scaled_heatmap):.4f}", help="Average force intensity")
+
+    with st.expander("How to read the results"):
+        st.markdown("""
+**Input (left):** Bright-field microscopy image of a cell or spheroid on a substrate.  
+This is the raw image you provided—it shows cell shape but not forces.
+
+**Output (right):** Predicted traction force map.  
+- **Color** indicates force magnitude: blue = low, red = high  
+- **Brighter/warmer colors** = stronger forces exerted by the cell on the substrate  
+- Values are normalized to [0, 1] for visualization
+
+**Metrics:**
+- **Sum of all pixels:** Total force is the sum of all pixels in the force map. Each pixel represents the magnitude of force at that location; summing them gives the overall traction.
+- **Cell force (scaled):** Total traction force in physical units (scaled by substrate stiffness)
+- **Heatmap max/mean:** Peak and average force intensity in the map
+        """)
+
+    original_vals = _build_original_vals(scaled_heatmap, pixel_sum, force, force_scale)
+    btn_col1, btn_col2, btn_col3 = st.columns(3)
+    with btn_col1:
+        if HAS_DRAWABLE_CANVAS and st_dialog:
+            if st.button("Measure tool", key="open_measure", icon=":material/straighten:"):
+                st.session_state["open_measure_dialog"] = True
+                st.rerun()
+        elif HAS_DRAWABLE_CANVAS:
+            with st.expander("Measure tool"):
+                _render_region_canvas(
+                    scaled_heatmap,
+                    bf_img=img,
+                    original_vals=original_vals,
+                    key_suffix="expander",
+                    input_filename=key_img,
+                )
+        else:
+            st.caption("Install `streamlit-drawable-canvas-fix` for region measurement: `pip install streamlit-drawable-canvas-fix`")
+    with btn_col2:
+        st.download_button(
+            "Download heatmap",
+            width="stretch",
+            data=buf_hm.getvalue(),
+            file_name="s2f_heatmap.png",
+            mime="image/png",
+            key=f"download_heatmap{download_key_suffix}",
+            icon=":material/download:",
+        )
+    with btn_col3:
+        st.download_button(
+            "Download values",
+            width="stretch",
+            data=buf_main_csv.getvalue(),
+            file_name=f"{base_name}_main_values.csv",
+            mime="text/csv",
+            key=f"download_main_values{download_key_suffix}",
+            icon=":material/download:",
+        )
+
+
+def _compute_region_metrics(scaled_heatmap, mask, original_vals=None):
+    """Compute region metrics from mask. Returns dict with area_px, force_sum, density, etc."""
+    area_px = int(np.sum(mask))
+    region_values = scaled_heatmap * mask
+    region_nonzero = region_values[mask > 0]
+    force_sum = float(np.sum(region_values))
+    density = force_sum / area_px if area_px > 0 else 0
+    region_max = float(np.max(region_nonzero)) if len(region_nonzero) > 0 else 0
+    region_mean = float(np.mean(region_nonzero)) if len(region_nonzero) > 0 else 0
+    region_force_scaled = (
+        force_sum * (original_vals["force"] / original_vals["pixel_sum"])
+        if original_vals and original_vals.get("pixel_sum", 0) > 0
+        else force_sum
+    )
+    return {
+        "area_px": area_px,
+        "force_sum": force_sum,
+        "density": density,
+        "max": region_max,
+        "mean": region_mean,
+        "force_scaled": region_force_scaled,
+    }
+
+
+def _render_region_metrics_and_downloads(metrics, heatmap_rgb, mask, input_filename, key_suffix, has_original_vals):
+    """Render region metrics and download buttons."""
+    base_name = os.path.splitext(input_filename or "image")[0]
+    st.markdown("**Region (drawn)**")
+    if has_original_vals:
+        r1, r2, r3, r4, r5, r6 = st.columns(6)
+        with r1:
+            st.metric("Area", f"{metrics['area_px']:,}")
+        with r2:
+            st.metric("F.sum", f"{metrics['force_sum']:.3f}")
+        with r3:
+            st.metric("Force", f"{metrics['force_scaled']:.1f}")
+        with r4:
+            st.metric("Max", f"{metrics['max']:.3f}")
+        with r5:
+            st.metric("Mean", f"{metrics['mean']:.3f}")
+        with r6:
+            st.metric("Density", f"{metrics['density']:.4f}")
+        csv_rows = [
+            ["image", "Area", "F.sum", "Force", "Max", "Mean", "Density"],
+            [base_name, metrics["area_px"], f"{metrics['force_sum']:.3f}", f"{metrics['force_scaled']:.1f}",
+             f"{metrics['max']:.3f}", f"{metrics['mean']:.3f}", f"{metrics['density']:.4f}"],
+        ]
+    else:
+        c1, c2, c3 = st.columns(3)
+        with c1:
+            st.metric("Area (px²)", f"{metrics['area_px']:,}")
+        with c2:
+            st.metric("Force sum", f"{metrics['force_sum']:.4f}")
+        with c3:
+            st.metric("Density", f"{metrics['density']:.6f}")
+        csv_rows = [
+            ["image", "Area", "Force sum", "Density"],
+            [base_name, metrics["area_px"], f"{metrics['force_sum']:.4f}", f"{metrics['density']:.6f}"],
+        ]
+    buf_csv = io.StringIO()
+    csv.writer(buf_csv).writerows(csv_rows)
+    buf_img = io.BytesIO()
+    Image.fromarray(_make_annotated_heatmap(heatmap_rgb, mask)).save(buf_img, format="PNG")
+    buf_img.seek(0)
+    dl_col1, dl_col2 = st.columns(2)
+    with dl_col1:
+        st.download_button("Download values", data=buf_csv.getvalue(),
+            file_name=f"{base_name}_region_values.csv", mime="text/csv",
+            key=f"download_region_values_{key_suffix}", icon=":material/download:")
+    with dl_col2:
+        st.download_button("Download annotated heatmap", data=buf_img.getvalue(),
+            file_name=f"{base_name}_annotated_heatmap.png", mime="image/png",
+            key=f"download_annotated_{key_suffix}", icon=":material/image:")
+
+
+def _render_region_canvas(scaled_heatmap, bf_img=None, original_vals=None, key_suffix="", input_filename=None):
+    """Render drawable canvas and region metrics. Used in dialog or expander."""
+    h, w = scaled_heatmap.shape
+    heatmap_display = (np.clip(scaled_heatmap, 0, 1) * 255).astype(np.uint8)
+    heatmap_rgb = cv2.cvtColor(cv2.applyColorMap(heatmap_display, cv2.COLORMAP_JET), cv2.COLOR_BGR2RGB)
+    pil_bg = Image.fromarray(heatmap_rgb).resize((CANVAS_SIZE, CANVAS_SIZE), Image.Resampling.LANCZOS)
+
+    st.markdown("""
     <style>
-    section[data-testid="stSidebar"] { width: 380px !important; }
+        [data-testid="stDialog"] [data-testid="stSelectbox"], [data-testid="stExpander"] [data-testid="stSelectbox"],
+        [data-testid="stDialog"] [data-testid="stSelectbox"] > div, [data-testid="stExpander"] [data-testid="stSelectbox"] > div {
+            width: 100% !important; max-width: 100% !important;
+        }
+        [data-testid="stDialog"] [data-testid="stMetric"] label, [data-testid="stDialog"] [data-testid="stMetric"] [data-testid="stMetricValue"],
+        [data-testid="stExpander"] [data-testid="stMetric"] label, [data-testid="stExpander"] [data-testid="stMetric"] [data-testid="stMetricValue"] {
+            font-size: 0.95rem !important;
+        }
+        [data-testid="stDialog"] img, [data-testid="stExpander"] img { border-radius: 0 !important; }
     </style>
     """, unsafe_allow_html=True)
+
+    if bf_img is not None:
+        bf_resized = cv2.resize(bf_img, (CANVAS_SIZE, CANVAS_SIZE))
+        bf_rgb = cv2.cvtColor(bf_resized, cv2.COLOR_GRAY2RGB) if bf_img.ndim == 2 else cv2.cvtColor(bf_resized, cv2.COLOR_BGR2RGB)
+        left_col, right_col = st.columns(2, gap=None)
+        with left_col:
+            draw_mode = st.selectbox("Tool", DRAW_TOOLS, format_func=lambda x: TOOL_LABELS[x], key=f"draw_mode_region_{key_suffix}")
+            st.caption("Left-click add, right-click close.  \nForce map (draw region)")
+            canvas_result = st_canvas(
+                fill_color="rgba(255, 165, 0, 0.3)", stroke_width=2, stroke_color="#ff6600",
+                background_image=pil_bg, drawing_mode=draw_mode, update_streamlit=True,
+                height=CANVAS_SIZE, width=CANVAS_SIZE, display_toolbar=True,
+                key=f"region_measure_canvas_{key_suffix}",
+            )
+        with right_col:
+            if original_vals:
+                st.markdown('<p style="font-weight: 400; color: #334155; font-size: 0.95rem; margin: 0 20px 4px 4px;">Full map</p>', unsafe_allow_html=True)
+                st.markdown(f"""
+                <div style="width: 100%; box-sizing: border-box; border: 1px solid #e2e8f0; border-radius: 10px;
+                    padding: 10px 12px; margin: 0 10px 20px 10px; background: linear-gradient(145deg, #f8fafc 0%, #f1f5f9 100%);
+                    box-shadow: 0 1px 3px rgba(0,0,0,0.06);">
+                    <div style="display: flex; flex-wrap: wrap; gap: 5px; font-size: 0.9rem;">
+                        <span><strong>Sum:</strong> {original_vals['pixel_sum']:.1f}</span>
+                        <span><strong>Force:</strong> {original_vals['force']:.1f}</span>
+                        <span><strong>Max:</strong> {original_vals['max']:.3f}</span>
+                        <span><strong>Mean:</strong> {original_vals['mean']:.3f}</span>
+                    </div>
+                </div>
+                """, unsafe_allow_html=True)
+            st.caption("Bright-field")
+            st.image(bf_rgb, width=CANVAS_SIZE)
+    else:
+        st.markdown("**Draw a region** on the heatmap.")
+        draw_mode = st.selectbox("Drawing tool", DRAW_TOOLS,
+            format_func=lambda x: "Polygon (free shape)" if x == "polygon" else TOOL_LABELS[x],
+            key=f"draw_mode_region_{key_suffix}")
+        st.caption("Polygon: left-click to add points, right-click to close.")
+        canvas_result = st_canvas(
+            fill_color="rgba(255, 165, 0, 0.3)", stroke_width=2, stroke_color="#ff6600",
+            background_image=pil_bg, drawing_mode=draw_mode, update_streamlit=True,
+            height=CANVAS_SIZE, width=CANVAS_SIZE, display_toolbar=True,
+            key=f"region_measure_canvas_{key_suffix}",
+        )
+
+    if canvas_result.json_data:
+        mask, n = _parse_canvas_shapes_to_mask(canvas_result.json_data, CANVAS_SIZE, CANVAS_SIZE, h, w)
+        if mask is not None and n > 0:
+            metrics = _compute_region_metrics(scaled_heatmap, mask, original_vals)
+            _render_region_metrics_and_downloads(metrics, heatmap_rgb, mask, input_filename, key_suffix, original_vals is not None)
+
+
+st_dialog = getattr(st, "dialog", None) or getattr(st, "experimental_dialog", None)
+if HAS_DRAWABLE_CANVAS and st_dialog:
+    @st_dialog("Measure tool", width="medium")
+    def measure_region_dialog():
+        scaled_heatmap = st.session_state.get("measure_scaled_heatmap")
+        if scaled_heatmap is None:
+            st.warning("No prediction available to measure.")
+            return
+        bf_img = st.session_state.get("measure_bf_img")
+        original_vals = st.session_state.get("measure_original_vals")
+        input_filename = st.session_state.get("measure_input_filename", "image")
+        _render_region_canvas(scaled_heatmap, bf_img=bf_img, original_vals=original_vals, key_suffix="dialog", input_filename=input_filename)
+else:
+    def measure_region_dialog():
+        pass  # no-op when canvas or dialog not available
+
+
+st.set_page_config(page_title="Shape2Force (S2F)", page_icon="🦠", layout="centered")
+st.markdown("""
+<style>
+section[data-testid="stSidebar"] { width: 380px !important; }
+div[data-testid="stHorizontalBlock"]:has([data-testid="stDownloadButton"]):has([data-testid="stButton"]) > div {
+    flex: 1 1 0 !important; min-width: 0 !important;
+}
+div[data-testid="stHorizontalBlock"]:has([data-testid="stDownloadButton"]):has([data-testid="stButton"]) button {
+    width: 100% !important; min-width: 100px !important; white-space: nowrap !important;
+}
+div[data-testid="stHorizontalBlock"]:has([data-testid="stDownloadButton"]):has([data-testid="stButton"]) > div:nth-child(1) button {
+    background-color: #0d9488 !important; color: white !important; border-color: #0d9488 !important;
+}
+div[data-testid="stHorizontalBlock"]:has([data-testid="stDownloadButton"]):has([data-testid="stButton"]) > div:nth-child(1) button:hover {
+    background-color: #0f766e !important; border-color: #0f766e !important; color: white !important;
+}
+</style>
+""", unsafe_allow_html=True)
 st.title("🦠 Shape2Force (S2F)")
 st.caption("Predict traction force maps from bright-field microscopy images of cells or spheroids")
 
@@ -42,14 +444,12 @@ sample_base = os.path.join(S2F_ROOT, "samples")
 sample_single_cell = os.path.join(sample_base, "single_cell")
 sample_spheroid = os.path.join(sample_base, "spheroid")
 
-SAMPLE_EXTENSIONS = (".tif", ".tiff", ".png", ".jpg", ".jpeg")
-
 
 def get_ckp_files_for_model(model_type):
     """Return list of .pth files in the checkpoint folder for the given model type."""
     folder = ckp_single_cell if model_type == "single_cell" else ckp_spheroid
     if os.path.isdir(folder):
-        return sorted([f for f in os.listdir(folder) if f.endswith(".pth")])
+        return sorted(f for f in os.listdir(folder) if f.endswith(".pth"))
     return []
 
 
@@ -57,8 +457,7 @@ def get_sample_files_for_model(model_type):
     """Return list of sample images in the sample folder for the given model type."""
     folder = sample_single_cell if model_type == "single_cell" else sample_spheroid
     if os.path.isdir(folder):
-        return sorted([f for f in os.listdir(folder)
-                       if f.lower().endswith(SAMPLE_EXTENSIONS)])
+        return sorted(f for f in os.listdir(folder) if f.lower().endswith(SAMPLE_EXTENSIONS))
     return []
 
 # Sidebar: model configuration
@@ -67,11 +466,11 @@ with st.sidebar:
     model_type = st.radio(
         "Model type",
         ["single_cell", "spheroid"],
-        format_func=lambda x: "Single cell" if x == "single_cell" else "Spheroid",
+        format_func=lambda x: MODEL_TYPE_LABELS[x],
         horizontal=False,
         help="Single cell: substrate-aware force prediction. Spheroid: spheroid force maps.",
     )
-    st.caption(f"Inference mode: **{'Single cell' if model_type == 'single_cell' else 'Spheroid'}**")
+    st.caption(f"Inference mode: **{MODEL_TYPE_LABELS[model_type]}**")
 
     ckp_files = get_ckp_files_for_model(model_type)
     ckp_folder = ckp_single_cell if model_type == "single_cell" else ckp_spheroid
@@ -106,8 +505,6 @@ with st.sidebar:
                 manual_young = st.number_input("Young's modulus (Pa)", min_value=100.0, max_value=100000.0,
                                                value=6000.0, step=100.0, format="%.0f")
                 substrate_config = {"pixelsize": manual_pixelsize, "young": manual_young}
-            else:
-                substrate_config = None
         except FileNotFoundError:
             st.error("config/substrate_settings.json not found")
 
@@ -170,8 +567,7 @@ col_btn, col_model, col_path = st.columns([1, 1, 3])
 with col_btn:
     run = st.button("Run prediction", type="primary")
 with col_model:
-    model_label = "Single cell" if model_type == "single_cell" else "Spheroid"
-    st.markdown(f"<span style='display: inline-flex; align-items: center; height: 38px;'>{model_label}</span>", unsafe_allow_html=True)
+    st.markdown(f"<span style='display: inline-flex; align-items: center; height: 38px;'>{MODEL_TYPE_LABELS[model_type]}</span>", unsafe_allow_html=True)
 with col_path:
     ckp_path = f"ckp/{ckp_subfolder_name}/{checkpoint}" if checkpoint else f"ckp/{ckp_subfolder_name}/"
     st.markdown(f"<span style='display: inline-flex; align-items: center; height: 38px;'>Checkpoint: <code>{ckp_path}</code></span>", unsafe_allow_html=True)
@@ -208,69 +604,9 @@ if just_ran:
 
                 st.success("Prediction complete!")
 
-                # Apply force scale to displayed heatmap
                 scaled_heatmap = heatmap * force_scale
 
-                # Visualization - Plotly with zoom/pan, annotated (titles in Streamlit to avoid clipping)
-                tit1, tit2 = st.columns(2)
-                with tit1:
-                    st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Input: Bright-field image</p>', unsafe_allow_html=True)
-                with tit2:
-                    st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Output: Predicted traction force map</p>', unsafe_allow_html=True)
-                fig_pl = make_subplots(rows=1, cols=2)
-                fig_pl.add_trace(go.Heatmap(z=img, colorscale="gray", showscale=False), row=1, col=1)
-                fig_pl.add_trace(go.Heatmap(z=scaled_heatmap, colorscale="Jet", zmin=0, zmax=1, showscale=True,
-                    colorbar=dict(len=0.4, thickness=12)), row=1, col=2)
-                fig_pl.update_layout(
-                    height=400,
-                    margin=dict(l=10, r=10, t=10, b=10),
-                    xaxis=dict(scaleanchor="y", scaleratio=1),
-                    xaxis2=dict(scaleanchor="y2", scaleratio=1),
-                )
-                fig_pl.update_xaxes(showticklabels=False)
-                fig_pl.update_yaxes(showticklabels=False, autorange="reversed")
-                st.plotly_chart(fig_pl, use_container_width=True)
-
-                # Metrics with help (below plot) - use scaled values
-                col1, col2, col3, col4 = st.columns(4)
-                with col1:
-                    st.metric("Sum of all pixels", f"{pixel_sum * force_scale:.2f}", help="Raw sum of all pixel values in the force map")
-                with col2:
-                    st.metric("Cell force (scaled)", f"{force * force_scale:.2f}", help="Total traction force in physical units")
-                with col3:
-                    st.metric("Heatmap max", f"{np.max(scaled_heatmap):.4f}", help="Peak force intensity in the map")
-                with col4:
-                    st.metric("Heatmap mean", f"{np.mean(scaled_heatmap):.4f}", help="Average force intensity")
-
-                # How to read (below numbers)
-                with st.expander("ℹ️ How to read the results"):
-                    st.markdown("""
-**Input (left):** Bright-field microscopy image of a cell or spheroid on a substrate.  
-This is the raw image you provided—it shows cell shape but not forces.
-
-**Output (right):** Predicted traction force map.  
-- **Color** indicates force magnitude: blue = low, red = high  
-- **Brighter/warmer colors** = stronger forces exerted by the cell on the substrate  
-- Values are normalized to [0, 1] for visualization
-
-**Metrics:**
-- **Sum of all pixels:** Total force is the sum of all pixels in the force map. Each pixel represents the magnitude of force at that location; summing them gives the overall traction.
-- **Cell force (scaled):** Total traction force in physical units (scaled by substrate stiffness)
-- **Heatmap max/mean:** Peak and average force intensity in the map
-                    """)
-
-                # Download (scaled heatmap)
-                heatmap_uint8 = (np.clip(scaled_heatmap, 0, 1) * 255).astype(np.uint8)
-                heatmap_rgb = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
-                heatmap_rgb = cv2.cvtColor(heatmap_rgb, cv2.COLOR_BGR2RGB)
-                pil_heatmap = Image.fromarray(heatmap_rgb)
-                buf_hm = io.BytesIO()
-                pil_heatmap.save(buf_hm, format="PNG")
-                buf_hm.seek(0)
-                st.download_button("Download Heatmap", data=buf_hm.getvalue(),
-                                   file_name="s2f_heatmap.png", mime="image/png", key="download_heatmap")
-
-                # Store in session state so results persist when user clicks Download
+                # Store result and measure data before rendering (Measure click survives rerun)
                 cache_key = (model_type, checkpoint, key_img)
                 st.session_state["prediction_result"] = {
                     "img": img.copy(),
@@ -279,77 +615,39 @@ This is the raw image you provided—it shows cell shape but not forces.
                     "pixel_sum": pixel_sum,
                     "cache_key": cache_key,
                 }
+                st.session_state["measure_scaled_heatmap"] = scaled_heatmap.copy()
+                st.session_state["measure_bf_img"] = img.copy()
+                st.session_state["measure_input_filename"] = key_img or "image"
+                st.session_state["measure_original_vals"] = _build_original_vals(scaled_heatmap, pixel_sum, force, force_scale)
+
+                _render_result_display(img, scaled_heatmap, pixel_sum, force, force_scale, key_img)
 
         except Exception as e:
             st.error(f"Prediction failed: {e}")
-            import traceback
             st.code(traceback.format_exc())
 
 elif has_cached:
-    # Show cached results (e.g. after clicking Download)
     r = st.session_state["prediction_result"]
     img, heatmap, force, pixel_sum = r["img"], r["heatmap"], r["force"], r["pixel_sum"]
     scaled_heatmap = heatmap * force_scale
-    st.success("Prediction complete!")
-    tit1, tit2 = st.columns(2)
-    with tit1:
-        st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Input: Bright-field image</p>', unsafe_allow_html=True)
-    with tit2:
-        st.markdown('<p style="font-size: 1.1rem; color: black; font-weight: 600;">Output: Predicted traction force map</p>', unsafe_allow_html=True)
-    fig_pl = make_subplots(rows=1, cols=2)
-    fig_pl.add_trace(go.Heatmap(z=img, colorscale="gray", showscale=False), row=1, col=1)
-    fig_pl.add_trace(go.Heatmap(z=scaled_heatmap, colorscale="Jet", zmin=0, zmax=1, showscale=True,
-        colorbar=dict(len=0.4, thickness=12)), row=1, col=2)
-    fig_pl.update_layout(height=400, margin=dict(l=10, r=10, t=10, b=10),
-                         xaxis=dict(scaleanchor="y", scaleratio=1),
-                         xaxis2=dict(scaleanchor="y2", scaleratio=1))
-    fig_pl.update_xaxes(showticklabels=False)
-    fig_pl.update_yaxes(showticklabels=False, autorange="reversed")
-    st.plotly_chart(fig_pl, use_container_width=True)
-    col1, col2, col3, col4 = st.columns(4)
-    with col1:
-        st.metric("Sum of all pixels", f"{pixel_sum * force_scale:.2f}", help="Raw sum of all pixel values in the force map")
-    with col2:
-        st.metric("Cell force (scaled)", f"{force * force_scale:.2f}", help="Total traction force in physical units")
-    with col3:
-        st.metric("Heatmap max", f"{np.max(scaled_heatmap):.4f}", help="Peak force intensity in the map")
-    with col4:
-        st.metric("Heatmap mean", f"{np.mean(scaled_heatmap):.4f}", help="Average force intensity")
-    with st.expander("ℹ️ How to read the results"):
-        st.markdown("""
-**Input (left):** Bright-field microscopy image of a cell or spheroid on a substrate.  
-This is the raw image you provided—it shows cell shape but not forces.
 
-**Output (right):** Predicted traction force map.  
-- **Color** indicates force magnitude: blue = low, red = high  
-- **Brighter/warmer colors** = stronger forces exerted by the cell on the substrate  
-- Values are normalized to [0, 1] for visualization
+    st.session_state["measure_scaled_heatmap"] = scaled_heatmap.copy()
+    st.session_state["measure_bf_img"] = img.copy()
+    st.session_state["measure_input_filename"] = key_img or "image"
+    st.session_state["measure_original_vals"] = _build_original_vals(scaled_heatmap, pixel_sum, force, force_scale)
 
-**Metrics:**
-- **Sum of all pixels:** Total force is the sum of all pixels in the force map. Each pixel represents the magnitude of force at that location; summing them gives the overall traction.
-- **Cell force (scaled):** Total traction force in physical units (scaled by substrate stiffness)
-- **Heatmap max/mean:** Peak and average force intensity in the map
-        """)
-    heatmap_uint8 = (np.clip(scaled_heatmap, 0, 1) * 255).astype(np.uint8)
-    heatmap_rgb = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
-    heatmap_rgb = cv2.cvtColor(heatmap_rgb, cv2.COLOR_BGR2RGB)
-    pil_heatmap = Image.fromarray(heatmap_rgb)
-    buf_hm = io.BytesIO()
-    pil_heatmap.save(buf_hm, format="PNG")
-    buf_hm.seek(0)
-    st.download_button("Download Heatmap", data=buf_hm.getvalue(),
-                       file_name="s2f_heatmap.png", mime="image/png", key="download_cached")
+    if st.session_state.pop("open_measure_dialog", False):
+        measure_region_dialog()
+
+    st.success("Prediction complete!")
+    _render_result_display(img, scaled_heatmap, pixel_sum, force, force_scale, key_img, download_key_suffix="_cached")
 
 elif run and not checkpoint:
     st.warning("Please add checkpoint files to the ckp/ folder and select one.")
 elif run and not has_image:
     st.warning("Please upload an image or select an example.")
 
-# Footer
 st.sidebar.divider()
 st.sidebar.caption(f"Examples: `samples/{ckp_subfolder_name}/`")
 st.sidebar.caption("If you find this software useful, please cite:")
-st.sidebar.caption(
-    "Lautaro Baro, Kaveh Shahhosseini, Amparo Andrés-Bordería, Claudio Angione, and Maria Angeles Juanes. "
-    "**\"Shape-to-force (S2F): Predicting Cell Traction Forces from LabelFree Imaging\"**, 2026."
-)
+st.sidebar.caption(CITATION)

S2FApp/requirements.txt

@@ -4,6 +4,7 @@ torchvision>=0.15.0
 numpy>=1.20.0
 opencv-python>=4.5.0
 streamlit>=1.28.0
+streamlit-drawable-canvas-fix>=0.9.8
 matplotlib>=3.5.0
 Pillow>=9.0.0
 plotly>=5.14.0