Mercurial > repos > bgruening > json2yolosegment

changeset 1:dfda27273ead draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 9685f843a52451b3416416094cc0e740f8825dcc
author bgruening
date Mon, 07 Jul 2025 06:47:08 +0000
parents 252fd085940d
children 158e6ce48345
files json2yolosegment.xml macros.xml test-data/0001 test-data/0001.jpg test-data/0003 test-data/0003.jpg test-data/0006 test-data/0006.jpg test-data/0007 test-data/0007.jpg test-data/0013 test-data/0013.jpg test-data/0016 test-data/0016.jpg test-data/0018 test-data/0018.jpg test-data/0021 test-data/0021.jpg test-data/bus.jpg test-data/bus.txt test-data/bus_detect.txt test-data/bus_mask.tiff test-data/bus_overlay.jpg test-data/bus_seg.jpg test-data/in_json1_noext test-data/in_json1_noext.txt test-data/in_json_noext test-data/in_json_noext.txt test-data/results_plot1.png test-data/yolo-test-classes.txt yolov8.py
diffstat 27 files changed, 761 insertions(+), 31 deletions(-) [+]
line wrap: on
line diff
--- a/json2yolosegment.xml	Fri Jun 13 11:23:35 2025 +0000
+++ b/json2yolosegment.xml	Mon Jul 07 06:47:08 2025 +0000
@@ -5,16 +5,24 @@
     </macros>
     <expand macro="creator" />
     <expand macro="edam" />
+    <expand macro="requirements" />
     <command detect_errors="aggressive">
     <![CDATA[
       mkdir ./input ./output &&
 
       #for $filename in $in_json:
-          ln -s '$filename' './input/${filename.element_identifier}' &&
+          #if $filename.element_identifier.endswith($filename.ext)
+              ln -s '$filename' './input/${filename.element_identifier}' &&
+	  #else:
+	      ln -s '$filename' './input/${filename.element_identifier}.${filename.ext}' &&
+          #end if
       #end for
-		    
-      python '$__tool_directory__/json2yolosegment.py' -i ./input/ -o ./output -c '$class_name'
-		
+
+      python '$__tool_directory__/json2yolosegment.py'
+	-i ./input/
+	-o ./output
+	-c '$class_name'
+	
     ]]>
     </command>
     <inputs>
@@ -45,6 +53,24 @@
                 </element>
             </output_collection>
         </test>
+        <test>
+            <param name="in_json" value="in_json_noext,in_json1_noext" />
+            <param name="class_name" value="class_names.txt" />
+	    <output_collection name="output_yolo">
+                <element name="in_json1_noext">
+                    <assert_contents>
+                        <has_n_lines n="1" />
+                        <has_text text="0.7710371819960861" />
+                    </assert_contents>
+                </element>
+                <element name="in_json_noext">
+                    <assert_contents>
+                        <has_n_lines n="1" />
+                        <has_text text="0.8532289628180039" />
+                    </assert_contents>
+                </element>
+            </output_collection>
+        </test>
     </tests>
     <help>
 <![CDATA[
--- a/macros.xml	Fri Jun 13 11:23:35 2025 +0000
+++ b/macros.xml	Mon Jul 07 06:47:08 2025 +0000
@@ -1,6 +1,6 @@
 <macros>
     <token name="@TOOL_VERSION@">8.3.0</token>
-    <token name="@VERSION_SUFFIX@">0</token>
+    <token name="@VERSION_SUFFIX@">1</token>
     <xml name="creator">
         <creator>
             <person name="Yi Sun" email="sunyi000@gmail.com" />
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/0001	Mon Jul 07 06:47:08 2025 +0000
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Binary file test-data/0006.jpg has changed
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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Binary file test-data/bus.jpg has changed
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/bus_detect.txt	Mon Jul 07 06:47:08 2025 +0000
@@ -0,0 +1,5 @@
+0 670.23 388.68 809.51 877.14 0.88 
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Binary file test-data/bus_mask.tiff has changed
Binary file test-data/bus_overlay.jpg has changed
Binary file test-data/bus_seg.jpg has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in_json1_noext	Mon Jul 07 06:47:08 2025 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in_json1_noext.txt	Mon Jul 07 06:47:08 2025 +0000
@@ -0,0 +1,1 @@
+0 0.7710371819960861 0.20039100684261973 0.7592954990215264 0.2013685239491691 0.735812133072407 0.19648093841642228 0.6927592954990215 0.19745845552297164 0.6810176125244618 0.1935483870967742 0.649706457925636 0.1906158357771261 0.6203522504892368 0.19452590420332355 0.5792563600782779 0.20918866080156404 0.5342465753424658 0.2130987292277615 0.47162426614481406 0.2434017595307918 0.42661448140900193 0.28152492668621704 0.4070450097847358 0.30791788856304986 0.37377690802348335 0.386119257086999 0.37377690802348335 0.43206256109481916 0.3542074363992172 0.4701857282502444 0.3522504892367906 0.49853372434017595 0.3424657534246575 0.5259042033235581 0.3405088062622309 0.5669599217986315 0.33268101761252444 0.5845552297165201 0.33463796477495106 0.5972629521016618 0.3268101761252446 0.6041055718475073 0.31898238747553814 0.6598240469208211 0.30919765166340507 0.6666666666666666 0.30919765166340507 0.6754643206256109 0.2876712328767123 0.6959921798631477 0.27984344422700586 0.7223851417399805 0.29158512720156554 0.729227761485826 0.29354207436399216 0.7350928641251222 0.3131115459882583 0.7438905180840665 0.32289628180039137 0.7556207233626588 0.33659491193737767 0.761485826001955 0.35812133072407043 0.7781036168132942 0.3913894324853229 0.7927663734115347 0.40313111545988256 0.7956989247311828 0.4090019569471624 0.793743890518084 0.4207436399217221 0.7986314760508308 0.43052837573385516 0.7976539589442815 0.44422700587084146 0.8005865102639296 0.45596868884540115 0.7986314760508308 0.5283757338551859 0.7986314760508308 0.5342465753424658 0.7947214076246334 0.5636007827788649 0.7927663734115347 0.6164383561643836 0.7820136852394917 0.6223091976516634 0.7781036168132942 0.6281800391389433 0.7790811339198436 0.6516634050880626 0.7595307917888563 0.6614481409001957 0.7556207233626588 0.675146771037182 0.7360703812316716 0.7064579256360078 0.7096774193548387 0.7103718199608611 0.6911045943304008 0.7221135029354208 0.678396871945259 0.7299412915851272 0.6578690127077224 0.7377690802348337 0.6559139784946236 0.7436399217221135 0.6480938416422287 0.7475538160469667 0.6275659824046921 0.7534246575342466 0.624633431085044 0.7553816046966731 0.6168132942326491 0.7514677103718199 0.5845552297165201 0.7573385518590998 0.5659824046920822 0.7553816046966731 0.5239491691104594 0.7455968688845401 0.5063538611925709 0.7475538160469667 0.4633431085043988 0.761252446183953 0.4398826979472141 0.7710371819960861 0.40078201368523947 0.8180039138943248 0.33822091886608013 0.8277886497064579 0.3333333333333333 0.8317025440313112 0.3225806451612903 0.8395303326810176 0.31867057673509286 0.8414872798434442 0.3088954056695992 0.8493150684931506 0.3020527859237537 0.8493150684931506 0.2913000977517107 0.8551859099804305 0.2873900293255132 0.8512720156555773 0.27956989247311825 0.8551859099804305 0.270772238514174 0.8532289628180039 0.23949169110459434 0.8473581213307241 0.2316715542521994 0.8395303326810176 0.23069403714565004 0.8297455968688845 0.21994134897360704
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in_json_noext	Mon Jul 07 06:47:08 2025 +0000
@@ -0,0 +1,332 @@
+{
+  "version": "0.4.10",
+  "flags": {},
+  "shapes": [
+    {
+      "label": "animal",
+      "text": "",
+      "points": [
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+          199.0
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+          296.0,
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+          273.0,
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+          218.0,
+          288.0
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+        [
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+      ],
+      "group_id": null,
+      "shape_type": "polygon",
+      "flags": {}
+    }
+  ],
+  "imagePath": "C2-MAX_20230629_DE_W0003_P0001-0001.tif",
+  "imageData": null,
+  "imageHeight": 1024,
+  "imageWidth": 512,
+  "text": ""
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in_json_noext.txt	Mon Jul 07 06:47:08 2025 +0000
@@ -0,0 +1,1 @@
+0 0.7710371819960861 0.20039100684261973 0.7592954990215264 0.2013685239491691 0.735812133072407 0.19648093841642228 0.6927592954990215 0.19745845552297164 0.6810176125244618 0.1935483870967742 0.649706457925636 0.1906158357771261 0.6203522504892368 0.19452590420332355 0.5792563600782779 0.20918866080156404 0.5342465753424658 0.2130987292277615 0.47162426614481406 0.2434017595307918 0.42661448140900193 0.28152492668621704 0.4070450097847358 0.30791788856304986 0.37377690802348335 0.386119257086999 0.37377690802348335 0.43206256109481916 0.3542074363992172 0.4701857282502444 0.3522504892367906 0.49853372434017595 0.3424657534246575 0.5259042033235581 0.3405088062622309 0.5669599217986315 0.33268101761252444 0.5845552297165201 0.33463796477495106 0.5972629521016618 0.3268101761252446 0.6041055718475073 0.31898238747553814 0.6598240469208211 0.30919765166340507 0.6666666666666666 0.30919765166340507 0.6754643206256109 0.2876712328767123 0.6959921798631477 0.27984344422700586 0.7223851417399805 0.29158512720156554 0.729227761485826 0.29354207436399216 0.7350928641251222 0.3131115459882583 0.7438905180840665 0.32289628180039137 0.7556207233626588 0.33659491193737767 0.761485826001955 0.35812133072407043 0.7781036168132942 0.3913894324853229 0.7927663734115347 0.40313111545988256 0.7956989247311828 0.4090019569471624 0.793743890518084 0.4207436399217221 0.7986314760508308 0.43052837573385516 0.7976539589442815 0.44422700587084146 0.8005865102639296 0.45596868884540115 0.7986314760508308 0.5283757338551859 0.7986314760508308 0.5342465753424658 0.7947214076246334 0.5636007827788649 0.7927663734115347 0.6164383561643836 0.7820136852394917 0.6223091976516634 0.7781036168132942 0.6281800391389433 0.7790811339198436 0.6516634050880626 0.7595307917888563 0.6614481409001957 0.7556207233626588 0.675146771037182 0.7360703812316716 0.7064579256360078 0.7096774193548387 0.7103718199608611 0.6911045943304008 0.7221135029354208 0.678396871945259 0.7299412915851272 0.6578690127077224 0.7377690802348337 0.6559139784946236 0.7436399217221135 0.6480938416422287 0.7475538160469667 0.6275659824046921 0.7534246575342466 0.624633431085044 0.7553816046966731 0.6168132942326491 0.7514677103718199 0.5845552297165201 0.7573385518590998 0.5659824046920822 0.7553816046966731 0.5239491691104594 0.7455968688845401 0.5063538611925709 0.7475538160469667 0.4633431085043988 0.761252446183953 0.4398826979472141 0.7710371819960861 0.40078201368523947 0.8180039138943248 0.33822091886608013 0.8277886497064579 0.3333333333333333 0.8317025440313112 0.3225806451612903 0.8395303326810176 0.31867057673509286 0.8414872798434442 0.3088954056695992 0.8493150684931506 0.3020527859237537 0.8493150684931506 0.2913000977517107 0.8551859099804305 0.2873900293255132 0.8512720156555773 0.27956989247311825 0.8551859099804305 0.270772238514174 0.8532289628180039 0.23949169110459434 0.8473581213307241 0.2316715542521994 0.8395303326810176 0.23069403714565004 0.8297455968688845 0.21994134897360704
Binary file test-data/results_plot1.png has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/yolo-test-classes.txt	Mon Jul 07 06:47:08 2025 +0000
@@ -0,0 +1,7 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
--- a/yolov8.py	Fri Jun 13 11:23:35 2025 +0000
+++ b/yolov8.py	Mon Jul 07 06:47:08 2025 +0000
@@ -412,6 +412,25 @@
         if (args.mode == "detect"):
             # Save bounding boxes
             save_yolo_bounding_boxes_to_txt(predictions, args.save_dir)
+
+            # Loop over each result
+            for result in predictions:
+                img = np.copy(result.orig_img)
+                image_filename = pathlib.Path(result.path).stem
+                overlay_path = os.path.join(args.save_dir, f"{image_filename}_overlay.jpg")
+
+                for box, cls, conf in zip(result.boxes.xyxy, result.boxes.cls, result.boxes.conf):
+                    x1, y1, x2, y2 = map(int, box.tolist())
+                    class_num = int(cls.item())
+                    confidence = conf.item()
+                    label = f"{class_names[class_num]} {confidence:.2f}" if class_names else f"{class_num} {confidence:.2f}"
+
+                    cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 255, 0), thickness=2)
+                    cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX,
+                                0.5, (0, 255, 0), thickness=1)
+
+                cv2.imwrite(overlay_path, img)
+                print(colored(f"Overlay image saved at: {overlay_path}", 'cyan'))
         elif (args.mode == "track"):
             results = model.track(source=datapath_for_prediction,
                                   tracker=args.tracker_file,
@@ -451,44 +470,42 @@
             # Read class names from the file
             with open(args.class_names_file, 'r') as f:
                 class_names = [line.strip() for line in f.readlines()]
-            # Create a mapping from class names to indices
             class_to_index = {class_name: i for i, class_name in enumerate(class_names)}
 
             # Save polygon coordinates
             for result in predictions:
-                # Create binary mask
                 img = np.copy(result.orig_img)
                 filename = pathlib.Path(result.path).stem
                 b_mask = np.zeros(img.shape[:2], np.uint8)
                 mask_save_as = str(pathlib.Path(os.path.join(args.save_dir, filename + "_mask.tiff")).absolute())
-                # Define output file path for text file
-                output_filename = os.path.splitext(filename)[0] + ".txt"
                 txt_save_as = str(pathlib.Path(os.path.join(args.save_dir, filename + ".txt")).absolute())
 
                 for c, ci in enumerate(result):
-                    #  Extract contour result
-                    contour = ci.masks.xy.pop()
-                    #  Changing the type
-                    contour = contour.astype(np.int32)
-                    #  Reshaping
-                    contour = contour.reshape(-1, 1, 2)
-                    # Draw contour onto mask
-                    _ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)
+                    if ci.masks is not None and ci.masks.xy:
+                        #  Extract contour
+                        contour = ci.masks.xy.pop()
+                        contour = contour.astype(np.int32).reshape(-1, 1, 2)
+                        _ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)
+
+                        # Normalized polygon points
+                        points = ci.masks.xyn.pop()
+                        obj_class = int(ci.boxes.cls.to("cpu").numpy().item())
+                        confidence = result.boxes.conf.to("cpu").numpy()[c]
 
-                    # Normalized polygon points
-                    points = ci.masks.xyn.pop()
-                    obj_class = int(ci.boxes.cls.to("cpu").numpy().item())
-                    confidence = result.boxes.conf.to("cpu").numpy()[c]
+                        with open(txt_save_as, 'a') as f:
+                            segmentation_points = ['{} {}'.format(points[i][0], points[i][1]) for i in range(len(points))]
+                            segmentation_points_string = ' '.join(segmentation_points)
+                            line = '{} {} {}\n'.format(obj_class, segmentation_points_string, confidence)
+                            f.write(line)
+                    else:
+                        print(colored(f"⚠️ No mask found for object {c} in '{filename}'. Skipping.", "yellow"))
 
-                    with open(txt_save_as, 'a') as f:
-                        segmentation_points = ['{} {}'.format(points[i][0], points[i][1]) for i in range(len(points))]
-                        segmentation_points_string = ' '.join(segmentation_points)
-                        line = '{} {} {}\n'.format(obj_class, segmentation_points_string, confidence)
-                        f.write(line)
+                # Overlay mask onto original image
+                colored_mask = cv2.merge([b_mask, np.zeros_like(b_mask), np.zeros_like(b_mask)])
+                blended = cv2.addWeighted(img, 1.0, colored_mask, 0.5, 0)
+                overlay_path = os.path.join(args.save_dir, filename + "_overlay.jpg")
+                cv2.imwrite(overlay_path, blended)
 
-                imwrite(mask_save_as, b_mask, imagej=True)  # save image
-                print(colored(f"Saved cropped image as : \n '{mask_save_as}' \n", 'magenta'))
+                imwrite(mask_save_as, b_mask, imagej=True)
+                print(colored(f"Saved binary mask as : \n '{mask_save_as}' \n", 'magenta'))
                 print(colored(f"Polygon coordinates saved as : \n '{txt_save_as}' \n", 'cyan'))
-
-        else:
-            raise Exception(("Currently only 'detect' and 'segment' modes are available"))