From f141efd60ccfec88b9ac6c48ca3e810d4732db88 Mon Sep 17 00:00:00 2001 From: Mariusz Gumowski Date: Thu, 16 Oct 2025 12:26:41 +0200 Subject: [PATCH 1/4] save --- .python-version | 2 +- pyproject.toml | 2 + .../visualizer/scene/classification.py | 10 +- src/model_api/visualizer/scene/detection.py | 7 +- .../segmentation/instance_segmentation.py | 8 +- src/model_api/visualizer/utils.py | 48 + uv.lock | 2396 ++++++++++++----- webui.py | 392 +++ 8 files changed, 2112 insertions(+), 753 deletions(-) create mode 100644 src/model_api/visualizer/utils.py create mode 100644 webui.py diff --git a/.python-version b/.python-version index c8cfe395..24ee5b1b 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.10 +3.13 diff --git a/pyproject.toml b/pyproject.toml index eb2fc04f..b26343c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,8 @@ dependencies = [ "opencv-python-headless", "openvino>=2025.3", "pillow", + "gradio", + "fastrtc", ] [project.optional-dependencies] diff --git a/src/model_api/visualizer/scene/classification.py b/src/model_api/visualizer/scene/classification.py index 04d08774..a9a15fe4 100644 --- a/src/model_api/visualizer/scene/classification.py +++ b/src/model_api/visualizer/scene/classification.py @@ -11,6 +11,7 @@ from model_api.models.result import ClassificationResult from model_api.visualizer.layout import Flatten, Layout from model_api.visualizer.primitive import Label, Overlay +from model_api.visualizer.utils import generate_color_palette from .scene import Scene @@ -19,6 +20,12 @@ class ClassificationScene(Scene): """Classification Scene.""" def __init__(self, image: Image, result: ClassificationResult, layout: Union[Layout, None] = None) -> None: + # Generate colors for labels if there are multiple top labels + label_names = [] + if result.top_labels is not None and len(result.top_labels) > 0: + label_names = [label.name for label in result.top_labels if label.name is not None] + self.color_per_label = generate_color_palette(label_names, seed=0) + super().__init__( base=image, label=self._get_labels(result), @@ -31,7 +38,8 @@ def _get_labels(self, result: ClassificationResult) -> list[Label]: if result.top_labels is not None and len(result.top_labels) > 0: for label in result.top_labels: if label.name is not None: - labels.append(Label(label=label.name, score=label.confidence)) + bg_color = self.color_per_label.get(label.name, "blue") + labels.append(Label(label=label.name, score=label.confidence, bg_color=bg_color)) return labels def _get_overlays(self, result: ClassificationResult) -> list[Overlay]: diff --git a/src/model_api/visualizer/scene/detection.py b/src/model_api/visualizer/scene/detection.py index 7ffa3e97..58b451af 100644 --- a/src/model_api/visualizer/scene/detection.py +++ b/src/model_api/visualizer/scene/detection.py @@ -11,6 +11,7 @@ from model_api.models.result import DetectionResult from model_api.visualizer.layout import Flatten, HStack, Layout from model_api.visualizer.primitive import BoundingBox, Label, Overlay +from model_api.visualizer.utils import generate_color_palette from .scene import Scene @@ -19,6 +20,9 @@ class DetectionScene(Scene): """Detection Scene.""" def __init__(self, image: Image, result: DetectionResult, layout: Union[Layout, None] = None) -> None: + # Generate consistent colors for each unique label + self.color_per_label = generate_color_palette(result.label_names, seed=0) + super().__init__( base=image, bounding_box=self._get_bounding_boxes(result), @@ -43,7 +47,8 @@ def _get_bounding_boxes(self, result: DetectionResult) -> list[BoundingBox]: for score, label_name, bbox in zip(result.scores, result.label_names, result.bboxes): x1, y1, x2, y2 = bbox label = f"{label_name} ({score:.2f})" - bounding_boxes.append(BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2, label=label)) + color = self.color_per_label[label_name] + bounding_boxes.append(BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2, label=label, color=color)) return bounding_boxes @property diff --git a/src/model_api/visualizer/scene/segmentation/instance_segmentation.py b/src/model_api/visualizer/scene/segmentation/instance_segmentation.py index 8c89ca01..0bcf5a33 100644 --- a/src/model_api/visualizer/scene/segmentation/instance_segmentation.py +++ b/src/model_api/visualizer/scene/segmentation/instance_segmentation.py @@ -3,7 +3,6 @@ # Copyright (C) 2025 Intel Corporation # SPDX-License-Identifier: Apache-2.0 -import random from typing import Union import cv2 @@ -13,15 +12,16 @@ from model_api.visualizer.layout import Flatten, HStack, Layout from model_api.visualizer.primitive import BoundingBox, Label, Overlay, Polygon from model_api.visualizer.scene import Scene +from model_api.visualizer.utils import generate_color_palette class InstanceSegmentationScene(Scene): """Instance Segmentation Scene.""" def __init__(self, image: Image, result: InstanceSegmentationResult, layout: Union[Layout, None] = None) -> None: - # nosec as random is used for color generation - g = random.Random(0) # noqa: S311 # nosec B311 - self.color_per_label = {label: f"#{g.randint(0, 0xFFFFFF):06x}" for label in set(result.label_names)} # nosec B311 + # Generate consistent colors for each unique label + self.color_per_label = generate_color_palette(result.label_names, seed=0) + super().__init__( base=image, label=self._get_labels(result), diff --git a/src/model_api/visualizer/utils.py b/src/model_api/visualizer/utils.py new file mode 100644 index 00000000..1e1266f3 --- /dev/null +++ b/src/model_api/visualizer/utils.py @@ -0,0 +1,48 @@ +"""Utility functions for the visualizer.""" + +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import hashlib +import random +from typing import Dict, List + + +def generate_color_palette(labels: List[str], seed: int = 0) -> Dict[str, str]: + """Generate a consistent color palette for a list of labels. + + Uses label names as seeds to ensure the same label always gets the same color, + regardless of which other labels are present in the current detection. + + Args: + labels: List of label names + seed: Base seed for color generation (optional, for additional randomization) + + Returns: + Dictionary mapping label names to hex color strings + """ + color_map = {} + for label in labels: + # Create a deterministic seed based on the label name + label_hash = hashlib.md5(label.encode()).hexdigest() + label_seed = int(label_hash[:8], 16) + seed + + # Generate color using the label-specific seed + g = random.Random(label_seed) # noqa: S311 # nosec B311 + color_map[label] = f"#{g.randint(0, 0xFFFFFF):06x}" # nosec B311 + + return color_map + + +def get_distinct_colors(num_colors: int, seed: int = 0) -> List[str]: + """Generate a list of distinct colors. + + Args: + num_colors: Number of colors to generate + seed: Random seed for reproducible colors + + Returns: + List of hex color strings + """ + g = random.Random(seed) # noqa: S311 # nosec B311 + return [f"#{g.randint(0, 0xFFFFFF):06x}" for _ in range(num_colors)] # nosec B311 \ No newline at end of file diff --git a/uv.lock b/uv.lock index 71f9eaba..6d9a0500 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 2 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'darwin'", @@ -11,7 +11,9 @@ resolution-markers = [ "python_full_version == '3.11.*' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version < '3.11'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and 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self.available_models = self._discover_models() + self.visualizer = Visualizer() + self.load_model(list(self.available_models.keys())[0]) + + def _discover_models(self): + """Discover all available models in the data folder""" + models = {} + data_path = Path("data") + + # Find all model.xml files + model_files = list(data_path.glob("**/*.xml")) + + for model_file in model_files: + # Get the relative path from data folder + rel_path = model_file.relative_to(data_path) + parts = rel_path.parts + + # Skip if not enough folder depth (need at least 3 folders) + if len(parts) < 3: + continue + + # Create model name from the 3 upper folders (excluding model.xml) + # Example: AnomalyDetection/STFPM/openvino_fp32 + model_name = "/".join(parts[-4:-1]) + model_path = str(model_file) + + models[model_name] = model_path + + # Sort models by name for better UI experience + return dict(sorted(models.items())) + + + + def load_model(self, model_name): + """Load a model""" + try: + self.model_name = model_name + model_path = self.available_models[model_name] + print(f"Loading model: {model_path} to device: {self.device}") + self.model = Model.create_model(model_path, device=self.device) + except Exception as e: + print(f"Error loading model {model_name}: {str(e)}") + + + def set_device(self, device_name): + """Set the device for inference""" + print(f"Setting device to: {device_name}") + self.device = device_name + if self.model: + self.load_model(self.model_name) + + + def run_inference(self, image): + """Run inference on the input image""" + print("Running inference...") + try: + if isinstance(image, Image.Image): + image_array = np.array(image) + else: + image_array = image + predictions = self.model(image_array) + stats = self.model.get_performance_metrics() + result_image = self.visualizer.render(image=image_array, result=predictions) + + # Format statistics for display + stats_dict = self.format_statistics(stats) + + except Exception as e: + print(f"Error during inference: {str(e)}") + return None, self.format_statistics(PerformanceMetrics()) + return result_image, stats_dict + + def format_statistics(self, stats): + """Format statistics into a dictionary for display""" + return { + 'fps': f"{stats.get_fps():.2f}", + 'total_frames': f"{stats.get_total_frames():,}", + 'preprocess_mean': f"{stats.get_preprocess_time().mean() * 1000:.2f}", + 'inference_mean': f"{stats.get_inference_time().mean() * 1000:.2f}", + 'postprocess_mean': f"{stats.get_postprocess_time().mean() * 1000:.2f}", + 'total_mean': f"{stats.total_time.mean() * 1000:.2f}", + 'total_min': f"{stats.get_total_time_min() * 1000:.2f}", + 'total_max': f"{stats.get_total_time_max() * 1000:.2f}", + } + + def load_images_from_folder(self, folder_path): + """Load images from a folder and return list of image paths""" + try: + if not folder_path or not os.path.exists(folder_path): + return [] + + # Common image extensions + image_extensions = ['*.jpg', '*.jpeg', '*.png', '*.bmp', '*.gif', '*.tiff', '*.webp'] + image_files = [] + + for extension in image_extensions: + image_files.extend(glob.glob(os.path.join(folder_path, extension))) + image_files.extend(glob.glob(os.path.join(folder_path, extension.upper()))) + + # Sort the files for consistent ordering + image_files.sort() + print(f"Found {len(image_files)} images in {folder_path}") + return image_files + + except Exception as e: + print(f"Error loading images from folder {folder_path}: {str(e)}") + return [] + + def stream_vid(self, video): + cap = cv2.VideoCapture(video) + + # This means we will output mp4 videos + video_codec = cv2.VideoWriter_fourcc(*"mp4v") + fps = int(cap.get(cv2.CAP_PROP_FPS)) + + desired_fps = fps + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + iterating, frame = cap.read() + + n_frames = 0 + + # Use UUID to create a unique video file + output_video_name = f"/tmp/output_{uuid.uuid4()}.mp4" + + # Output Video + output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore + + while iterating: +# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5) + try: + predictions = self.model(frame) + result_image = self.visualizer.render(image=frame, result=predictions) + output_video.write(result_image) + except Exception as e: + print(f"Error during inference: {str(e)}") + return None + + if n_frames % (desired_fps*2) == 0: + output_video.release() + # Get current performance metrics + stats = self.model.get_performance_metrics() + stats_dict = self.format_statistics(stats) + + # Yield video path and stats as tuple + yield ( + output_video_name, + stats_dict['fps'], + stats_dict['total_frames'], + stats_dict['preprocess_mean'], + stats_dict['inference_mean'], + stats_dict['postprocess_mean'], + stats_dict['total_mean'], + stats_dict['total_min'], + stats_dict['total_max'] + ) + output_video_name = f"/tmp/output_{uuid.uuid4()}.mp4" + output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore + + iterating, frame = cap.read() + n_frames += 1 + + def load_and_process_image(self, image_path): + if image_path: + image = Image.open(image_path) + result_img, stats = self.run_inference(image) + return ( + image, + result_img, + stats['fps'], + stats['total_frames'], + stats['preprocess_mean'], + stats['inference_mean'], + stats['postprocess_mean'], + stats['total_mean'], + stats['total_min'], + stats['total_max'] + ) + return None, None, "0.00", "0", "0.00", "0.00", "0.00", "0.00", "0.00", "0.00" + + def create_interface(self): + with gr.Blocks(title="Geti Predict", theme=gr.themes.Base()) as demo: + gr.Markdown("# Geti Predict") + with gr.Row(): + with gr.Column(scale=5): + tabs = gr.Tabs() + with tabs: + with gr.TabItem("Image"): + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("### Input Source") + sample_image_files = glob.glob("data/images/*") + sample_image_dropdown = gr.Dropdown( + choices=sample_image_files, + label="Sample Images", + value=sample_image_files[0] if sample_image_files else None + ) + sample_image_display = gr.Image(sources=["upload", "clipboard"], label="Image", type="pil", show_label=False) + + with gr.Column(scale=3): + gr.Markdown("### Inferences") + result = gr.Image(label="Result", type="pil", show_label=False) + + + with gr.TabItem("Dataset"): + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("## Input Dataset") + image_gallery = gr.Gallery( + label="Gallery", + elem_id="gallery", + object_fit="contain", + height="auto", + allow_preview=True, + show_label=False, + show_fullscreen_button=True, + columns=4 + ) + + with gr.Column(scale=3): + gr.Markdown("## Inferences") + ds_result = gr.Image(label="DS Result", type="pil", show_label=False) + + + with gr.TabItem("Video"): + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("## Input Source") + video_input = gr.Video(label="Video", show_label=False) + with gr.Column(scale=3): + gr.Markdown("## Inference Stream") + result_stream = gr.Video( + label="Processed Video", + streaming=True, + autoplay=True, + show_label=False, + show_download_button=True) + + with gr.TabItem("Camera"): + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown("## Input Source") + cam_input = gr.Image(sources=["webcam"], label="Webcam", type="pil") + with gr.Column(scale=1): + gr.Markdown("## Camera Stream") + with gr.TabItem("Model"): + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown("## Load/Reload Model") + model_dropdown = gr.Dropdown( + choices=self.available_models.keys(), + label="Select Model", + value=self.available_models.keys().__iter__().__next__(), + interactive=True + ) + device_dropdown = gr.Dropdown( + choices=self.available_devices, + label="Select Device Source", + value=self.available_devices[0], + ) + device_info = gr.Markdown(f"**Device:** {self.device}") + + with gr.Column(scale=1): + gr.Markdown("### Model Load Status") + model_status = gr.Markdown("") + + model_dropdown.input( + fn=self.load_model, + inputs=model_dropdown, + outputs=[] + ) + + device_dropdown.input( + fn=self.set_device, + inputs=device_dropdown, + outputs=[] + ) + with gr.Column(scale=1): + # Performance Metrics Dashboard + gr.Markdown("### Performance Metrics") + with gr.Row(): + fps_display = gr.Textbox(label="🚀 FPS", value="0.00", interactive=False) + total_frames_display = gr.Textbox(label="🎞️ Total Frames", value="0", interactive=False) + + with gr.Accordion("📊 Detailed Timing (ms)", open=True): + with gr.Row(): + preprocess_display = gr.Textbox(label="⚙️ Preprocess", value="0.00", interactive=False) + inference_display = gr.Textbox(label="🧠 Inference", value="0.00", interactive=False) + postprocess_display = gr.Textbox(label="🎨 Postprocess", value="0.00", interactive=False) + + with gr.Row(): + total_mean_display = gr.Textbox(label="📈 Total (Mean)", value="0.00", interactive=False) + total_min_display = gr.Textbox(label="⚡ Total (Min)", value="0.00", interactive=False) + total_max_display = gr.Textbox(label="🐌 Total (Max)", value="0.00", interactive=False) + + + def load_and_process_image_gallery(evt: gr.SelectData): + results = self.load_and_process_image(evt.value['image']['path'] if isinstance(evt.value, dict) else evt.value) + return results[1:] + + + sample_image_dropdown.input( + fn=self.load_and_process_image, + inputs=sample_image_dropdown, + outputs=[ + sample_image_display, + result, + fps_display, + total_frames_display, + preprocess_display, + inference_display, + postprocess_display, + total_mean_display, + total_min_display, + total_max_display + ] + ) + + image_gallery.select( + fn=load_and_process_image_gallery, + inputs=None, + outputs=[ + ds_result, + fps_display, + total_frames_display, + preprocess_display, + inference_display, + postprocess_display, + total_mean_display, + total_min_display, + total_max_display + ] + ) + + video_input.play( + fn=self.stream_vid, + inputs=[video_input], + outputs=[ + result_stream, + fps_display, + total_frames_display, + preprocess_display, + inference_display, + postprocess_display, + total_mean_display, + total_min_display, + total_max_display + ] + ) + + return demo + + +# Create the demo instance at module level for gradio CLI hotreload +app = ModelAPIGradioApp() +demo = app.create_interface() + +if __name__ == "__main__": + demo.launch( + server_name="0.0.0.0", + server_port=7860, + debug=True + ) \ No newline at end of file From e541fba511d1e98b052cf71167368f8510a75921 Mon Sep 17 00:00:00 2001 From: Mariusz Gumowski Date: Thu, 16 Oct 2025 12:57:35 +0200 Subject: [PATCH 2/4] camfix --- webui.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 62 insertions(+), 4 deletions(-) diff --git a/webui.py b/webui.py index bfa4b83c..6f52ac82 100644 --- a/webui.py +++ b/webui.py @@ -189,6 +189,40 @@ def stream_vid(self, video): iterating, frame = cap.read() n_frames += 1 + def start_camera_stream(self, frame): + """Process camera stream - runs inference on captured camera frame""" + try: + if frame is None: + return None, "0.00", "0", "0.00", "0.00", "0.00", "0.00", "0.00", "0.00" + + #if isinstance(image, Image.Image): + # image_array = np.array(image) + #else: + # image_array = image + + # Run inference + predictions = self.model(frame) + result_image = self.visualizer.render(image=frame, result=predictions) + + # Get current performance metrics + stats = self.model.get_performance_metrics() + stats_dict = self.format_statistics(stats) + + return ( + result_image, + stats_dict['fps'], + stats_dict['total_frames'], + stats_dict['preprocess_mean'], + stats_dict['inference_mean'], + stats_dict['postprocess_mean'], + stats_dict['total_mean'], + stats_dict['total_min'], + stats_dict['total_max'] + ) + except Exception as e: + print(f"Error during camera inference: {str(e)}") + return None, "0.00", "0", "0.00", "0.00", "0.00", "0.00", "0.00", "0.00" + def load_and_process_image(self, image_path): if image_path: image = Image.open(image_path) @@ -267,11 +301,17 @@ def create_interface(self): with gr.TabItem("Camera"): with gr.Row(): - with gr.Column(scale=1): + with gr.Column(scale=2): gr.Markdown("## Input Source") - cam_input = gr.Image(sources=["webcam"], label="Webcam", type="pil") - with gr.Column(scale=1): - gr.Markdown("## Camera Stream") + cam_input = gr.Image( + sources=["webcam"], + label="Webcam", + type="numpy", + show_label=False + ) + with gr.Column(scale=3): + gr.Markdown("## Camera Inference Stream") + cam_result = gr.Image(label="Camera Result", type="pil", show_label=False) with gr.TabItem("Model"): with gr.Row(): with gr.Column(scale=1): @@ -377,6 +417,24 @@ def load_and_process_image_gallery(evt: gr.SelectData): ] ) + live_stream = cam_input.stream( + fn=self.start_camera_stream, + inputs=[cam_input], + outputs=[ + cam_result, + fps_display, + total_frames_display, + preprocess_display, + inference_display, + postprocess_display, + total_mean_display, + total_min_display, + total_max_display + ], + time_limit=60, + stream_every=0.1, + concurrency_limit=2) + return demo From d47f1aafe0686971737ab4375eab83387f5a0186 Mon Sep 17 00:00:00 2001 From: Mariusz Gumowski Date: Thu, 16 Oct 2025 14:11:23 +0200 Subject: [PATCH 3/4] save --- webui.py | 48 +++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 39 insertions(+), 9 deletions(-) diff --git a/webui.py b/webui.py index 6f52ac82..336cf6e9 100644 --- a/webui.py +++ b/webui.py @@ -27,6 +27,7 @@ def __init__(self): self.stats = PerformanceMetrics() self.available_models = self._discover_models() self.visualizer = Visualizer() + self.streaming = False self.load_model(list(self.available_models.keys())[0]) def _discover_models(self): @@ -49,9 +50,18 @@ def _discover_models(self): # Create model name from the 3 upper folders (excluding model.xml) # Example: AnomalyDetection/STFPM/openvino_fp32 model_name = "/".join(parts[-4:-1]) + + # Find corresponding .bin file and get its size + bin_file = model_file.with_suffix('.bin') + size_str = "" + if bin_file.exists(): + size_mb = bin_file.stat().st_size / (1024 * 1024) + size_str = f" [{size_mb:.1f}MB]" + + model_name_with_size = f"{model_name}{size_str}" model_path = str(model_file) - models[model_name] = model_path + models[model_name_with_size] = model_path # Sort models by name for better UI experience return dict(sorted(models.items())) @@ -134,6 +144,7 @@ def load_images_from_folder(self, folder_path): return [] def stream_vid(self, video): + self.streaming = True cap = cv2.VideoCapture(video) # This means we will output mp4 videos @@ -141,8 +152,6 @@ def stream_vid(self, video): fps = int(cap.get(cv2.CAP_PROP_FPS)) desired_fps = fps - width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) iterating, frame = cap.read() @@ -151,10 +160,17 @@ def stream_vid(self, video): # Use UUID to create a unique video file output_video_name = f"/tmp/output_{uuid.uuid4()}.mp4" - # Output Video + # Output Video - get dimensions from first result image + predictions = self.model(frame) + result_image = self.visualizer.render(image=frame, result=predictions) + height, width = result_image.shape[:2] output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore - while iterating: + while iterating and self.streaming: + if frame is None: + break + + # Convert BGR to RGB # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5) try: @@ -189,6 +205,9 @@ def stream_vid(self, video): iterating, frame = cap.read() n_frames += 1 + def stop_vid(self): + self.streaming = False + def start_camera_stream(self, frame): """Process camera stream - runs inference on captured camera frame""" try: @@ -290,6 +309,10 @@ def create_interface(self): with gr.Column(scale=2): gr.Markdown("## Input Source") video_input = gr.Video(label="Video", show_label=False) + with gr.Row(): + video_start_button = gr.Button("Start", variant="primary") + video_stop_button = gr.Button("Stop", variant="stop") + with gr.Column(scale=3): gr.Markdown("## Inference Stream") result_stream = gr.Video( @@ -314,7 +337,7 @@ def create_interface(self): cam_result = gr.Image(label="Camera Result", type="pil", show_label=False) with gr.TabItem("Model"): with gr.Row(): - with gr.Column(scale=1): + with gr.Column(scale=3): gr.Markdown("## Load/Reload Model") model_dropdown = gr.Dropdown( choices=self.available_models.keys(), @@ -401,7 +424,7 @@ def load_and_process_image_gallery(evt: gr.SelectData): ] ) - video_input.play( + video_start_button.click( fn=self.stream_vid, inputs=[video_input], outputs=[ @@ -417,6 +440,12 @@ def load_and_process_image_gallery(evt: gr.SelectData): ] ) + video_stop_button.click( + fn=self.stop_vid, + inputs=None, + outputs=None + ) + live_stream = cam_input.stream( fn=self.start_camera_stream, inputs=[cam_input], @@ -432,8 +461,9 @@ def load_and_process_image_gallery(evt: gr.SelectData): total_max_display ], time_limit=60, - stream_every=0.1, - concurrency_limit=2) + stream_every=0.2, + concurrency_limit=16 + ) return demo From 193ccc850d650faabdf4d4c31f03f6e9533be8f1 Mon Sep 17 00:00:00 2001 From: Mariusz Gumowski Date: Mon, 20 Oct 2025 10:21:47 +0200 Subject: [PATCH 4/4] save --- .../scene/segmentation/segmentation.py | 20 ++++++++++++++----- webui.py | 2 +- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/src/model_api/visualizer/scene/segmentation/segmentation.py b/src/model_api/visualizer/scene/segmentation/segmentation.py index 12bbaa49..cdf4e2bd 100644 --- a/src/model_api/visualizer/scene/segmentation/segmentation.py +++ b/src/model_api/visualizer/scene/segmentation/segmentation.py @@ -30,11 +30,21 @@ def _get_overlays(self, result: ImageResultWithSoftPrediction) -> list[Overlay]: # Use the hard prediction to get the overlays hard_prediction = result.resultImage # shape H,W num_classes = hard_prediction.max() - for i in range(1, num_classes + 1): # ignore background - class_map = (hard_prediction == i).astype(np.uint8) * 255 - class_map = cv2.applyColorMap(class_map, cv2.COLORMAP_JET) - class_map = cv2.cvtColor(class_map, cv2.COLOR_BGR2RGB) - overlays.append(Overlay(class_map, label=f"Class {i}")) + + # Create a single colored segmentation map with all classes + h, w = hard_prediction.shape + colored_segmentation = np.zeros((h, w, 3), dtype=np.uint8) + + # Generate distinct colors for each class using HSV color space + for i in range(1, num_classes + 1): # ignore background (class 0) + class_mask = (hard_prediction == i) + # Generate a distinct color for each class using HSV + hue = int(180 * i / num_classes) # Distribute hues across the spectrum + color_hsv = np.uint8([[[hue, 255, 255]]]) + color_rgb = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB)[0, 0] + colored_segmentation[class_mask] = color_rgb + + overlays.append(Overlay(colored_segmentation, label="Segmentation")) # Add saliency map if result.saliency_map is not None and result.saliency_map.size > 0: diff --git a/webui.py b/webui.py index 336cf6e9..61cb8156 100644 --- a/webui.py +++ b/webui.py @@ -44,7 +44,7 @@ def _discover_models(self): parts = rel_path.parts # Skip if not enough folder depth (need at least 3 folders) - if len(parts) < 3: + if len(parts) < 2: continue # Create model name from the 3 upper folders (excluding model.xml)