Merge pull request #134 from pipeless-ai/yolo-world

Support several inference outputs for ONNX Runtime + YOLO World example

Author: miguelaeh

examples/onnx-candy/post-process.py

@@ -2,7 +2,7 @@
 import cv2
 
 def hook(frame_data, _):
-    inference_results = frame_data["inference_output"]
+    inference_results = frame_data["inference_output"].get("output1", [])
     candy_image = inference_results[0] # Remove batch axis
     candy_image = np.clip(candy_image, 0, 255)
     candy_image = candy_image.transpose(1,2,0).astype("uint8")

examples/onnx-yolo/post-process.py

@@ -6,7 +6,7 @@ def hook(frame_data, _):
     model_output = frame_data['inference_output']
     if len(model_output) > 0:
         yolo_input_shape = (640, 640, 3) # h,w,c
-        boxes, scores, class_ids = postprocess_yolo(frame.shape, yolo_input_shape, model_output)
+        boxes, scores, class_ids = postprocess_yolo(frame.shape, yolo_input_shape, model_output.get("output0", []))
         class_labels = [yolo_classes[id] for id in class_ids]
         for i in range(len(boxes)):
             draw_bbox(frame, boxes[i], class_labels[i], scores[i], color_palette[class_ids[i]])

examples/yolo-world/post-process.py

@@ -0,0 +1,100 @@
+import cv2
+import numpy as np
+
+def hook(frame_data, _):
+    frame = frame_data['original']
+    model_output = frame_data['inference_output']
+    if len(model_output) > 0:
+        yolo_input_shape = (640, 640, 3) # h,w,c
+        boxes, scores, class_ids = postprocess_yolo_world(frame.shape, yolo_input_shape, model_output)
+        class_labels = [yolo_classes[int(id)] for id in class_ids]
+        for i in range(len(boxes)):
+            draw_bbox(frame, boxes[i], class_labels[i], scores[i], color_palette[int(class_ids[i])])
+
+        frame_data['modified'] = frame
+
+#################################################
+# Util functions to make the hook more readable #
+#################################################
+yolo_classes = ['hard hat', 'gloves', 'protective boot', 'reflective vest', 'person']
+color_palette = np.random.uniform(0, 255, size=(len(yolo_classes), 3))
+
+def draw_bbox(image, box, label='', score=None, color=(255, 0, 255), txt_color=(255, 255, 255)):
+    lw = max(round(sum(image.shape) / 2 * 0.003), 2)
+    p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+    cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
+    if label:
+        tf = max(lw - 1, 1)  # font thickness
+        w, h = cv2.getTextSize(str(label), 0, fontScale=lw / 3, thickness=tf)[0]  # text width, height
+        outside = p1[1] - h >= 3
+        p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+        cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA)  # filled
+        if score is not None:
+            cv2.putText(image, f'{label} - {score}', (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+                0, lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
+        else:
+            cv2.putText(image, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+                0, lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
+
+def postprocess_yolo_world(original_frame_shape, resized_img_shape, output):
+    original_height, original_width, _ = original_frame_shape
+    resized_height, resized_width, _ = resized_img_shape
+
+    boxes = np.array(output['boxes'][0])
+    classes = np.array(output['labels'][0])
+    scores = np.array(output['scores'][0])
+
+    # Filter negative indexes
+    neg_indexes_classes = np.where(classes < 0)[0]
+    neg_indexes_scores = np.where(scores < 0)[0]
+    neg_indexes = np.concatenate((neg_indexes_classes, neg_indexes_scores))
+
+    mask = np.ones(classes.shape, dtype=bool)
+    mask[neg_indexes] = False
+
+    boxes = boxes[mask]
+    classes = classes[mask]
+    scores = scores[mask]
+
+    # arrays to accumulate the results
+    result_boxes = []
+    result_classes = []
+    result_scores = []
+
+    # Calculate the scaling factors for the bounding box coordinates
+    if original_height > original_width:
+        scale_factor = original_height / resized_height
+    else:
+        scale_factor = original_width / resized_width
+
+    # Resize the output boxes
+    for i, score in enumerate(scores):
+        if score < 0.05: # apply confidence threshold
+            continue
+        if not score < 1:
+            continue # Remove bad predictions that return a score of 1.0
+
+        x1, y1, x2, y2 = boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]
+
+        ## Calculate the scaled coordinates of the bounding box
+        ## the original image was padded to be square
+        if original_height > original_width:
+            # we added pad on the width
+            pad = (resized_width - original_width / scale_factor) // 2
+            x1 = int((x1 - pad) * scale_factor)
+            y1 = int(y1 * scale_factor)
+            x2 = int((x2 - pad) * scale_factor)
+            y2 = int(y2 * scale_factor)
+        else:
+            # we added pad on the height
+            pad = (resized_height - original_height / scale_factor) // 2
+            x1 = int(x1 * scale_factor)
+            y1 = int((y1 - pad) * scale_factor)
+            x2 = int(x2 * scale_factor)
+            y2 = int((y2 - pad) * scale_factor)
+
+        result_classes.append(classes[i])
+        result_scores.append(score)
+        result_boxes.append([x1, y1, x2, y2])
+
+    return result_boxes, result_scores, result_classes

examples/yolo-world/pre-process.py

@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+
+def is_cuda_available():
+    return cv2.cuda.getCudaEnabledDeviceCount() > 0
+
+"""
+Resize and pad image. Uses CUDA when available
+"""
+def resize_and_pad(frame, target_dim, pad_top, pad_bottom, pad_left, pad_right):
+    target_height, target_width = target_dim
+    if is_cuda_available():
+        # FIXME: due to the memory allocation here could be even slower than running on CPU. We must provide the frame from GPU memory to the hook
+        frame_gpu = cv2.cuda_GpuMat(frame)
+        resized_frame_gpu = cv2.cuda.resize(frame_gpu, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
+        padded_frame_gpu = cv2.cuda.copyMakeBorder(resized_frame_gpu, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=(0, 0, 0))
+        result = padded_frame_gpu.download()
+        return result
+    else:
+        resized_frame = cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
+        padded_frame = cv2.copyMakeBorder(resized_frame, pad_top, pad_bottom, pad_left, pad_right,
+                        borderType=cv2.BORDER_CONSTANT, value=(0, 0, 0))
+        return padded_frame
+
+def resize_with_padding(frame, target_dim):
+    target_height, target_width, _ = target_dim
+    frame_height, frame_width, _ = frame.shape
+
+    width_ratio = target_width / frame_width
+    height_ratio = target_height / frame_height
+    # Choose the minimum scaling factor to maintain aspect ratio
+    scale_factor = min(width_ratio, height_ratio)
+    # Calculate new dimensions after resizing
+    new_width = int(frame_width * scale_factor)
+    new_height = int(frame_height * scale_factor)
+    # Calculate padding dimensions
+    pad_width = (target_width - new_width) // 2
+    pad_height = (target_height - new_height) // 2
+
+    padded_image = resize_and_pad(frame, (new_height, new_width), pad_height, pad_height, pad_width, pad_width)
+    return padded_image
+
+def hook(frame_data, _):
+    frame = frame_data["original"].view()
+    yolo_input_shape = (640, 640, 3) # h,w,c
+    frame = resize_with_padding(frame, yolo_input_shape)
+    frame = np.array(frame) / 255.0 # Normalize pixel values
+    frame = np.transpose(frame, axes=(2,0,1)) # Convert to c,h,w
+    inference_inputs = frame.astype("float32")
+    frame_data['inference_input'] = inference_inputs

examples/yolo-world/process.json

@@ -0,0 +1,7 @@
+{
+    "runtime": "onnx",
+    "model_uri": "https://pipeless-public.s3.eu-west-3.amazonaws.com/yolow-l-ppe.onnx",
+    "inference_params": {
+        "execution_provider": "cpu"
+    }
+}

pipeless/Cargo.lock

@@ -1,6 +1,6 @@
 [package]
 name = "pipeless-ai"
-version = "1.10.0"
+version = "1.11.0"
 edition = "2021"
 authors = ["Miguel A. Cabrera Minagorri"]
 description = "An open-source computer vision framework to build and deploy applications in minutes"

pipeless/Cargo.toml

@@ -14,6 +14,11 @@ pub enum UserData {
     Dictionary(Vec<(String, UserData)>),
 }
 
+pub enum InferenceOutput {
+    Default(ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>),
+    OnnxInferenceOutput(crate::stages::inference::onnx::OnnxInferenceOutput)
+}
+
 pub struct RgbFrame {
     uuid: uuid::Uuid,
     original: ndarray::Array3<u8>,
@@ -26,7 +31,8 @@ pub struct RgbFrame {
     fps: u8,
     input_ts: f64, // epoch in seconds
     inference_input: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>,
-    inference_output: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>,
+    // We can convert the output into an arrayview since the user does not need to modify it and the inference runtimes returns a view, so we avoid a copy
+    inference_output: InferenceOutput,
     pipeline_id: uuid::Uuid,
     user_data: UserData,
     frame_number: u64,
@@ -47,7 +53,7 @@ impl RgbFrame {
             pts, dts, duration, fps,
             input_ts,
             inference_input: ndarray::ArrayBase::zeros(ndarray::IxDyn(&[0])),
-            inference_output: ndarray::ArrayBase::zeros(ndarray::IxDyn(&[0])),
+            inference_output: InferenceOutput::Default(ndarray::ArrayBase::zeros(ndarray::IxDyn(&[0]))),
             pipeline_id,
             user_data: UserData::Empty,
             frame_number,
@@ -62,7 +68,7 @@ impl RgbFrame {
         pts: u64, dts: u64, duration: u64,
         fps: u8, input_ts: f64,
         inference_input: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>,
-        inference_output: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>,
+        inference_output: InferenceOutput,
         pipeline_id: &str,
         user_data: UserData, frame_number: u64,
     ) -> Self {
@@ -122,13 +128,13 @@ impl RgbFrame {
     pub fn get_inference_input(&self) -> &ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>> {
         &self.inference_input
     }
-    pub fn get_inference_output(&self) -> &ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>> {
+    pub fn get_inference_output(&self) -> &InferenceOutput{
         &self.inference_output
     }
     pub fn set_inference_input(&mut self, input_data: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>) {
         self.inference_input = input_data;
     }
-    pub fn set_inference_output(&mut self, output_data: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>) {
+    pub fn set_inference_output(&mut self, output_data: InferenceOutput) {
         self.inference_output = output_data;
     }
     pub fn get_pipeline_id(&self) -> &uuid::Uuid {
@@ -180,7 +186,7 @@ impl Frame {
             Frame::RgbFrame(frame) => frame.get_inference_input()
         }
     }
-    pub fn get_inference_output(&self) -> &ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>> {
+    pub fn get_inference_output(&self) -> &InferenceOutput {
         match self {
             Frame::RgbFrame(frame) => frame.get_inference_output()
         }
@@ -190,7 +196,7 @@ impl Frame {
             Frame::RgbFrame(frame) => { frame.set_inference_input(input_data); },
         }
     }
-    pub fn set_inference_output(&mut self, output_data: ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>) {
+    pub fn set_inference_output(&mut self, output_data: InferenceOutput) {
         match self {
             Frame::RgbFrame(frame) => { frame.set_inference_output(output_data); },
         }

pipeless/src/data.rs

@@ -203,6 +203,8 @@ pub fn publish_new_frame_change_event_sync(
 ) {
     let new_frame_event = Event::new_frame_change(frame);
     // By using try_send frames are discarded when the channel is full
+    // However, note this does not produce a fluid output video. For that instead of discarding the frame
+    // we would need to send it to the output without processing it
     if let Err(err) = bus_sender.try_send(new_frame_event) {
         debug!("Discarding frame: {}", err);
     }

pipeless/src/events.rs

@@ -1,8 +1,12 @@
+use std::collections::HashMap;
+
 use log::{error, warn};
 use ort;
 
 use crate as pipeless;
 
+pub type OnnxInferenceOutput = HashMap<String, ndarray::ArrayBase<ndarray::OwnedRepr<f32>, ndarray::Dim<ndarray::IxDynImpl>>>;
+
 pub struct OnnxSessionParams {
     stage_name: String, // Name o the stage this session belongs to
     execution_provider: String, //The user has to provide the execution provider
@@ -163,10 +167,43 @@ impl super::session::SessionTrait for OnnxSession {
                 match self.session.run_with_binding(&io_bindings) {
                     Ok(()) => {
                         let outputs = io_bindings.outputs().unwrap();
-                        // TODO: iterate over the outputs hashmap to return all the model outputs not just the first
-                        let output = outputs[&self.session.outputs[0].name].try_extract().unwrap();
-                        let output_ndarray = output.view().to_owned();
-                        frame.set_inference_output(output_ndarray);
+                        let mut frame_inference_output = OnnxInferenceOutput::new();
+                        for (output_name, output_value) in outputs {
+                            // FIXME: the extract code is very unelegant. The extract can return several different numric types depending on the model used
+                            //        and there is not a number wrapper that we can apply, so we have to check type by type
+                            match output_value.try_extract() {
+                                Ok(output) => {
+                                    //let output = output.view().map(|v: &_| v.into());
+                                    // FIXME: we can use an arrayview for the inference output instead of owned array base to avoid copying here.
+                                    let output_ndarray = output.view().to_owned();
+                                    frame_inference_output.insert(output_name, output_ndarray);
+                                },
+                                Err(_err) => {
+                                    // Try to convert from i64 since sometimes the models do not return floats
+                                    match output_value.try_extract() {
+                                        Ok(output) => {
+                                            // FIXME: this copies the array twice, first to_owned and then the mapv
+                                            let output_ndarray: ndarray::ArrayBase<ndarray::OwnedRepr<i64>, _> = output.view().to_owned();
+                                            let float_output = output_ndarray.mapv(|v| v as f32);
+                                            frame_inference_output.insert(output_name, float_output);
+                                        }
+                                        Err(_err) => {
+                                            // Try to convert from i64 since sometimes the models do not return floats
+                                            match output_value.try_extract() {
+                                                Ok(output) => {
+                                                    // FIXME: this copies the array twice, first to_owned and then the mapv
+                                                    let output_ndarray: ndarray::ArrayBase<ndarray::OwnedRepr<i32>, _> = output.view().to_owned();
+                                                    let float_output = output_ndarray.mapv(|v| v as f32);
+                                                    frame_inference_output.insert(output_name, float_output);
+                                                }
+                                                Err(err) => warn!("Error extracting inference results: {}", err.to_string()),
+                                            }
+                                        },
+                                    }
+                                }
+                            }
+                        }
+                        frame.set_inference_output(pipeless::data::InferenceOutput::OnnxInferenceOutput(frame_inference_output));
                     },
                     Err(err) => error!("There was an error running inference: {}", err)
                 }