add export to FP16

add inference with possibility to have different

inference/inference.py

@@ -0,0 +1,140 @@
+import cv2
+import numpy as np
+
+from onnx_engine import InferenceEngine
+from dto import AnnotationClass, Annotation, Detection
+
+
+class Inference:
+    def __init__(self, engine: InferenceEngine, confidence_threshold, iou_threshold):
+        self.engine = engine
+        self.confidence_threshold = confidence_threshold
+        self.iou_threshold = iou_threshold
+        self.batch_size = engine.get_batch_size()
+
+        self.model_height, self.model_width = engine.get_input_shape()
+        self.classes = AnnotationClass.read_json()
+
+    def draw(self, annotation: Annotation):
+        img = annotation.frame
+        img_height, img_width = img.shape[:2]
+        for d in annotation.detections:
+            x1 = int(img_width * (d.x - d.w / 2))
+            y1 = int(img_height * (d.y - d.h / 2))
+            x2 = int(x1 + img_width * d.w)
+            y2 = int(y1 + img_height * d.h)
+
+            color = self.classes[d.cls].opencv_color
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+            label = f"{self.classes[d.cls].name}: {d.confidence:.2f}"
+            (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+
+            label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
+
+            cv2.rectangle(
+                img, (x1, label_y - label_height), (x1 + label_width, label_y + label_height), color, cv2.FILLED
+            )
+            cv2.putText(img, label, (x1, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
+        cv2.imshow('Video', img)
+
+    def preprocess(self, frames):
+        blobs = [cv2.dnn.blobFromImage(frame,
+                                       scalefactor=1.0 / 255.0,
+                                       size=(self.model_width, self.model_height),
+                                       mean=(0, 0, 0),
+                                       swapRB=True,
+                                       crop=False)
+                 for frame in frames]
+        return np.vstack(blobs)
+
+    def postprocess(self, batch_frames, batch_timestamps, output):
+        anns = []
+        for i in range(len(output[0])):
+            frame = batch_frames[i]
+            timestamp = batch_timestamps[i]
+            detections = []
+            for det in output[0][i]:
+                if det[4] == 0:
+                    break
+                if det[4] < self.confidence_threshold:
+                    continue
+
+                x1 = max(0, det[0] / self.model_width)
+                y1 = max(0, det[1] / self.model_height)
+                x2 = min(1, det[2] / self.model_width)
+                y2 = min(1, det[3] / self.model_height)
+                conf = round(det[4], 2)
+                class_id = int(det[5])
+
+                x = (x1 + x2) / 2
+                y = (y1 + y2) / 2
+                w = x2 - x1
+                h = y2 - y1
+                detections.append(Detection(x, y, w, h, class_id, conf))
+
+            filtered_detections = self.remove_overlapping_detections(detections)
+
+            # if len(filtered_detections) > 0:
+            # _, image = cv2.imencode('.jpg', frame)
+            # image_bytes = image.tobytes()
+            annotation = Annotation(frame, timestamp, filtered_detections)
+            anns.append(annotation)
+        return anns
+
+    def process(self, video):
+        frame_count = 0
+        batch_frames = []
+        batch_timestamps = []
+        v_input = cv2.VideoCapture(video)
+        while v_input.isOpened():
+            ret, frame = v_input.read()
+            if not ret or frame is None:
+                break
+
+            frame_count += 1
+            if frame_count % 4 == 0:
+                batch_frames.append(frame)
+                batch_timestamps.append(int(v_input.get(cv2.CAP_PROP_POS_MSEC)))
+
+            if len(batch_frames) == self.batch_size:
+                input_blob = self.preprocess(batch_frames)
+                outputs = self.engine.run(input_blob)
+                annotations = self.postprocess(batch_frames, batch_timestamps, outputs)
+                for annotation in annotations:
+                    self.draw(annotation)
+                    print(f'video: {annotation.time / 1000:.3f}s')
+                    if cv2.waitKey(1) & 0xFF == ord('q'):
+                        break
+                batch_frames.clear()
+                batch_timestamps.clear()
+
+        if len(batch_frames) > 0:
+            input_blob = self.preprocess(batch_frames)
+            outputs = self.engine.run(input_blob)
+            annotations = self.postprocess(batch_frames, batch_timestamps, outputs)
+            for annotation in annotations:
+                self.draw(annotation)
+                print(f'video: {annotation.time / 1000:.3f}s')
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+
+    def remove_overlapping_detections(self, detections):
+        filtered_output = []
+        filtered_out_indexes = []
+
+        for det1_index in range(len(detections)):
+            if det1_index in filtered_out_indexes:
+                continue
+            det1 = detections[det1_index]
+            res = det1_index
+            for det2_index in range(det1_index + 1, len(detections)):
+                det2 = detections[det2_index]
+                if det1.overlaps(det2, self.iou_threshold):
+                    if det1.confidence > det2.confidence or (det1.confidence == det2.confidence and det1.cls < det2.cls):
+                        filtered_out_indexes.append(det2_index)
+                    else:
+                        filtered_out_indexes.append(res)
+                        res = det2_index
+            filtered_output.append(detections[res])
+            filtered_out_indexes.append(res)
+        return filtered_output