Exception Handling #114
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,136 +1,157 @@ | ||
| #!/usr/bin/env python3 | ||
| import json | ||
| import struct | ||
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| import cv2 | ||
| import numpy as np | ||
| import onnx | ||
| import onnxruntime as ort | ||
| import redis | ||
| import rospkg | ||
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| from line_fitting import fit_lanes | ||
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| WIDTH: int = 330 | ||
| HEIGHT: int = 180 | ||
| DEBUG_MODE: bool = True | ||
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| class CVModelInferencer: | ||
| def __init__(self): | ||
| self.redis = redis.Redis(host="127.0.0.1", port=6379, db=3) | ||
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| rospack = rospkg.RosPack() | ||
| model_path = ( | ||
| rospack.get_path("lane_detection") + "/models/unet_with_sigmoid.onnx" | ||
| ) | ||
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| self.model = onnx.load(model_path) | ||
| onnx.checker.check_model(self.model) | ||
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| self.ort_session = ort.InferenceSession( | ||
| model_path, providers=["CUDAExecutionProvider"] | ||
| ) | ||
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| def run(self): | ||
| raw = self._fromRedisImg("zed/preprocessed") | ||
| if raw is None: | ||
| return | ||
| img = get_input(raw) # Does not return None | ||
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| # Do model inference | ||
| outputs = self.ort_session.run(None, {"Inputs": img.astype(np.float32)}) | ||
| mask = np.where(outputs[0][0][0] > 0.5, 1.0, 0.0) | ||
| self._toRedisImg(mask, "cv/model/output") | ||
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| lanes = fit_lanes(mask) | ||
| if lanes is not None: | ||
| self._toRedisLanes(lanes, "lane_detection") | ||
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| if DEBUG_MODE: | ||
| toshow = np.concatenate([cv2.resize(raw, (330, 180)), np.tile(mask[...,np.newaxis]*255, (1, 1, 3))], axis=1).astype(np.uint8) | ||
| cv2.imshow("image", toshow) | ||
| cv2.waitKey(1) | ||
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| def _toRedisLanes(self, lanes, name): | ||
| """Store given Numpy array 'img' in Redis under key 'name'""" | ||
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| encoded = json.dumps(lanes) | ||
| # Store encoded data in Redis | ||
| self.redis.set(name, encoded) | ||
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| def _fromRedisImg(self, name): | ||
| """Retrieve Numpy array from Redis key 'n'""" | ||
| encoded = self.redis.get(name) | ||
| if encoded is None: | ||
| return None | ||
| h, w = struct.unpack(">II", encoded[:8]) | ||
| a = cv2.imdecode(np.frombuffer(encoded, dtype=np.uint8, offset=8), 1).reshape( | ||
| h, w, 3 | ||
| ) | ||
| return a | ||
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| def _toRedisImg(self, img, name): | ||
| """Store given Numpy array 'img' in Redis under key 'name'""" | ||
| h, w = img.shape[:2] | ||
| shape = struct.pack(">II", h, w) | ||
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| retval, buffer = cv2.imencode(".png", img) | ||
| img_bytes = np.array(buffer).tostring() | ||
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| encoded = shape + img_bytes | ||
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| # Store encoded data in Redis | ||
| self.redis.set(name, encoded) | ||
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| return | ||
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| def find_edge_channel(img): | ||
| edges_mask = np.zeros(img.shape[0:2], dtype=np.uint8) | ||
| width = img.shape[1] | ||
| height = img.shape[0] | ||
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| gray_im = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | ||
| # gray_im = cv2.GaussianBlur(gray_im,(3,3),0) | ||
| # Separate into quadrants | ||
| med1 = np.median(gray_im[: height // 2, : width // 2]) | ||
| med2 = np.median(gray_im[: height // 2, width // 2 :]) | ||
| med3 = np.median(gray_im[height // 2 :, width // 2 :]) | ||
| med4 = np.median(gray_im[height // 2 :, : width // 2]) | ||
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| l1 = int(max(0, (1 - 0.205) * med1)) | ||
| u1 = int(min(255, (1 + 0.205) * med1)) | ||
| e1 = cv2.Canny(gray_im[: height // 2, : width // 2], l1, u1) | ||
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| l2 = int(max(0, (1 - 0.205) * med2)) | ||
| u2 = int(min(255, (1 + 0.205) * med2)) | ||
| e2 = cv2.Canny(gray_im[: height // 2, width // 2 :], l2, u2) | ||
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| l3 = int(max(0, (1 - 0.205) * med3)) | ||
| u3 = int(min(255, (1 + 0.205) * med3)) | ||
| e3 = cv2.Canny(gray_im[height // 2 :, width // 2 :], l3, u3) | ||
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| l4 = int(max(0, (1 - 0.205) * med4)) | ||
| u4 = int(min(255, (1 + 0.205) * med4)) | ||
| e4 = cv2.Canny(gray_im[height // 2 :, : width // 2], l4, u4) | ||
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| # Stitch the edges together | ||
| edges_mask[: height // 2, : width // 2] = e1 | ||
| edges_mask[: height // 2, width // 2 :] = e2 | ||
| edges_mask[height // 2 :, width // 2 :] = e3 | ||
| edges_mask[height // 2 :, : width // 2] = e4 | ||
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| edges_mask_inv = cv2.bitwise_not(edges_mask) | ||
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| return edges_mask, edges_mask_inv | ||
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Contributor
Author
There was a problem hiding this comment. I deleted this because it makes the model do work on an unnecessarily large image |
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| def get_input(frame: np.ndarray) -> np.ndarray: | ||
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Contributor
Author
There was a problem hiding this comment. Added typing. Okay in Python 3 only |
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| frame_copy = np.copy(frame) | ||
| frame_copy = cv2.resize(frame_copy, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA) | ||
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| test_edges, test_edges_inv = find_edge_channel(frame_copy) | ||
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Contributor
Author
There was a problem hiding this comment. REAL CHANGE I combined the np.append's into a single np.block. I also moved the resize to the top (I'm fairly sure the function that is called on frame_copy is shape agnostic) |
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| frame_copy = np.block( | ||
| [ | ||
| frame_copy, # shape: (180, 330, 3) | ||
| test_edges.reshape(*test_edges.shape, 1), # shape: (180, 330, 1) | ||
| test_edges_inv.reshape(*test_edges_inv.shape, 1), # shape: (180, 330, 1) | ||
| ] | ||
| ) # (330, 180, 5) | ||
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| input = (frame_copy / 255.0).transpose(2, 0, 1).reshape(1, 5, *test_edges.shape) | ||
| return input | ||
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| if __name__ == "__main__": | ||
| wrapper = CVModelInferencer() | ||
| if DEBUG_MODE: | ||
| while True: | ||
| wrapper.run() | ||
| else: | ||
| while True: | ||
| try: | ||
| wrapper.run() | ||
| except: | ||
| pass | ||
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First "real" change (moved logic to if-guard)