Initial release

Author: mikhail-vl

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.github/workflows/docker.yml

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+name: Docker
+
+on:
+  schedule:
+    - cron: "0 1 * * *" # run at 1 AM UTC
+
+  workflow_dispatch:
+
+jobs:
+  docker:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v1
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v1
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Build linux/amd64
+        id: docker_build
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: ./Dockerfile
+          platforms: linux/amd64
+
+      - name: Build linux/arm64
+        id: docker_build_arm64
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: ./Dockerfile
+          platforms: linux/arm64
+
+      - name: Build linux/arm
+        id: docker_build_arm
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: ./Dockerfile
+          platforms: linux/arm
+
+      - name: Push images
+        id: docker_push
+        uses: docker/build-push-action@v2
+        with:
+          platforms: linux/amd64,linux/arm64,linux/arm
+          push: true
+          tags: ghcr.io/redisgrafana/redis-opencv:latest
+
+      - name: Image digest
+        run: echo ${{ steps.docker_build.outputs.digest }}

.gitignore

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+node_modules/
+.DS_Store
+data/

CHANGELOG.md

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+# Change Log
+
+## v1.0.0 (IN PROGRESS)
+
+### Features / Enhancements
+
+- Initial release

Dockerfile

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+FROM redislabs/redisai:latest-cpu-x64-bionic as redisai
+FROM redislabs/redistimeseries:latest as redistimeseries
+FROM redislabs/redisgears:latest
+
+ENV LD_LIBRARY_PATH=/usr/lib/redis/modules
+
+ARG MODULES=/var/opt/redislabs/lib/modules
+ARG RG=${MODULES}/redisgears.so
+ARG REDIS="redis-server --loadmodule ${RG} PythonHomeDir /opt/redislabs/lib/modules/python3"
+
+ARG DEPS="python python3-pip python3-setuptools libglib2.0-0 libsm6 libxrender1 libxext6 libgomp1"
+ARG REQ="numpy \
+    Pillow \
+    opencv-python"
+
+# Set up a build environment
+WORKDIR /data
+RUN set -ex;\
+    deps="$DEPS";\
+    apt-get update;\
+    apt-get install -y --no-install-recommends $deps;
+
+# Copy RedisTimeSeries
+COPY --from=redistimeseries ${LD_LIBRARY_PATH}/*.so ${LD_LIBRARY_PATH}/
+
+# Copy RedisAI
+COPY --from=redisai ${LD_LIBRARY_PATH}/redisai.so ${LD_LIBRARY_PATH}/
+COPY --from=redisai ${LD_LIBRARY_PATH}/backends ${LD_LIBRARY_PATH}/backends
+
+# Start Redis and install Deps
+RUN nohup bash -c "${REDIS}&" && sleep 4 && redis-cli RG.PYEXECUTE "GearsBuilder().run()" REQUIREMENTS $REQ \
+    && redis-cli save
+
+ENTRYPOINT ["redis-server"]
+CMD ["--loadmodule", "/usr/lib/redis/modules/redistimeseries.so", \
+    "--loadmodule", "/usr/lib/redis/modules/redisai.so", \
+    "--loadmodule", "/var/opt/redislabs/lib/modules/redisgears.so", \
+    "PythonHomeDir", "/opt/redislabs/lib/modules/python3"]

LICENSE

@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+   Copyright 2021 Mikhail Volkov
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

ai/init.py

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+import argparse
+import redis
+from urllib.parse import urlparse
+
+if __name__ == '__main__':
+    # Parse arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-d', '--device', help='CPU or GPU',
+                        type=str, default='CPU')
+    parser.add_argument('-u', '--url', help='Redis URL',
+                        type=str, default='redis://127.0.0.1:6379')
+    args = parser.parse_args()
+
+    # Set up Redis connection
+    url = urlparse(args.url)
+    conn = redis.Redis(host=url.hostname, port=url.port)
+    if not conn.ping():
+        raise Exception('Redis unavailable')
+
+    # Load the RedisAI model
+    print('Loading model - ', end='')
+    with open('./tiny-yolo-voc.pb', 'rb') as f:
+        model = f.read()
+        res = conn.execute_command('AI.MODELSET', 'yolo:model', 'TF',
+                                   args.device, 'INPUTS', 'input', 'OUTPUTS', 'output', 'BLOB', model)
+        print(res)
+
+    # Load the PyTorch post processing boxes script
+    print('Loading script - ', end='')
+    with open('./yolo_boxes.py', 'rb') as f:
+        script = f.read()
+        res = conn.execute_command(
+            'AI.SCRIPTSET', 'yolo:script', args.device, 'SOURCE', script)
+        print(res)

ai/tiny-yolo-voc.pb

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+def nms(boxes):
+    # assuming only one image in one batch
+    boxes = boxes.squeeze()
+    nms_thresh = 0.45
+    conf_thresh = 0.2
+    no_of_valid_elems = (boxes[:, 4] > conf_thresh).nonzero().numel()
+    boxes_confs_inv = 1 - boxes[:, 4]
+    _, sort_ids = torch.sort(boxes_confs_inv)
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    for index in range(no_of_valid_elems):
+        i = sort_ids[index]
+        new_ind = index + 1
+        if float(boxes[i, 4]) > conf_thresh:
+            xx1 = torch.max(x1[i], x1[sort_ids[new_ind:]])
+            yy1 = torch.max(y1[i], y1[sort_ids[new_ind:]])
+            xx2 = torch.min(x2[i], x2[sort_ids[new_ind:]])
+            yy2 = torch.min(y2[i], y2[sort_ids[new_ind:]])
+            w = torch.max(torch.zeros(1, device=boxes.device), xx2 - xx1 + 1)
+            h = torch.max(torch.zeros(1, device=boxes.device), yy2 - yy1 + 1)
+            overlap = (w * h) / area[sort_ids[new_ind:]]
+            higher_nms_ind = (overlap > nms_thresh).nonzero()
+            boxes[sort_ids[new_ind:][higher_nms_ind]
+                  ] = torch.zeros(7, device=boxes.device)
+    return boxes.unsqueeze(0)
+
+
+def get_region_boxes(output):
+    conf_thresh = 0.2
+    num_classes = 20
+    num_anchors = 5
+    anchor_step = 2
+    anchors_ = [1.08, 1.19,  3.42, 4.41,  6.63,
+                11.38,  9.42, 5.11,  16.62, 10.52]
+
+    anchors = torch.empty(num_anchors * 2, device=output.device)
+    for i in range(num_anchors * 2):
+        anchors[i] = anchors_[i]
+
+    batch = output.size(0)
+    # assert(output.size(1) == (5+num_classes)*num_anchors)
+    h = output.size(2)
+    w = output.size(3)
+
+    output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,
+                                                                          1).contiguous().view(5+num_classes, batch*num_anchors*h*w)
+
+    grid_x = torch.linspace(0, w-1, w, device=output.device).repeat(h,
+                                                                    1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
+    grid_y = torch.linspace(0, h-1, h, device=output.device).repeat(w,
+                                                                    1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
+    xs = torch.sigmoid(output[0]) + grid_x
+    ys = torch.sigmoid(output[1]) + grid_y
+
+    anchor_w = anchors.view(num_anchors, anchor_step).index_select(
+        1, torch.zeros(1, device=output.device).long())
+    anchor_h = anchors.view(num_anchors, anchor_step).index_select(
+        1, torch.ones(1, device=output.device).long())
+    anchor_w = anchor_w.repeat(batch, 1).repeat(
+        1, 1, h*w).view(batch*num_anchors*h*w)
+    anchor_h = anchor_h.repeat(batch, 1).repeat(
+        1, 1, h*w).view(batch*num_anchors*h*w)
+    ws = torch.exp(output[2]) * anchor_w.float()
+    hs = torch.exp(output[3]) * anchor_h.float()
+
+    det_confs = torch.sigmoid(output[4])
+
+    cls_confs = torch.softmax(output[5: 5+num_classes].transpose(0, 1), dim=1)
+    cls_max_confs, cls_max_ids = torch.max(cls_confs, 1)
+    cls_max_confs = cls_max_confs.view(-1)
+    cls_max_ids = cls_max_ids.view(-1)
+
+    sz_hw = h * w
+    boxes = torch.zeros(batch, h * w * num_anchors, 7)
+    # assuming only one image in a batch
+    x1 = xs / w
+    y1 = ys / h
+    x2 = ws / w
+    y2 = hs / h
+    higher_confs = ((det_confs * cls_max_confs) > conf_thresh).nonzero()
+    no_selected_elems = higher_confs.numel()
+    if no_selected_elems > 0:
+        boxes[:, 0:no_selected_elems] = torch.stack(
+            [x1, y1, x2, y2, det_confs, cls_max_confs, cls_max_ids.float()], dim=1)[higher_confs.squeeze()]
+    return boxes
+
+
+def boxes_from_tf(output):
+    boxes = get_region_boxes(output.permute(0, 3, 1, 2).contiguous())
+    boxes = nms(boxes)
+    return boxes

ai/yolo_boxes.py

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+version: "3.4"
+
+services:
+  redis:
+    container_name: redis
+    image: ghcr.io/redisgrafana/redis-opencv:latest
+    ports:
+      - 6379:6379
+
+  grafana:
+    container_name: grafana
+    image: ghcr.io/redisgrafana/redis-app:latest
+    ports:
+      - "3000:3000"
+    environment:
+      - GF_AUTH_ANONYMOUS_ORG_ROLE=Admin
+      - GF_AUTH_ANONYMOUS_ENABLED=true
+      - GF_AUTH_BASIC_ENABLED=false
+      - GF_ENABLE_GZIP=true
+      - GF_USERS_DEFAULT_THEME=light
+    volumes:
+      - ./provisioning:/etc/grafana/provisioning
+      - ./dashboards:/var/lib/grafana/dashboards