We can export a mermaid diagram to PNG very simply using the official Mermaid CLI Tool.
Installation via npm
npm i -g mermaid.cli
Usage:
mmdc -i ${input_file_location} -o ${output_file_location}
echo 'sequenceDiagram\nAlice ->> Bob: Hey, Bob!\nBob ->> Alice: Hey there, Alice!' > /tmp/alice-bob.mmd
mmdc -i /tmp/alice-bob.mmd -o /tmp/alice-bob.png
open /tmp/alice-bob.png
We can combine using mmdc
to create images from Mermaid diagrams with creating data URLs from the command line (as illustrated here) to include Mermaid diagrams into markdown files where the rendering engine does not support mermaid diagrams but does support data URIs.
data_url="data:image/png;base64,$(base64 < /tmp/alice-bob.png)"
Result of this is the following base 64 string
data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAcIAAADnCAYAAACNDlZ0AAAgAElEQVR4nO3deVyVZd7H8Q+IsggKJiCkiGQ1NVaU0tNoCtVo9YiWlKG5jFpM+JBWmpZPjiWTOlpTNtaTC42aOy6M5pobGDpSNOK4hIaIjaG5oSyH7QDPHwz3SO4JHs+5v+/X67x6nXv9nTwX33Nf13Wf41RVVVWFiIiISTnbugARERFbUhCKi 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
This can be embedded as an image in markdown engines which support image data URIs as follows:
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)
Note: Using this technique will obviously bloat your Markdown file. So, use at your own discretion.
Hi Friedrich,
I have a question about the PNG resolution. I tried to create a PNG for my .mmd which is a little bigger than usual. When I enlarge the resulting PNG, the quality is a little bit too low. Do you have suggestions on how to enhance the quality?
This is my sample .mmd
graph TD
A[Start] --> B[Declare Variables]
B --> C[Select from JSON and populate #tracy_in]
C --> D[Check if delivery company = 'Ensign Delivery']
D -->|if YES| E[Set ffa_id]
D -->|if NO| Z1[ERROR: Unknown ffa check API settings wmsx.nl]
E --> F[Check if ffa_id exists in xcompany]
F -->|if YES| G[Insert data into #stock_check from #tracy_in]
F -->|if NO| Z2[ERROR: API CHECK WITH CONSULTANT. FFA DOES NOT EXIST]
G --> H{Check existence in #stock_check}
H -->|if YES| I[Set rowkey and select data]
H -->|if NO| AA[Drop #stock_check]
I --> J[Check if fustcode exists in xfust]
J -->|if YES| K[Get xfust_id]
J -->|if NO| L[Insert into xfust]
K --> M[Check if lookup exists in xstock]
M -->|if YES| N[Check if pallet_status exists for xstock_id and batch_code]
M -->|if NO| O[Check if sku exists in xstock]
N -->|if YES| P[Update #tracy_in with xstock_id, client_id]
N -->|if NO| Z3[ERROR: no stock available for article]
O -->|if YES| N
O -->|if NO| Q[Exec create_wms_micro_item]
P --> H
Q --> M
AA --> BB[Insert data into #lot_check from #tracy_in]
BB --> CC{Check existence in #lot_check}
CC -->|if YES| DD[Set rowkey and select data]
CC -->|if NO| ZZ[Drop #lot_check]
DD --> EE[Check if item_lotnr is null]
EE -->|if YES| FF[Get lot_id without lot number]
EE -->|if NO| GG[Get lot_id with lot number]
FF --> HH[Check if lot_id exists]
GG --> HH
HH -->|if YES| II[Update #tracy_in with lot_id]
HH -->|if NO| Z4[ERROR: Unexpected container data]
II --> CC
ZZ --> JJ[Insert data into #dest_check from #tracy_in]
JJ --> KK{Check existence in #dest_check}
KK -->|if YES| LL[Set rowkey and select data]
KK -->|if NO| YY[Drop #dest_check]
LL --> MM[Check if recipient_name is null]
MM -->|if YES| Z5[ERROR: No destination]
MM -->|if NO| NN[Check if recipient_name exists in xlocation]
NN -->|if YES| OO[Update #tracy_in with cmr_dest_loc_id]
NN -->|if NO| PP[Insert into xlocation]
OO --> KK
PP --> NN
YY --> QQ[Select from #tracy_in]
QQ --> RR[Insert into api_outbound_request]
RR --> SS[Insert into api_outbound_request_details]
SS --> TT[Set result with #tracy_in]
TT --> UU[Drop #tracy_in and select into _posted_ensingn_outbound]
UU --> VV[End]