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This was necessary due to the increase in the frame rate. The previous method did not correlate to time. All scripts updated.
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Snapshot on Face Detection Example
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#
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# Note: You will need an SD card to run this example.
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#
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# This example demonstrates using face tracking on your OpenMV Cam to take a
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# picture.
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import sensor, image, pyb
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RED_LED_PIN = 1
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BLUE_LED_PIN = 3
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sensor.reset() # Initialize the camera sensor.
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sensor.set_pixformat(sensor.GRAYSCALE)
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sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
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sensor.skip_frames(time = 2000) # Let new settings take affect.
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# Load up a face detection HaarCascade. This is object that your OpenMV Cam
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# can use to detect faces using the find_features() method below. Your OpenMV
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# Cam has fontalface HaarCascade built-in. By default, all the stages of the
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# HaarCascade are loaded. However, You can adjust the number of stages to speed
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# up processing at the expense of accuracy. The frontalface HaarCascade has 25
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# stages.
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face_cascade = image.HaarCascade("frontalface", stages=25)
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while(True):
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pyb.LED(RED_LED_PIN).on()
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print("About to start detecting faces...")
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sensor.skip_frames(time = 2000) # Give the user time to get ready.
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pyb.LED(RED_LED_PIN).off()
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print("Now detecting faces!")
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pyb.LED(BLUE_LED_PIN).on()
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diff = 10 # We'll say we detected a face after 10 frames.
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while(diff):
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img = sensor.snapshot()
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# Threshold can be between 0.0 and 1.0. A higher threshold results in a
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# higher detection rate with more false positives. The scale value
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# controls the matching scale allowing you to detect smaller faces.
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faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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if faces:
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diff -= 1
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for r in faces:
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img.draw_rectangle(r)
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pyb.LED(BLUE_LED_PIN).off()
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print("Face detected! Saving image...")
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sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.
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