@@ -58,7 +58,7 @@ def get_col_lux(string):
Still returns colour if that has been found.
"""
return col, None
- return int( col ), int( lux )
+ return int(col), int(lux)
"""
Camera object that is the backbone of the tuning tool.
@@ -96,18 +96,18 @@ class Camera:
},
"rpi.awb": {
"priors" : [
- {"lux": 0, "prior": [ 2000, 1.0, 3000, 0.0, 13000, 0.0]},
- {"lux": 800, "prior": [ 2000, 0.0, 6000, 2.0, 13000, 2.0]},
- {"lux": 1500, "prior": [ 2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]}
+ {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]},
+ {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]},
+ {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]}
],
"modes" : {
- "auto" : { "lo" : 2500, "hi" : 8000 },
- "incandescent" : { "lo" : 2500, "hi" : 3000 },
- "tungsten" : { "lo" : 3000, "hi" : 3500 },
- "fluorescent" : { "lo" : 4000, "hi" : 4700 },
- "indoor" : { "lo" : 3000, "hi" : 5000 },
- "daylight" : { "lo" : 5500, "hi" : 6500 },
- "cloudy" : { "lo" : 7000, "hi" : 8600 }
+ "auto" : {"lo" : 2500, "hi" : 8000},
+ "incandescent" : {"lo" : 2500, "hi" : 3000},
+ "tungsten" : {"lo" : 3000, "hi" : 3500},
+ "fluorescent" : {"lo" : 4000, "hi" : 4700},
+ "indoor" : {"lo" : 3000, "hi" : 5000},
+ "daylight" : {"lo" : 5500, "hi" : 6500},
+ "cloudy" : {"lo" : 7000, "hi" : 8600}
},
"bayes" : 1
},
@@ -129,8 +129,8 @@ class Camera:
"gain" : [1.0, 2.0, 4.0, 6.0, 6.0]
},
"sport": {
- "shutter": [ 100, 5000, 10000, 20000, 120000 ],
- "gain": [ 1.0, 2.0, 4.0, 6.0, 6.0 ]
+ "shutter": [100, 5000, 10000, 20000, 120000],
+ "gain": [1.0, 2.0, 4.0, 6.0, 6.0 ]
}
},
"constraint_modes" : {
@@ -138,8 +138,8 @@ class Camera:
{"bound" : "LOWER", "q_lo" : 0.98, "q_hi" : 1.0, "y_target" : [0, 0.5, 1000, 0.5]}
],
"highlight": [
- { "bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [ 0, 0.5, 1000, 0.5 ] },
- { "bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [ 0, 0.8, 1000, 0.8 ] }
+ {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]},
+ {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]}
]
},
"y_target" : [0, 0.16, 1000, 0.165, 10000, 0.17]
@@ -168,12 +168,12 @@ def do_ccm(r, g, b, m_srgb):
rb_gbs = (rb*gb)
gb_2s = (gb*gb)
- r_rbs = ( rb * (m_srgb[..., 0] - b) )
- r_gbs = ( gb * (m_srgb[..., 0] - b) )
- g_rbs = ( rb * (m_srgb[..., 1] - b) )
- g_gbs = ( gb * (m_srgb[..., 1] - b) )
- b_rbs = ( rb * (m_srgb[..., 2] - b) )
- b_gbs = ( gb * (m_srgb[..., 2] - b) )
+ r_rbs = rb * (m_srgb[..., 0] - b)
+ r_gbs = gb * (m_srgb[..., 0] - b)
+ g_rbs = rb * (m_srgb[..., 1] - b)
+ g_gbs = gb * (m_srgb[..., 1] - b)
+ b_rbs = rb * (m_srgb[..., 2] - b)
+ b_gbs = gb * (m_srgb[..., 2] - b)
"""
Obtain least squares fit
@@ -20,7 +20,7 @@ def geq_fit(Cam, plot):
Fits geq model by looking at difference
between greens in macbeth patches
"""
- geqs = np.array([ geq(Cam, Img)*Img.againQ8_norm for Img in imgs ])
+ geqs = np.array([geq(Cam, Img)*Img.againQ8_norm for Img in imgs])
Cam.log += '\nProcessed all images'
geqs = geqs.reshape((-1, 2))
"""
@@ -25,7 +25,7 @@ def get_square_verts(c_err = 0.05, scale = scale):
c3 = (x_max, y_max)
c4 = (x_max, 0)
mac_norm = np.array((c1, c2, c3, c4), np.float32)
- mac_norm = np.array([ mac_norm ])
+ mac_norm = np.array([mac_norm])
square_verts = []
square_0 = np.array(((0, 0), (0, side),
E201 whitespace after '(' E201 whitespace after '{' E201 whitespace after '[' E202 whitespace before '}' E202 whitespace before ']' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> --- utils/raspberrypi/ctt/ctt.py | 30 ++++++++++++++--------------- utils/raspberrypi/ctt/ctt_ccm.py | 12 ++++++------ utils/raspberrypi/ctt/ctt_geq.py | 2 +- utils/raspberrypi/ctt/ctt_ransac.py | 2 +- 4 files changed, 23 insertions(+), 23 deletions(-)